Article

Toolkit for Inclusion of User Experience Design Guidelines in
the Development of Assistants Based on Generative
Artificial Intelligence
Carlos Alberto Peláez 1 , Andrés Solano 1 , Johann A. Ospina 1 , Juan C. Espinosa 1 , Ana S. Montaño 1 ,
Paola A. Castillo 1 , Juan Sebastián Duque 2 , David A. Castro 2 , Juan M. Nuñez Velasco 3, *
and Fernando De la Prieta 3

                                             1   Grupo de Investigación GITI, Universidad Autónoma de Occidente, Cali 760030, Colombia;
                                                 capa@uao.edu.co (C.A.P.); afsolano@uao.edu.co (A.S.); jaospina@uao.edu.co (J.A.O.);
                                                 juan_cam.espinosa@uao.edu.co (J.C.E.); ana_sofia.montano@uao.edu.co (A.S.M.);
                                                 pacastillo@uao.edu.co (P.A.C.)
                                             2   Corporación Talentum, Cali 760042, Colombia; juan.duque@talentum.edu.co (J.S.D.);
                                                 david.castro@talentum.edu.co (D.A.C.)
                                             3   Grupo de Investigación BISITE, Universidad de Salamanca, 37008 Salamanca, Spain; fer@usal.es
                                             *   Correspondence: jmnunez@usal.es


                                             Abstract: This study addresses the need to integrate ethical, human-centered principles into
                                             user experience (UX) design for generative AI (GenAI)-based assistants. Acknowledging
                                             the ethical and societal challenges posed by the democratization of GenAI, this study
                                             developed a set of six UX design guidelines and 37 recommendations to guide development
                                             teams in creating GenAI assistants. A card-based toolkit was designed to encapsulate
                                             these guidelines, applying color theory and Gestalt principles to enhance usability and
                                             understanding. The design science research methodology (DSRM) was followed, and the
                                             toolkit was validated through a hands-on workshop with software and UX professionals,
                                             assessing usability, user experience, and utility. The quantitative results indicated the
Academic Editors: Da Tao,
Tingru Zhang and Hailiang Wang
                                             high internal consistency and effectiveness of the toolkit, while the qualitative analysis
                                             highlighted its capacity to foster collaboration and address GenAI-specific challenges. This
Received: 19 November 2024
Revised: 29 December 2024
                                             study concludes that the toolkit improves usability and utility in UX design for GenAI-
Accepted: 7 January 2025                     based assistants, though it identifies areas for future enhancement and the need for further
Published: 24 January 2025                   validation across varied contexts.
Citation: Peláez, C.A.; Solano, A.;
Ospina, J.A.; Espinosa, J.C.; Montaño,       Keywords: user experience; generative artificial intelligence; human-centered artificial
A.S.; Castillo, P.A.; Duque, J.S.; Castro,   intelligence
D.A.; Nuñez Velasco, J.M.; De la
Prieta, F. Toolkit for Inclusion of User
Experience Design Guidelines in the
Development of Assistants Based on
                                             1. Introduction
Generative Artificial Intelligence.
Informatics 2025, 12, 10.                         A new era has begun for the discipline of artificial intelligence (AI), driven by its
https://doi.org/10.3390/                     democratization. Sectors such as healthcare [1] and education [2] serve as examples of
informatics12010010                          non-IT niches that are beginning to experience profound transformations because of the
Copyright: © 2025 by the authors.            use and appropriation of AI. The evolution of generative artificial intelligence (GenAI) has
Licensee MDPI, Basel, Switzerland.           proven to be the key enabler of democratization [3]. Neural networks based on transformer
This article is an open access article       architectures have undoubtedly expanded the range of possibilities that these technologies
distributed under the terms and
                                             offer [4].
conditions of the Creative Commons
                                                  Society must inevitably confront new challenges and risks; like a child who grows up
Attribution (CC BY) license
(https://creativecommons.org/                and leaves home, AI has embarked on a journey beyond its origins in computer science.
licenses/by/4.0/).                           Responsibility for the use, appropriation, and potential future evolution of AI is now


Informatics 2025, 12, 10                                                                                    https://doi.org/10.3390/informatics12010010
Informatics 2025, 12, 10                                                                                         2 of 25



                           rapidly extending to other knowledge areas and disciplines. These fields are tasked with
                           the challenge of educating society and building a culture capable of making informed,
                           ethical, and moral decisions regarding the use of this technology. In this context, human-
                           centered artificial intelligence (HCAI) is poised to play a key role [5].
                                Previous experiences have provided us with prior knowledge that enables us to
                           better face this new challenge. For example, we are aware of the role played by the
                           Human–Computer Interaction (HCI) discipline in bridging the gap between non-experts
                           and technology. HCI has facilitated an interaction with technology during the era of
                           computational democratization, which is marked by the emergence of personal computers
                           and the internet [5].
                                The transition from HCI to HCAI can be understood as a process of conceptual
                           expansion and disciplinary scope. While HCI emerged to ensure that digital technologies
                           were accessible, comprehensible, and useful to people, HCAI takes these fundamental
                           principles further by incorporating the unique capabilities and characteristics of AI [6].
                           Thus, HCAI inherits from HCI its focus on usability, user satisfaction, and the reduction
                           in cognitive and emotional barriers in the interaction while introducing new challenges,
                           such as the need to design for the transparency of complex models, ethical responsibility in
                           automated decision-making, and fostering trust in autonomous systems [7].
                                In essence, HCAI not only seeks to make interfaces understandable to users but also
                           to ensure that the algorithms and underlying logics of AI align with human values, moral
                           principles, and sociocultural contexts [8]. In this way, HCAI expands the scope of HCI,
                           taking on not only the design of surface-level user experiences but also the responsible
                           configuration of the technological core that underpins them.
                                The above presents a new scenario with emerging challenges in light of the rise in
                           solutions based on GenAI-mediated assistants that aims to mitigate the implicit risks posed
                           by the widespread use of this technology [9]. The current literature largely focuses on
                           studies that highlight the potential benefits offered by GenAI-based solutions for user
                           experience (UX) design in interactive systems [10,11]. However, there has been little
                           discussion regarding what UX design should consider in the development of GenAI-based
                           assistant solutions [12]. This research specifically aims to contribute to this discussion
                           by providing a toolkit that compiles a set of guidelines and recommendations for the UX
                           design of GenAI-based assistants. These guidelines are aimed at designers and developers
                           to increase the perception of trust, usefulness, and UX in human–AI interactions. To
                           this end, this paper first reviews the related works, noting their purpose, contributions,
                           and limitations. Then, it provides a description of the methodology used, detailing its
                           implementation throughout the course of this study. The last sections present the results
                           obtained from the experiments conducted with the stakeholders, as well as a final discussion
                           of the findings.

                           2. Background
                                HCAI is a field that strongly emphasizes human experiences, satisfaction, and needs.
                           Its goal is to improve, augment, and optimize human performance while ensuring that
                           AI systems are dependable, secure, and trustworthy. This approach supports human self-
                           efficacy, encourages creativity, defines responsibilities more clearly, and fosters greater
                           social engagement [13]. These aspects are considered to be empirically significant relative
                           to their influence on the UX of GenAI-based systems. However, some researchers have
                           underscored the need for a further investigation into the UX of AI-assisted chatbots [14].
                           Concerns have also been raised by various authors about GenAI-based tools like ChatGPT,
                           particularly regarding their tendency to generate inaccurate or fabricated information and
Informatics 2025, 12, 10                                                                                            3 of 25



                           their ability to circumvent systems designed to detect duplicate content, which is a critical
                           issue in domains where originality is paramount [15].
                                A comprehensive review of 223 documents sourced from various electronic databases
                           revealed the key factors influencing UX design for the development of GenAI-based
                           systems. This state-of-the-art analysis highlighted the substantial impact of the principles
                           of the HCAI discipline [12].
                                Multiple studies have highlighted how GenAI can reshape a variety of domains, such
                           as healthcare, education, finance, tourism, and cultural heritage, by enhancing personal-
                           ization and improving overall UX [16–19]. Central to this evolution is the shift from HCI
                           principles to HCAI approaches [20], emphasizing ethics, inclusivity, fairness, and cultural
                           sensitivity in GenAI-driven solutions [21].
                                Human feedback is a crucial factor in maintaining the quality and relevance of GenAI
                           outputs [16,22] and ensuring trust, reliability, transparency, and explainability in interactive
                           systems [23]. Researchers underscore that effective GenAI solutions must align with user
                           needs, expectations, and values while incorporating the psychological, social, and cognitive
                           dimensions of interactions [24].
                                Studies on GenAI’s multimodal capabilities have indicated progress in enhancing
                           UX, especially when bridging the gap between algorithmic complexity and user-friendly
                           interfaces [25]. This includes enabling more natural communication through conversational
                           systems, improving personalization, and ensuring that designs remain accessible to diverse
                           user groups [26].
                                The collective insights from the reviewed works underscore the need to incorporate
                           a human-centered, ethically grounded UX design that integrates transparency, fairness,
                           accountability, and explainability into GenAI solutions [27–30]. This approach entails
                           inclusive design, user education, and continuous feedback loops, ensuring that GenAI
                           solutions are not only technologically advanced but also respectful of human values, social
                           contexts, and long-term well-being [31,32]. Studies have suggested that user trust in
                           GenAI-based technologies is largely dependent on how these ethical aspects are addressed,
                           particularly in sensitive sectors such as healthcare and education.
                                Likewise, inclusivity and accessibility are key priorities. Studies have advocated
                           for designing GenAI solutions that are accessible to a wide range of users, regardless of
                           their abilities or socioeconomic context [21,33]. This trend represents an effort to democra-
                           tize access to advanced technologies and ensure that the benefits of GenAI are available
                           to everyone.
                                A final discussion, resulting from the review of these studies, highlights the concerns
                           raised in the literature regarding how to address sensitive factors, such as ethics, trans-
                           parency, inclusivity, accessibility, and fairness, among others, in GenAI-mediated solutions.
                           However, while it is common for these studies to emphasize HCAI as an essential disci-
                           pline for providing such a foundation, no studies explicitly offer precise contributions to
                           incorporate these considerations, for instance, at the design process level for such solutions.
                                Based on the above elements, this study makes a contribution through a toolkit
                           that defines a set of UX design guidelines for GenAI-based assistants. This toolkit
                           serves as a resource for generating spaces for analysis, discussion, and conceptualiza-
                           tion among members of a multidisciplinary team during the design process of GenAI-based
                           assistant solutions.

                           3. Materials and Methods
                           3.1. Methodology
                               The initial challenge posed by the need to address the scope and implications of
                           UX design in the development of GenAI-based assistants led to the creation of an arti-
                           3.1. Methodology
                                   The initial challenge posed by the need to address the scope and implications of UX
Informatics 2025, 12, 10    design in the development of GenAI-based assistants led to the creation of an artifact                                        as a
                                                                                                                                                       4 of 25
                            proposed solution. In this case, the solution comprises a set of guidelines and recommen-
                            dations for the UX design of GenAI-based assistants. Additionally, a toolkit was designed
                            to facilitate
                           fact              the application
                                  as a proposed        solution. of    In these   guidelines
                                                                           this case,               and recommendations
                                                                                          the solution       comprises a set ofand           improveand
                                                                                                                                         guidelines        the
                            experience of teams
                           recommendations             fordeveloping
                                                            the UX design   a GenAI       assistant. In addressing
                                                                                   of GenAI-based             assistants. the      challenge of
                                                                                                                              Additionally,          artifact
                                                                                                                                                  a toolkit
                            development,
                           was    designed to   thefacilitate
                                                      design science         researchof
                                                                   the application         methodology
                                                                                             these guidelines   (DSRM) and[34]     oﬀered the bestand
                                                                                                                              recommendations             ap-
                            proach. the experience of teams developing a GenAI assistant. In addressing the challenge
                           improve
                                   Following
                           of artifact            the processing
                                         development,           the designguided
                                                                               scienceby the    DSRM,
                                                                                           research          an adaptation
                                                                                                        methodology               was [34]
                                                                                                                             (DSRM)      made    basedthe
                                                                                                                                               offered      on
                            the framework
                           best   approach. for UX design and the assessments of Adikari et al. [35]. To address the
                            challenges
                                  Following facedthebyprocessing
                                                         teams developing guided by   software
                                                                                          the DSRM, solutions,    we initiated
                                                                                                            an adaptation        was a research
                                                                                                                                        made based  processon
                            focused
                           the          on reviewing
                                 framework        for UX existing,
                                                              design and   validated       experiences
                                                                               the assessments                and guidelines
                                                                                                         of Adikari                 from
                                                                                                                          et al. [35].   Tothe   literature
                                                                                                                                              address     the
                            on UX design,
                           challenges      facedparticularly          in the context
                                                    by teams developing                     of designing
                                                                                     software      solutions,GenAI-based
                                                                                                                  we initiated aassistant
                                                                                                                                       researchsolutions
                                                                                                                                                   process
                            [12].
                           focused     on reviewing existing, validated experiences and guidelines from the literature on
                                   The design
                           UX design,               processinfor
                                            particularly           thethe   UX guidelines
                                                                        context    of designing   involved      a team of
                                                                                                       GenAI-based             expert solutions
                                                                                                                           assistant     UX researchers
                                                                                                                                                      [12].
                            and The     design process
                                   experienced       industry    fordesigners.
                                                                     the UX guidelines
                                                                                   This process  involved       a team the
                                                                                                        emphasized         of expert
                                                                                                                                need to  UXdevelop
                                                                                                                                              researchersa set
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                                                                   integratingThis  UXprocess
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                                                                                                                                          when creating
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                                                                                                    of the methodology           appliedcreating    GenAI-
                                                                                                                                              consisted     of
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                            two phases:       ansolutions.
                                                   expert panel   The first
                                                                        phasepart   of the methodology
                                                                                 followed      by a descriptive  applied andconsisted
                                                                                                                               inferential of two   phases:
                                                                                                                                               analysis     of
                           an
                            theexpert    panel survey.
                                  conducted       phase followed           by a descriptive
                                                               Each guideline                       and inferential
                                                                                       was characterized           usinganalysis      of the conducted
                                                                                                                             the Mann–Whitney             test
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                            [36] to evaluate       potential  was     characterized
                                                                   significant             using the
                                                                                  diﬀerences              Mann–Whitney
                                                                                                     between                      test [36] toregarding
                                                                                                                  experts’ perceptions           evaluate
                           potential
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                                            clarity fordifferences
                                                           each UX design between       experts’ perceptions regarding utility and clarity
                                                                                   guideline.
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                                                                                  for developing          which     was A
                                                                                                               toolkit.    reviewed
                                                                                                                              group ofand       discussed
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                                                                              most eﬀective                    the toolkit. Aand
                                                                                                     colors, illustrations,        group    of designers
                                                                                                                                        formats     for pre-
                           participated       in discussions
                            senting the guidelines                   aboutrecommendations,
                                                             and their        the most effectiveboth      colors,   illustrations,
                                                                                                                digitally     and on and      formats
                                                                                                                                        printed    cards. for
                           presenting      the guidelines
                                   The definition       of theand       their recommendations,
                                                                   elements     that comprise the both       cardsdigitally     and onisprinted
                                                                                                                     in the toolkit         based on  cards.
                                                                                                                                                           the
                            IDEO  The   definition
                                     method      card of setthe[37]elements
                                                                      and thethat      comprise theDesign
                                                                                  Value-Sensitive           cards in     the toolkit
                                                                                                                      Toolkit            is based
                                                                                                                                 [38]. Both          on the
                                                                                                                                                references
                           IDEO
                            providedmethod      card set for
                                          a foundation        [37]structuring
                                                                     and the Value-Sensitive
                                                                                    the information       Design     Toolkit [38].
                                                                                                            in an accessible,           Both references
                                                                                                                                    practical,   and easy-
                           provided
                            to-implement a foundation        for structuring
                                                way. Additionally,            the the    information
                                                                                    specific    context in  of an  accessible,
                                                                                                                GenAI      requires practical,   and easy-
                                                                                                                                        consideration       of
                           to-implement         way.    Additionally,        the  specific     context      of GenAI
                            the unique challenges in this field, such as the need to explain complex AI concepts to       requires     consideration        of
                           the   unique challenges
                            non-technical       users and  in this    field, such as
                                                                the importance         ofthe   need to expectations
                                                                                           managing        explain complex          AI concepts
                                                                                                                              regarding     system   tocapa-
                                                                                                                                                         non-
                           technical
                            bilities.   users   and    the  importance        of  managing       expectations       regarding      system     capabilities.
                                  The
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                                                                                                                                              design team
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                            at a software development company. The goal was to assess the toolkit’s contribution in
                               a software      development          company.       The     goal  was     to  assess   the   toolkit’s    contribution       in
                           terms
                            terms of of user
                                        user experience,
                                                experience, usability,
                                                                   usability, andand utility
                                                                                         utility inin identifying
                                                                                                       identifying factors
                                                                                                                        factors associated
                                                                                                                                   associated withwith UX UX
                           design
                            design forfor GenAI-based
                                           GenAI-basedassistants.assistants. TheThe process
                                                                                       processisisoutlined
                                                                                                      outlinedin   inFigure
                                                                                                                       Figure1.1.




                           Figure 1. The DSRM process guided the development of the UX design guidelines toolkit for
                           GenAI-based assistants. Adapted from [35].

                           3.2. UX Design Guidelines and Recommendations for GenAI-Based Assistants
                                The general findings identified in the literature led to the recognition of several
                           elements where emphasis was placed on various factors with a potential influence on how
                           people perceive a good UX as a result of using and adopting a GenAI-based assistant. These
                           factors encompass ethical and responsible design considerations, inclusivity for appropriate
Informatics 2025, 12, 10                                                                                                  5 of 25



                                  AI usage, fostering effective human–AI collaboration, personalization of the human–AI
                                  experience, trust, and reliability in the human–AI relationship, and addressing issues of
                                  inaccuracy and variability.
                                        A key factor in the discussion of these elements is the relationship between innovation
                                  and necessity. Undoubtedly, topics such as the discussion of ethical and moral factors in the
                                  development of interactive systems [39], as well as accessibility as an essential element for
                                  inclusive solutions [40], are widely addressed in the literature of disciplines such as HCI.
                                  These critical considerations of morality, ethics, and inclusion should not be overlooked;
                                  on the contrary, they highlight the urgent need for reassessment considering the unique
                                  characteristics of GenAI technologies and the challenges they pose. Consistent with the
                                  present analysis, this study identified six UX design guidelines that should be considered
                                  in the development of a GenAI-based assistant.
                                        A set of 37 recommendations was identified to be associated with the UX design
                                  guidelines for the development of GenAI-based assistants. These recommendations allow
                                  for a more detailed and precise interpretation of the scope of each UX design guideline in
                                  the indicated context. In addition, they provide a reference for the foundation of an analysis
                                  and discussion process among the team members responsible for designing a GenAI-based
                                  assistant solution according to the context and stakeholder needs.
                                        Table 1 presents the six UX design guidelines for the development of GenAI-based
                                  assistants, each accompanied by a brief description. A specific nomenclature is used to
                                  facilitate the inclusion of those guidelines in both the description of recommendations and
                                  the toolkit that is introduced further in this paper.

                                  Table 1. UX design guidelines for the development of GenAI-based assistants.

     Code                    UX Guideline                                             Description
                                                         This guideline should be considered a cross-cutting factor in the
                                                         development of any solution based on GenAI, regardless of its
                                                         application context [41]. Incorporating a GenAI-based solution or
                                                         evolving an existing one should prompt us to question how that
                                                         solution or its evolution will contribute to creating value for the
                                                         UX [42].
                       Design Based on Ethical and
       G1                                                A design grounded in ethical and responsible principles contributes
                         Responsible Principles
                                                         to creating value in UX by enhancing the user’s perception of
                                                         transparency in GenAI-based assistants. It enables mechanisms
                                                         that clearly communicate the solution’s capabilities, limitations,
                                                         and potential risks, prevents misunderstandings, and includes bias
                                                         mitigation as a key element that requires traceability and control
                                                         throughout the human–AI interaction process [43].
                                                         A design based on the principles of inclusion ensures that
                                                         GenAI-based assistants can be used and understood by a diverse
                                                         and representative audience. This is achieved by creating solutions
                                                         adaptable to various accessibility needs, addressing several
                                                         disabilities within the population. Furthermore, it emphasizes the
                                                         importance of considering cultural and linguistic variations to
                      Design Based on Principles of      ensure their utility for a global and diverse audience and mitigating
       G2
                               Inclusion                 potential biases and prejudices [44].
                                                         This approach promotes the reduction in barriers to the adoption of
                                                         GenAI in disadvantaged contexts, particularly in emerging
                                                         economies, recognizing the need to advance toward models whose
                                                         development is not constrained by data derived predominantly
                                                         from customs, cultures, and contexts belonging to industrialized
                                                         countries [21].
Informatics 2025, 12, 10                                                                                                     6 of 25



                                  Table 1. Cont.

     Code                    UX Guideline                                            Description
                                                       This guideline responds to the significant variety of human tasks
                                                       that can be supported by GenAI-based solutions at various stages,
                                                       tailored to specific needs throughout their execution [45].
                   Design that Promotes AI–Human       Grounded in the principle of HCAI, this guideline adheres to the
       G3
                            Collaboration              premise that AI solutions should exist to augment capabilities, not
                                                       replace them. In this context, various types of human activities can
                                                       be greatly enhanced through the mediation of GenAI-based
                                                       solutions in their processes.
                                                       GenAI-based solutions offer the potential to usher in a new era of
                                                       information technologies, with the capacity to scale toward a more
                                                       natural and personalized human–computer experience. A key
                       Design Oriented Toward          factor enabling this evolution is the anthropomorphism resulting
       G4          Personalization of the AI–Human     from human–GenAI interaction [46]. It is crucial, however, to
                              Experience               ensure that users remain aware that they are interacting with
                                                       technology, not another human (a conscious being). This guideline
                                                       highlights the challenge of value tension [38], which must be
                                                       considered in UX design for such solutions.
                                                       In the context of emerging technologies, such as GenAI-based
                                                       solutions, trust and reliability are critical factors for their adoption
                                                       and evolution. Trust arises when a system delivers useful results, a
                                                       high quality, and is verifiable [29]. It is essential for users to identify
                                                       issues such as hallucinations, biases, and errors and evaluate
                                                       whether these problems are manageable or acceptable. This
                                                       capability allows users to adjust their expectations, maintain
                                                       control, and ensure informed use of the system.
                                                       In contrast, reliability refers to the system’s consistency and
                                                       predictability under different conditions. A reliable system not only
                        Design Based on Trust and
                                                       reduces errors but also strengthens user perception by
       G5              Reliability in GenAI–Human
                                                       demonstrating safe and consistent performance. Both
                                 Interaction
                                                       concepts—trust and reliability—are essential pillars for ensuring
                                                       effective and sustainable interaction in GenAI-based solutions.
                                                       In this regard, the adoption of GenAI follows a cycle similar to
                                                       Gartner’s Hype Cycle for emerging technologies. Currently, many
                                                       GenAI solutions are at a peak of inflated expectations [47], where
                                                       the initial enthusiasm of users contrasts with their difficulties in
                                                       recognizing the limitations and potential risks of these technologies.
                                                       However, as these solutions progress toward maturity, trust needs
                                                       to be established in human–AI interactions to determine their
                                                       long-term success and adoption.
                                                       The results generated by GenAI-based solutions are far from
                                                       infallible. This may significantly deviate from the originally
                                                       estimated or expected results. On the one hand, responses may be
                      Design with Consideration of     subject to inaccuracies due to the inherent limitations of the
       G6
                       Inaccuracy and Variability      algorithms, data, and architectures underlying these solutions. On
                                                       the other hand, users should be aware that GenAI systems can
                                                       provide responses that vary in quality and form, even if the input
                                                       provided by the user remains unchanged [45].


                                       Associated with each of the previously discussed UX design guidelines, a set of
                                  recommendations is provided to guide the UX design team during the analysis and creation
                                  phases of the GenAI-based assistant. These recommendations are intended to streamline
                                  the incorporation of the guidelines into the design process and to ensure their effective
Informatics 2025, 12, 10                                                                                                                         7 of 25



                                 implementation in the final specification of the solution. Table 2 provides a detailed
                                 overview of the recommendation associated with each guideline.

                                 Table 2. UX design guidelines and their associated recommendations.

        UX Guideline                                                    Associated Recommendations
                           G1R1. Communicate the System’s Scope and Limitations: Ensure that stakeholders are aware of the system’s scope,
                           limitations, and primary intended uses. Clearly specify the purposes of the GenAI system. The system should be
                           prepared to assist users when they inquire about its capabilities.
                           G1R2. Suggest Limits and Offer Assistance: The system should suggest its own limitations and deficiencies when
                           inaccuracies are detected in the generated results. This can foster trust and reassurance in users. If the system fails, it
                           should provide users with the option to be transferred to human customer support.
                           G1R3. Communicate Possibilities of Emergent Behaviors: If your system allows for emergent behaviors, ensure that
                           users understand that the GenAI-based system may provide feedback on topics for which they have not been
                           explicitly trained.
                           G1R4. Monitor and Mitigate Biases and Inappropriate Content: Test, monitor, and track the results generated from
                           user interactions with your GenAI solution, paying attention to outcomes that may exhibit potential biases, prejudices,
                           or even toxic content or misinformation. If such issues are identified, various mechanisms should be explored to
                           mitigate their impact, both at the system level and through communication with stakeholders.
                           G1R5. Document Limitations and Known Issues of the Model: Ensure that you identify and document the potential
              G1
                           limitations and known issues associated with the model used for the development of your GenAI-based solution.
                           Investigate whether there is verifiable information about similar solutions that have exhibited biases, prejudices, or
                           negative stereotypes when using the same model.
                           G1R6. Establish Ethical Boundaries and Detect Bias in Responses: Identify biased response patterns related to racial,
                           gender, ethnic, or political identities. Set clear boundaries for the behavior of the AI model, particularly concerning
                           sensitive content and language. Prioritize ethical guidelines that promote more holistic decision-making,
                           incorporating ethics, morality, and fairness.
                           G1R7. Raise Stakeholder Awareness of the Real Capabilities of AI: Educate stakeholders throughout the lifecycle,
                           guiding the development and eventual launch of the GenAI-based solution, providing a realistic and informed
                           perspective on how the system functions. This will help them avoid misinformation, false expectations, and
                           exaggerations about the capabilities of these types of solutions.
                           G1R8. Clearly Communicate the Scope of the Solution: Clearly articulate the solution’s scope at the outset of the
                           interaction to prevent user frustration when the system encounters requests that are beyond its capabilities. Ensure
                           that the organization transparently informs users that they are engaged with a GenAI-based system.
                           G2R1. Adapt the GenAI Solution to the Context and Communities: Acknowledge communities and their context by
                           understanding their cultural, economic, social, and governance limitations. Consider how to tailor your GenAI-based
                           solution to be useful in this context and strive to mitigate any potential features associated with this model and
                           pre-training that could undermine its primary purpose.
                           G2R2. Assessing the Need for Multimodality to Ensure Accessibility: Although incorporating multimodality into
                           GenAI solutions naturally entails a significantly higher cost, its necessity within a specific context and communities
                           should be evaluated. Determine the potential modalities required to ensure accessibility for most stakeholders. If
                           multimodality is not feasible in the initial versions due to various factors, ensure that the solution’s architecture is
                           sufficiently modular, flexible, and economically viable to accommodate future evolutions of GenAI systems.
                           G2R3. Use Open and Adaptable Models: Encourage the use of open models that allow for greater adaptability to a
                           knowledge base aligned with the context and communities targeted by your GenAI solution.
                           G2R4. Communicate Evidence of Biases and Prejudices in GenAI Responses: Upon identifying potential biases and
                           prejudices in your GenAI solution’s responses that could affect or harm users, such as those related to gender ideology,
                           sexual preferences, religious beliefs, or ethnic and social discrimination, promptly communicate these issues to
              G2           stakeholders. If possible, implement corrective actions to evolve the solution to minimize this type of response.
                           G2R5. Promote Inclusion and Consideration of GenAI Developers: Raise awareness among stakeholders to adopt an
                           attitude of inclusion and consideration toward those responsible for developing the GenAI solution. Encourage an
                           understanding of the limitations and risks inherent in the current use and adoption of such technologies. This
                           approach fosters relationships that support the system’s evolution through a more inclusive design process that
                           involves all stakeholders.
                           G2R6. Provide Accessible Alternatives to Generated Content: Offer alternatives to generated content, such as text
                           descriptions of images. These alternatives are essential to ensure that all users, regardless of their abilities, can access
                           and understand the information provided by the AI. Adopt recognized accessibility standards, such as the Web
                           Content Accessibility Guidelines (WCAGs).
                           G2R7. Ensure Accessibility Technologies and Navigation Options: Consider assistive technologies and alternative
                           navigation options to ensure that the experience is accessible to users with functional differences. Ensure that
                           multimodal information is accessible to all users by providing options such as subtitles for videos, audio descriptions
                           for images, contrast and readable font settings, adjustable speed of the information being presented, and flexible
                           interaction methods with different resources.
Informatics 2025, 12, 10                                                                                                                       8 of 25



                                 Table 2. Cont.

        UX Guideline                                                   Associated Recommendations
                           G3R1. Enable Co-Editing of Work Outputs: Ensure that your GenAI assistant promotes the co-editing of work outputs,
                           allowing users to enhance them with the system’s assistance.
                           G3R2. Identify Opportunities for Human–AI Collaboration: Adapt workflows and cooperative dynamics by
                           identifying points of the value chain where the use of a GenAI assistant can provide significant advantages. Define
                           work outputs amenable to generative processes, ensuring that the responsible use of the solution remains a core
                           principle throughout its implementation.
                           G3R3. Provide Guidance for Crafting Effective Prompts: Offer users guidance on how to improve the creation of
              G3           prompts to generate work outputs that are more consistent and aligned with users’ needs. The solution should
                           provide options and support to help users understand how to define a clear prompt, identify relevant keywords, and
                           establish an appropriate overall structure for writing prompts, tailored to their needs and the solution’s capabilities.
                           G3R4. Implement Real-Time Bidirectional Feedback: Incorporate functionalities in your solution that enable real-time
                           bidirectional feedback during human–AI interactions, allowing for the continuous improvement in work outputs
                           generated by the GenAI assistant.
                           G3R5. Provide Contextual Help for New Features: This feature provides users contextual assistance when they are
                           about to use a feature for the first time.
                           G4R1. Enable Context and Process Flow Configuration: Provide users with the ability to configure factors related to
                           the application context and process flow within the GenAI assistant, tailoring the generation of work outputs to meet
                           their expectations.
                           G4R2. Enable Interaction History Management: Allow users to retain their history of previous interactions with the
                           GenAI assistant, providing them with the ability to manage these records by deleting or preserving those deemed
                           relevant and necessary for their needs and goals.
                           G4R3. Provide Control Over Work Output Parameters: Allow users to control parameters related to generative work
                           outputs according to their needs. For example, users should be able to specify the number of images they wish to
                           produce or the number of alternatives they wish to generate for a specific planning strategy, among other needs,
                           depending on the context of the GenAI assistant solution.
              G4           G4R4. Personalize the Experience Through Individual Parameters and Preferences: Facilitate human–AI interaction by
                           promoting the personalization of the experience through a set of user-specific information and basic configuration
                           parameters (e.g., personal information, colors, interface layout, images, audio, etc.). Additionally, enable the modeling
                           of a structure for more personalized and user-centered generative response styles tailored to the needs of each user.
                           G4R5. Adapt the Communication Style to Context and User Profile: Ensure that the GenAI assistant offers a
                           communication style in human–GenAI interactions that is adaptable and consistent with both the application context
                           and the specific characteristics of the user profile for whom the solution is intended.
                           G4R6. Control and Monitor Anthropomorphization and Clarify the Nature of AI: Regulate and monitor the
                           anthropomorphization in human–GenAI interactions, reminding users that they are not interacting with a human
                           being (raising their awareness). As such, AI should not be attributed with intelligence, sensitivity, or empathy, and its
                           primary purpose is to assist users more efficiently.
                           G5R1. Guide Users in Identifying Expected Benefits: Support the user in defining the specific benefits anticipated to
                           arise from using a GenAI assistant on their processes, workflows, and work styles. In addition, it helps them identify
                           the key stages where the solution should be applied to maximize its impact.
                           G5R2. Disclose the Sources of Information Used: Inform stakeholders of the resources (sources) of the original
                           information, which enables the generation of responses that the GenAI system provides to the user.
                           G5R3. Set Realistic Expectations About Capabilities and Limitations: Communicate both the capabilities and
                           limitations of GenAI-based solutions to stakeholders to align user expectations with the system’s actual performance.
                           This approach helps build consistent trust in the system’s abilities and lays the foundation for future developments
                           that can progressively enhance user confidence in the GenAI system.
                           G5R4. Explain the Generation Processes and Mechanisms: Clearly and simply communicate to users the processes
                           and mechanisms used by AI models that enable the generation of responses or work outputs.
                           G5R5. Promote Safe Practices and Protect Sensitive Data: Inform users about the importance of not using sensitive
              G5
                           data during interactions with the GenAI system to ensure data security and prevent practices such as phishing,
                           privacy violations, confidential information leaks, and other security breaches. It is crucial that users are aware of how
                           to interact safely with a GenAI-based assistant and understand the risks associated with the inadvertent introduction
                           of sensitive data.
                           G5R6. Enable Persistence of Information in Interaction Sessions: The solution should be capable of maintaining
                           persistence in the information and elements generated during human–GenAI interaction sessions, thereby enhancing
                           the optimization of prompt engineering.
                           G5R7. Provide Users Control Over Personal Data: Offer intuitive interfaces within the GenAI assistant that allow
                           users to manage their personal information. This includes options to opt out of personal data collection and
                           mechanisms to ensure continuous and direct control over personal data.
                           G5R8. Scale the Solution While Preserving Privacy and Quality: Implement mechanisms that allow the GenAI
                           assistant to scale to a larger user base without compromising data privacy or the quality of the generated responses.
                           G6R1. Encourage Critical Evaluation of Generated Results: Raise user awareness about the importance of critically
                           analyzing the work outputs generated by GenAI mediation and assessing their accuracy and validity.
                           G6R2. Provide Multiple Generative Response Options: Whenever possible, offer users a set of generative response
              G6           options produced by the solution, allowing them to select the one that best aligns with their interests and needs.
                           G6R3. Provide Predefined Questions Aligned with the Scope: Consider offering users a predefined list of questions for
                           using a solution that aligns with its scope, allowing users to quickly obtain information. However, note that
                           predefined questions may limit the ability to explore all the system’s options.
Informatics 2025, 12, 10                                                                                           9 of 25



                           3.3. Toolkit for UX Design Guidelines for GenAI-Based Assistants
                                 While the guidelines and recommendations provided for the UX design of GenAI-
                           based assistants can offer a solid foundational basis to a team responsible for creating such
                           solutions, they are not sufficient on their own to ensure activities that genuinely enrich the
                           collaborative and creative work dynamics necessary for determining how to integrate these
                           guidelines. This is particularly true in processes that require ideation and collaboration
                           dynamics with a creative focus for UX design [48], which can significantly influence the
                           considerations to be considered in a requirement engineering process within the context of
                           developing GenAI-based solutions.
                                 Card-based design tools have significantly enhanced ideation and creativity processes,
                           fostering high levels of collaboration and interaction among participants [28]. Challenges
                           that require diverse approaches—such as addressing ethical and moral issues in the me-
                           diation of information technologies—are widely supported by theoretical approaches,
                           including Value-Sensitive Design (VSD), through the use of card-based toolkits [25].
                                 The use of card-based tools has also been extended to other disciplines, including
                           software engineering. In this field, standards such as Essence apply card-based tools
                           to design artifacts, including Alphas, to specify states and verification criteria [49]. This
                           strategy aims to facilitate communication among team members regarding project evolution,
                           thereby strengthening collaborative work.
                                 Given the importance of collaborative work in the ideation and creativity processes as-
                           sociated with UX design [50], a card-based toolkit was developed. This toolkit incorporates
                           UX design guidelines and recommendations for developing GenAI-based assistants. The
                           structure for the toolkit’s design was primarily based on previous experiences, such as the
                           IDEO method card set [37] and the VSD toolkit [38].
                                 Figure 2 presents the digital version of a set of cards from the UX design guide-
                           lines toolkit for GenAI-based assistants, specifically corresponding to guidelines G1 “De-
                           sign Based on Ethical and Responsible Principles” and G2 “Design Based on Principles
                           of Inclusion”.
                                 In designing the toolkit, color theory was used to facilitate the understanding and
                           categorization of the guidelines. Each guideline is associated with a specific color that
                           evokes psychological responses aligned with its content: red represents ethics and responsi-
                           bility; blue conveys urgency [32], associated with inclusion, and suggests trust [33]; orange,
                           for AI–human collaboration, evokes dynamism; purple is linked to personalization and
                           connotes innovation; green, representing reliability, communicates stability; and light blue,
                           for precision, conveys clarity. This color-coding enhances the tool’s visual esthetics and
                           improves the cognitive assimilation of the presented concepts.
                                 From a Gestalt psychology perspective, the card design implements principles that
                           optimize the organization and perception of information. Proximity and similarity are
                           applied to the grouping of textual elements and the consistent use of styles, creating visual
                           coherence and facilitating categorization. The principle of closure is evident in how color
                           blocks frame content, forming complete perceptual units. The figure-ground principle,
                           which uses contrasting text over solid color backgrounds, ensures a clear visual hierarchy
                           and optimal legibility. This content structure, grounded in Gestalt principles, seeks to
                           improve the toolkit’s usability and enhance the effective communication of guidelines and
                           recommendations for UX design in GenAI solutions [51].
                                 Figure 3 shows the printed version of the toolkit, which presents the deck associated
                           with the six UX guidelines for GenAI-based assistants.
                           and optimal legibility. This content structure, grounded in Gestalt principles, seeks to im-
                           prove the toolkit’s usability and enhance the eﬀective communication of guidelines and
                           recommendations for UX design in GenAI solutions [51].
                           and optimal legibility. This content structure, grounded in Gestalt principles, seeks to im-
Informatics 2025, 12, 10
                           prove the toolkit’s usability and enhance the eﬀective communication of guidelines10and   of 25

                           recommendations for UX design in GenAI solutions [51].




                           Figure 2. A sample of digital cards from the GenAI toolkit for guidelines G1 and G2 and recommen-
                           dations G1R1 and G2R1. Created by the author.

                            Figure2.2.A
                                      A sample
                                        sample of
                                               of digital cards
                                                          cards from
                                                                from the
                                                                     the GenAI
                                                                         GenAI toolkit  for guidelines
                                                                                             guidelinesG1
                                                                                                        G1and
                                                                                                           andG2 and
                                                                                                                  andrecommen-
                           Figure
                                 Figure   3 showsdigital
                                                   the printed   version  of the toolkit
                                                                                 toolkit,for
                                                                                           which  presents  theG2
                                                                                                               deck   recommen-
                                                                                                                     associated
                            dationsG1R1
                           dations   G1R1and
                                           andG2R1.
                                               G2R1. Created
                                                     Created by
                                                              by the author.
                           with the six UX guidelines for GenAI-based assistants.
                               Figure 3 shows the printed version of the toolkit, which presents the deck associated
                           with the six UX guidelines for GenAI-based assistants.




                            Figure3.3.A
                           Figure     Asample
                                        sample of
                                               of printed
                                                  printed version cards of the GenAI
                                                                               GenAI toolkit
                                                                                     toolkit for
                                                                                             for the
                                                                                                  the six
                                                                                                       sixUX
                                                                                                          UXguidelines.
                                                                                                             guidelines.Created
                                                                                                                         Created
                            bythe
                           by  theauthor.
                                   author.

                            Figure
                           3.4.    3. A sample of printed version cards of the GenAI toolkit for the six UX guidelines. Created
                            3.4.Evaluation
                                 Evaluation
                            by the author.
                                 A validation process was conducted to evaluate the contribution of the UX design
                           guidelines toolkit in the design of GenAI-based assistants, focusing on three key com-
                            3.4. Evaluation
                           ponents: usability, user experience, and utility. This process was conducted through a
                           hands-on workshop with an experienced software development company of 10 partici-
                           pants organized into three interdisciplinary teams. These teams included UX designers,
                           developers, requirements analysts, and other relevant roles critical to this project.
Informatics 2025, 12, 10                                                                                             11 of 25



                                 The initial hypothesis to be validated was that using the toolkit would enhance the
                           perceived usability, utility, and user experience during the UX design process, particularly
                           in the requirements analysis phase of GenAI-based assistant development. This premise
                           was evaluated from the perspective of the interdisciplinary development teams responsible
                           for its implementation.
                                 The toolkit was applied to a set of 11 predefined functional and non-functional re-
                           quirements for developing a GenAI-based assistant as part of a project to establish an
                           educational assistant for the Valle del Cauca, Colombia, school system. These requirements
                           were defined based on their representativeness in incorporating the UX design guidelines
                           for the development of the GenAI assistant. This selection was conducted in adherence to
                           the principles of the DSRM [52], focusing on requirements that provide representativeness
                           for the validation of the artifacts under evaluation. This project was led by a software
                           development company with the participation of the University Autónoma de Occidente in
                           Cali, Colombia, and the University of Salamanca in Salamanca, Spain. Following the work-
                           shop, the participants completed a questionnaire adapted to the MEEGA+ instruments [37]
                           and meCUE 2.0 [38] for a usability assessment in systems. The questionnaire included
                           23 statements related to the defined evaluation components, rated on a 5-point Likert
                           scale, along with three open-ended questions about positive aspects, negative aspects,
                           and additional comments on the instrument. Table 3 provides a detailed overview of the
                           questions answered by the users.

                           Table 3. Evaluation questionnaire items for the UX guidelines toolkit for GenAI system.

                                  Factor             Code                                Question
                                                                 The visual design of the toolkit facilitates the rapid
                                 Usability             P1
                                                                 identification of guidelines for GenAI solutions.
                                                                 The instructions and examples provided in the toolkit
                                 Usability             P2
                                                                 are clear and easily understandable.
                                                                 Learning to use the toolkit was straightforward and
                                 Usability             P3
                                                                 intuitive for me.
                                                                 I believe that most individuals can quickly learn to
                                 Usability             P4
                                                                 use this toolkit effectively.
                                                                 The recommendations and guidelines provided by the
                                 Usability             P5        toolkit are clear and comprehensible within the
                                                                 specific context of UX design for GenAI.
                                                                 The fonts (size and style) used in the toolkit are easily
                                 Usability             P6
                                                                 readable.
                                                                 The structure of the toolkit allows for easy navigation
                                 Usability             P7
                                                                 across different UX aspects specific to GenAI.
                                                                 I am satisfied with how the toolkit assisted me in
                             User Experience           P8
                                                                 addressing specific UX challenges.
                                                                 The use of the toolkit was a pleasant experience, free
                             User Experience           P9
                                                                 from frustration.
                                                                 I find the content of the toolkit to be relevant and
                             User Experience          P10
                                                                 useful for my work.
                                                                 The toolkit provides concrete and applicable
                             User Experience          P11        examples of how to implement UX guidelines across
                                                                 various types of GenAI solutions.
Informatics 2025, 12, 10                                                                                            12 of 25



                           Table 3. Cont.

                                  Factor            Code                               Question
                                                                Using the toolkit enabled me to enhance the accuracy
                             User Experience         P12
                                                                of the defined requirements.
                                                                The guidelines and examples provided by the toolkit
                             User Experience         P13
                                                                are useful and applicable to my specific needs.
                                                                The toolkit promotes collaboration and discussion
                             User Experience         P14
                                                                within the work team.
                                                                If possible, I would use this toolkit on a daily basis to
                             User Experience         P15
                                                                tackle UX design projects for GenAI.
                                                                I would choose the toolkit for use in future projects
                             User Experience         P16
                                                                related to GenAI.
                                                                I prefer using the physical version of the toolkit over
                             User Experience         P17
                                                                the digital version.
                                                                The toolkit helped me identify critical UX aspects that
                                  Utility            P18
                                                                are unique to GenAI solutions.
                                                                The toolkit’s recommendations were directly
                                  Utility            P19        applicable to the UX challenges encountered in the
                                                                projects.
                                                                The use of the toolkit allowed me to save time in
                                  Utility            P20        identifying and resolving GenAI-specific UX
                                                                problems.
                                                                The toolkit provides valuable guidance on how to
                                  Utility            P21        effectively communicate the capabilities and
                                                                limitations of GenAI to end-users.
                                                                The toolkit was useful in anticipating and mitigating
                                  Utility            P22
                                                                potential ethical issues in UX design for GenAI.
                                                                The toolkit is a valuable resource for enhancing UX in
                                  Utility            P23
                                                                GenAI projects.


                                The analysis of the results was conducted in two phases. The first phase involved an
                           analysis of the quantitative data, using non-parametric statistics and the statistical software
                           R-3.4.2 [53] to identify significant conclusions from the participants’ evaluations. The
                           second phase involved a thematic analysis of the qualitative information obtained through
                           observations and discussions.
                                To quantitatively evaluate the toolkit, the analysis incorporates several statistical
                           techniques. The internal consistency of the instruments is assessed through Cronbach’s
                           Alpha coefficient, which was calculated for three primary dimensions: usability, user
                           experience, and utility. This coefficient measures the reliability of the item set, with values
                           above 0.70 indicating strong internal consistency [54]. Cronbach’s Alpha is formally defined
                           as follows:
                                                                             ∑iN=1 σY2i
                                                                                        !
                                                                    N
                                                            α=           1−                                             (1)
                                                                  N−1           σX2

                           where N denotes the number of items, σY2 represents the variance of each item Yi , and σX2
                                                                      i
                           indicates the total variance of the test.
                                A Principal Component Analysis (PCA) was applied to reduce data dimensionality
                           and identify the variables explaining the majority of variability within each dimension. The
                           PCA included supplementary variables (role, experience, and frequency of use) to enhance
Informatics 2025, 12, 10                                                                                              13 of 25



                           the interpretation of the results. Formally, PCA attempts to identify a matrix P of principal
                           components, such that the following is true [55]:

                                                                       Z = XP                                             (2)

                           where X is the standardized data matrix, P is the eigenvector matrix defining the directions
                           of maximum variance, and Z is the transformed matrix of the principal components.
                                To visualize and interpret the results, contribution plots and biplots were generated.
                           Additionally, a sentiment analysis was conducted using natural language processing (NLP)
                           techniques on the qualitative observations collected from the questionnaire.

                           4. Results and Discussion
                           4.1. Quantitative Results
                                The reliability analysis results using Cronbach’s Alpha coefficient demonstrated high
                           internal consistency across the three evaluated dimensions. For the usability section, a raw
                           Cronbach’s Alpha of 0.82 and a standardized Alpha of 0.88 were obtained, reflecting high
                           reliability with an average inter-item correlation of 0.5. In the user experience dimension,
                           the raw Alpha was 0.82 and the standardized Alpha was 0.81, with an average inter-item
                           correlation of 0.3, indicating moderate consistency. Finally, in the utility dimension, a raw
                           Alpha of 0.84 and a standardized Alpha of 0.91 were achieved, indicating excellent internal
                           consistency with an average inter-item correlation of 0.63.
                                Table 4 presents the cumulative percentage of variance explained by each component
                           across the three categories. The results suggest that retaining two components per category
                           is sufficient to explain at least 70% of the variability, indicating that only the first factorial
                           plane of each category can be interpreted.

                           Table 4. Cumulative percentage of variance by category.

                                                                    Cumulative Percentage of Variance
                                Component
                                                          Usability           User Experience               Utility
                                      1                      61.5                     45.4                    76.9
                                      2                      89.3                     70.7                    94.5
                                      3                      97.4                     83.8                    98.1
                                      4                      99.6                     92.3                    99.4
                                      5                     100.0                     99.0                   100.0
                                      6                     100.0                    100.0                   100.0


                                Figure 4 illustrates the behavior of the variables in the first factorial plane for
                           each category.
                                The results of the Principal Component Analysis (PCA) reveal distinct patterns across
                           the three evaluated dimensions: usability, user experience, and utility. In the usability
                           dimension, the first principal component explains 61.54% of the variance, while the second
                           component accounts for 27.79%, totaling 89.33% of the explained variance. The first
                           component is dominated by variables related to the clarity and ease of use of the toolkit
                           (P1, P3, P4, P5, P7), which cluster closely on the positive side of the horizontal axis. The
                           second component reflects aspects of accessibility and structure, primarily represented by
                           P6 (font readability) and P2 (operability).
Informatics 2025, 12, x FOR PEER REVIEW                                                                                                   16 of 28
 Informatics 2025, 12, 10                                                                                                                  14 of 25




                               (a) Usability                                                  (b) User experience




                                                                  (c) Utility

                                  Figure
                                  Figure 4.
                                         4. Biplot
                                            Biplotby
                                                   bycategory:
                                                     category: (a)
                                                                (a)Usability,
                                                                   Usability, (b)
                                                                              (b) User
                                                                                  User experience,
                                                                                       experience, (c)
                                                                                                   (c) Utility.
                                                                                                       Utility.

                                        The   results
                                         In the  user of   the Principal
                                                        experience         Component
                                                                      category,    the firstAnalysis
                                                                                             component (PCA)    reveal distinct
                                                                                                             explains   45.42% patterns     across
                                                                                                                                  of the variance,
                                  the
                                  whilethree
                                           theevaluated     dimensions:accounts
                                                second component            usability,   user
                                                                                       for     experience,
                                                                                            25.29%,    totalingand  utility. A
                                                                                                                  70.71%.    In clear
                                                                                                                                the usability   di-
                                                                                                                                        separation
                                  mension,     the  first principal   component      explains    61.54%    of the  variance,
                                   is observed between two groups of variables: one capturing overall satisfaction and the     while   the second
                                  component       accounts
                                  perceived utility           fortoolkit
                                                         of the   27.79%,    totaling
                                                                          (P8,          89.33%
                                                                                P10, P11,   P12,of  theand
                                                                                                  P13)    explained
                                                                                                              anothervariance.     The first
                                                                                                                        related more          com-
                                                                                                                                         closely  to
                                  ponent    is dominated       by variables    related   to the  clarity
                                   future usage intent and format preferences (P9, P15, P16, P17).        and   ease  of use  of  the  toolkit (P1,
                                  P3, P4,InP5,
                                             theP7),  which
                                                 utility       cluster closely
                                                           dimension,    the firstoncomponent
                                                                                     the positiveexplains
                                                                                                    side of the   horizontal
                                                                                                                a very          axis. The second
                                                                                                                        high proportion      of the
                                  component       reflects   aspects   of  accessibility    and   structure,    primarily   represented
                                  variance (76.95%), with all variables (P19 to P23) showing strong correlations and clustering              by  P6
                                  (font
                                   towardreadability)
                                             the positive andside
                                                               P2 (operability).
                                                                   of this component. This suggests that all the assessed aspects of
                                   utility contribute similarly category,
                                        In  the  user  experience                  the first
                                                                      to the overall         component
                                                                                        perception     of theexplains  45.42%
                                                                                                               toolkit’s  utility.ofHowever,
                                                                                                                                     the variance,
                                                                                                                                                P18
                                  while   the second
                                  (identification         component
                                                      of critical        accounts
                                                                  UX aspects)        for 25.29%,
                                                                                  stands   out due tototaling   70.71%.
                                                                                                        its strong        A clear
                                                                                                                    alignment       separation
                                                                                                                                  with  the secondis
                                  observed     between     two   groups   of variables:    one  capturing    overall  satisfaction
                                   component, which indicates that it may capture a unique dimension of the toolkit’s utility.        and the  per-
                                  ceived    utility
                                         Figure      of the toolkit
                                                  5 presents          (P8, P10, P11,
                                                               the contribution           P12,ofP13)
                                                                                    analysis     eachand     another related
                                                                                                        component.               more closely
                                                                                                                       In the usability          to
                                                                                                                                          category,
                                  future
                                  variablesusage
                                               P3, intent
                                                    P6, P7,andandformat    preferences
                                                                   P4 exhibit              (P9, contributions.
                                                                                 the highest    P15, P16, P17).These variables correspond
                              P18 (identification of critical UX aspects) stands out due to its strong alignment with the
                              second component, which indicates that it may capture a unique dimension of the toolkit’s
                              utility.
Informatics 2025, 12, 10                                                                                                   15 of 25
                                    Figure 5 presents the contribution analysis of each component. In the usability cate-
                              gory, variables P3, P6, P7, and P4 exhibit the highest contributions. These variables corre-
                              spond
                              to       to aspects
                                 aspects   such assuch  as design,
                                                   visual  visual design,  toolkit structure,
                                                                   toolkit structure,         ease of learning,
                                                                                      ease of learning, and theand    the percep-
                                                                                                                  perception  that
                              tion  that  others can  quickly  learn to use  the toolkit. This result suggests
                              others can quickly learn to use the toolkit. This result suggests that ease of usethat  easeand
                                                                                                                           of use
                                                                                                                               the
                              and  the  toolkit’s structure  are fundamental   aspects  in the perception  of
                              toolkit’s structure are fundamental aspects in the perception of its usability. its usability.




                           (a) Usability                                                (b) User experience




                                                             (c) Utility

                              Figure 5. Contribution
                                           Contributionofofthe
                                                            thefirst
                                                                 firstfactorial plane
                                                                        factorial     byby
                                                                                  plane   category: (a) (a)
                                                                                             category:  Usability, (b) (b)
                                                                                                            Usability, User Experience,
                                                                                                                           User         (c)
                                                                                                                                Experience,
                              Utility.
                              (c)      The
                                  Utility.  redred
                                           The  line represents
                                                   line          1/number
                                                        represents   1/number of variables     × 100%.
                                                                                            × 100%.
                                                                                  of variables

                                   In the user experience category, variables P15, P8, P9, and P13 show the highest
                              contributions. These variables are associated with the willingness to use the toolkit daily,
                              the pleasantness of the user experience, satisfaction with how the toolkit aids in addressing
                              specific UX challenges, and the provision of concrete and applicable examples. This
                              indicates that overall satisfaction, perceived utility, and practical applicability are key
                              aspects of the UX with the toolkit.
                                   In the utility dimension, a more uniform distribution of contributions is observed.
                              Variables P18 and P21 stand out slightly, closely followed by P23, P19, and P22. These
                              variables assess the toolkit’s capacity to identify critical UX aspects in GenAI solutions,
Informatics 2025, 12, 10                                                                                                     16 of 25



                                   provide useful guidance on communicating GenAI capabilities and limitations, and assess
                                   its overall value for enhancing user experience in GenAI projects. The high contribution of
                                   all the variables in this category indicates that the toolkit is perceived as a comprehensive
                                   and valuable tool for addressing various UX aspects in GenAI projects [56].
                                         It is noteworthy that, across all categories, the variables with the highest contributions
                                   are related to the practical and applicable aspects of the toolkit, reinforcing its perception
                                   as a useful and relevant tool for professionals working on GenAI projects [57,58].
                                         Figure 6 presents the relationship between the collected demographic variables
Informatics 2025, 12, x FOR PEER REVIEW                                                                                     19 of 28
                                   (project role, experience with similar tools, and frequency of use of similar tools)        and
                                   the usability component.




                                (a) Role                                              (b) User experience




                                                       (c) Frequency of use

                                Figure 6.
                                Figure 6. Relationship
                                          Relationship between
                                                       between demographic
                                                                demographic variables
                                                                            variables and
                                                                                      and usability
                                                                                          usability component:
                                                                                                    component: (a)
                                                                                                               (a) Role,
                                                                                                                   Role, (b)
                                                                                                                          (b) User
                                                                                                                              User
                                Experience, (c)
                                            (c) Frequency
                                                Frequency of
                                                          of use.
                                                             use. .

                                     UX designers and project managers align with variables P1, P2, and P7. This suggests
                                that these roles, often involved in project supervision and planning, place high value on
                                the toolkit’s ability to provide clear and easily accessible information. In contrast, devel-
                                opers and other roles display a weaker association with these variables, which may indi-
                                cate that they prioritize diﬀerent aspects of the toolkit or interact with it in a distinct man-
                                ner.
Informatics 2025, 12, 10                                                                                            17 of 25



                                 UX designers and project managers align with variables P1, P2, and P7. This suggests
                           that these roles, often involved in project supervision and planning, place high value on the
                           toolkit’s ability to provide clear and easily accessible information. In contrast, developers
                           and other roles display a weaker association with these variables, which may indicate that
                           they prioritize different aspects of the toolkit or interact with it in a distinct manner.
                                 Users with little or no prior experience cluster more closely with variables P3 and P4,
                           which correspond to the ease of learning the toolkit. This indicates that novice users find
                           the toolkit’s learning curve manageable, which is a crucial factor for its adoption in diverse
                           teams. In contrast, highly experienced users demonstrated a stronger association with P2
                           and P6, suggesting that they focused more on the presentation details and structure of
                           the toolkit.
                                 Frequent users of design tools tend to value the same variables—P1, P2, and P6—as
                           users in higher-responsibility roles. This suggests that greater familiarity with the toolkit
                           enhances appreciation of its design elements and clear instructions. In contrast, those who
                           never used design tools showed a stronger association with questions related to ease of
                           learning, implying that their lack of use may be linked to perceived learning barriers.
                                 Figure 7 illustrates the relationship between the collected demographic variables
                           (project role, experience with similar tools, and frequency of use of similar tools) and the
                           UX component.
                                 Project managers and UX designers align more closely with variables P9 (pleasant
                           experience), P16 (choice for future projects), and P17 (preference for the physical version).
                           This alignment indicates that these roles find the toolkit satisfactory and are likely to
                           integrate it into their workflow. Developers, on the other hand, show stronger associations
                           with P11 (concrete examples) and P12 (accuracy improvement), suggesting that they value
                           the toolkit’s practical applications and its ability to enhance their work output.
                                 Experienced users exhibit a strong alignment with variables P15 and P16, which
                           indicates that those with more field experience recognize the long-term value of the toolkit
                           and are more likely to integrate it into their regular practices. In contrast, users with little
                           or no experience are more likely to be associated with P12 and P13, suggesting that they
                           appreciate the toolkit’s ability to provide concrete guidance and enhance the quality of
                           their work.
                                 Occasional users show a connection with P15 and P16, indicating that even infrequent
                           use fosters appreciation for the toolkit’s value in daily work and future projects. Those
                           who never used the toolkit were more likely to have questions about the relevance of
                           the examples (P10, P11), suggesting that demonstrating the toolkit’s applicability to their
                           specific tasks could increase adoption rates.
                                 Figure 8 presents the relationship between the collected demographic variables
                           (project role, experience with similar tools, and frequency of use of similar tools) and
                           the utility component.
                                 Project managers and UX designers align with P18 (identification of critical UX aspects),
                           highlighting their focus on high-level UX strategies. Developers, on the other hand, are
                           more associated with variables related to time-saving and the communication of GenAI
                           capabilities, reflecting their interest in practical efficiency and the technical communication
                           aspects of the toolkit.
                                 Highly experienced users align with P18, suggesting that they find the toolkit par-
                           ticularly valuable for identifying critical UX issues in GenAI projects. Users with little or
                           no experience are more likely to be associated with P21 and P22 (ethical issue mitigation),
                           indicating that the toolkit provides essential guidance on complex GenAI aspects for those
                           with less field experience.
                           never used design tools showed a stronger association with questions related to ease of
                           learning, implying that their lack of use may be linked to perceived learning barriers.
                                Figure 7 illustrates the relationship between the collected demographic variables
Informatics 2025, 12, 10   (project role, experience with similar tools, and frequency of use of similar tools) and
                                                                                                                 18 ofthe
                                                                                                                       25
                           UX component.




                           (a) Role                                                  (b) Experience




                                                   (c) Frequency of use

                            Figure 7. Relationship between demographic variables and UX component: (a) Role, (b) Experi-
                           Figure 7. Relationship between demographic variables and UX component: (a) Role, (b) Experience,
                            ence,
                           (c)    (c) Frequency
                               Frequency  of use.of use.

                                 Project managers
                                Occasional             and UX
                                               users value   P18 designers  align more closely
                                                                  and P19 (applicability           with variables
                                                                                           to UX challenges),       P9 (pleasant
                                                                                                                suggesting    that
                           experience),
                           even  infrequentP16use
                                                (choice
                                                    aids for future projects),
                                                         in identifying        and P17 (preference
                                                                        and addressing     critical UXfor the physical
                                                                                                       issues.          version).
                                                                                                                Users who    have
                           This alignment
                           never  used design indicates   that these
                                                   tools align  moreroles find
                                                                     closely    the P20,
                                                                              with  toolkit satisfactory
                                                                                          implying        andtoolkit’s
                                                                                                     that the  are likelycapacity
                                                                                                                           to inte-
                           grate
                           to     it intoethical
                              address      their workflow.
                                                  challenges Developers,    on the other
                                                               could be a compelling        hand,toshow
                                                                                         feature          stronger
                                                                                                     increase        associations
                                                                                                               adoption    within
                           this group.
                                This analysis reveals that the toolkit provides value across different roles, experience
                           levels, and usage frequencies, with each group deriving unique benefits. The toolkit is
                           particularly effective in offering clear guidance, facilitating learning, and addressing critical
                           UX and ethical issues in GenAI projects. These findings will inform future iterations of the
                           toolkit, ensuring that it continues to meet the diverse needs of its user base and potentially
                           increases its adoption and effectiveness across all groups.
                           of the examples (P10, P11), suggesting that demonstrating the toolkit’s applicability to
                           their specific tasks could increase adoption rates.
                                 Figure 8 presents the relationship between the collected demographic variables (pro-
Informatics 2025, 12, 10   ject role, experience with similar tools, and frequency of use of similar tools) and the 19
                                                                                                                    utility
                                                                                                                       of 25
                           component.




                           (a) Role                                              (b) Experience




                           Figure 8. Relationship between demographic variables and utility component: (a) Role, (b) Experi-
                           ence, (c) Frequency of use.

                                The quantitative analysis supports the effectiveness and perceived value of the UX
                           guidelines toolkit for the design of GenAI assistants, thereby enhancing the perceived
                           usability, utility, and user experience during the UX design process. The results indicate
                           that the toolkit is viewed as clear, applicable, and highly useful for enhancing UX and
                           addressing specific challenges in GenAI projects. The observed differences across roles,
                           experience levels, and usage frequency suggest that the toolkit is versatile and adaptable to
                           diverse needs and usage contexts, while also highlighting potential areas for improvement
                           in future iterations of the toolkit.

                           4.2. Qualitative Results
                               Figure 9 presents the results of the sentiment analysis conducted on the qualitative
                           responses from the questionnaire.
                           in future iterations of the toolkit.

                           4.2. Qualitative Results
Informatics 2025, 12, 10                                                                                    20 of 25
                               Figure 9 presents the results of the sentiment analysis conducted on the qualitative
                           responses from the questionnaire.




                           Figure 9. Sentiment analysis of qualitative questionnaire responses.
                           Figure 9. Sentiment analysis of qualitative questionnaire responses.
                                The results reveals that positive sentiment is predominant, followed by expressions
                                The results reveals that positive sentiment is predominant, followed by expressions
                           of trust, suggesting that participants generally had a favorable experience with the toolkit
                           of trust, suggesting that participants generally had a favorable experience with the toolkit
                           and  felt confident in its capabilities and applicability. Finally, Figure 10 presents a word
                           and felt confident in its capabilities and applicability. Finally, Figure 10 presents a word
                           cloud  generatedfrom
                           cloud generated    fromthethe participants’
                                                       participants’    observations.
                                                                     observations.




                                  10.Word
                           Figure 10.
                           Figure     Wordcloud of of
                                            cloud  terms from
                                                      terms   qualitative
                                                            from          responses.
                                                                 qualitative   responses.

                                This visualization highlights key terms, such as “participants”, “toolkit”, “cards”,
                                 This visualization highlights key terms, such as “participants”, “toolkit”, “cards”,
                           “instructions”, and “guidelines”, reflecting the most relevant aspects of user interaction
                           “instructions”, and “guidelines”, reflecting the most relevant aspects of user interaction
                           with the toolkit.
                           withThe
                                 the toolkit.
                                      qualitative information gathered following the use of the toolkit during the
                           discussionqualitative
                                 The    session and information
                                                      observationalgathered
                                                                      processfollowing
                                                                               was analyzedthe use  of athe
                                                                                                using        toolkit analysis
                                                                                                          thematic      during the dis-
                           cussion
                           approach, session   and observational
                                       identifying                    process themes
                                                     patterns and recurring     was analyzed      using aresponses.
                                                                                        in participants’      thematic analysis
                                                                                                                            This    ap-
                           proach,
                           analysis identifying
                                     provided an patterns    and recurringof
                                                   in-depth understanding    themes    in participants’
                                                                                how users   interacted withresponses.     This
                                                                                                                the toolkit, theanalysis
                           challenges  they  encountered,   and the areas in which   they perceived  the   greatest
                           provided an in-depth understanding of how users interacted with the toolkit, the chal-     value.
                                Regarding
                           lenges           usability andand
                                   they encountered,       learning curve,inparticipants
                                                               the areas     which they  demonstrated
                                                                                            perceived the a general
                                                                                                               greatestability to
                                                                                                                           value.
                           use the  toolkit without  the need  for extensive  additional  instructions.  An    initial
                                 Regarding usability and learning curve, participants demonstrated a general ability   learning
                           curve was observed, followed by an adaptation phase that allowed users to explore the con-
                           to use the toolkit without the need for extensive additional instructions. An initial learning
                           tent more efficiently. Although the toolkit has an intuitive design, it could benefit from more
                           curve was observed, followed by an adaptation phase that allowed users to explore the
                           detailed introductory instructions to facilitate an initial understanding of the instrument.
                           content more eﬃciently. Although the toolkit has an intuitive design, it could benefit from
                           more detailed introductory instructions to facilitate an initial understanding of the instru-
                           ment.
                                Preferences in terms of format and interaction were evident, with a clear preference
                           for handling the physical cards of the toolkit; this facilitated collective visualization and
Informatics 2025, 12, 10                                                                                          21 of 25



                                 Preferences in terms of format and interaction were evident, with a clear preference
                           for handling the physical cards of the toolkit; this facilitated collective visualization and
                           collaboration in group discussions [59]. Although the participants interacted with the
                           digital version, they found the physical version more useful for collaborative activities.
                           These results suggest that maintaining visual and practical elements in the future design of
                           the instrument is important for enhancing its use in team-based environments.
                                 The toolkit effectively fosters idea exchange and active collaboration among partici-
                           pants. Insightful discussions emerged around topics such as usability and utility, indicating
                           that the proposed instrument is well suited to promote reflection on relevant UX principles
                           in the design of GenAI assistants [60].
                                 Participants required more time to complete the proposed tasks than initially planned.
                           This suggests a need to re-evaluate and adjust the recommended time allocations for
                           effective toolkit application in real productive contexts. Such adjustments are essential
                           to ensure that users can thoroughly explore and integrate the toolkit’s guidelines and
                           recommendations without compromising efficiency in UX design processes [61].
                                 The toolkit demonstrated adaptability to various work styles and group dynamics.
                           Participants employed different approaches to organizing and using the cards, indicating
                           that the tool is sufficiently flexible to accommodate diverse methodologies and team
                           collaboration preferences.

                           5. Conclusions and Future Work
                                The results in Section 4 are consistent with the initial hypothesis, indicating that
                           the use of the toolkit indeed enhances perceived usability, utility, and user experience
                           in UX design within the development process of GenAI-based assistants, particularly
                           during the requirements analysis phase, where experimentation was conducted. The study
                           participants highlighted aspects such as the clarity of the recommendations, the organized
                           structure of the toolkit, and its utility in addressing specific GenAI design challenges,
                           including human–AI collaboration, ethical considerations in design, and adaptability to
                           various contexts.
                                The structure of the toolkit, which is grounded in principles of ethical design, cus-
                           tomization, and reliability, facilitated the addressing of complex, specific aspects of GenAI-
                           based assistants, such as the need for transparency and alignment with user expectations.
                           In addition, the recommendations on managing the communicability of system limitations
                           and potential emergent behaviors in GenAI were perceived as valuable for mitigating
                           misaligned expectations and fostering responsible human–AI interaction as a foundation
                           for high-quality UX.
                                For users who require more time to become familiar with the toolkit, several recom-
                           mendations are suggested to facilitate their engagement. First, providing more detailed
                           introductory instructions can help reduce the initial learning curve by providing a clear
                           guide on the structure and purpose of each card and guideline before the first application
                           in context. Additionally, implementing guided exercises or practical examples can be con-
                           sidered, allowing users to explore the toolkit in a structured and gradual manner, focusing
                           on step-by-step familiarization. In line with this approach, the development of a website is
                           currently being considered to enable stakeholders to access this foundational information
                           before using the toolkit.
                                Despite these encouraging results, the toolkit has some limitations. The findings
                           suggest the need to validate the toolkit with a broader range of design teams and end-users,
                           particularly those operating in varied organizational and cultural contexts. Expanding
                           this validation would ensure that the toolkit maintains its effectiveness and applicability
Informatics 2025, 12, 10                                                                                                             22 of 25



                                  across diverse scenarios, thereby contributing to its robustness and adaptability to different
                                  design needs.
                                       In future work, we will suggest investigating the integration of the toolkit into collabo-
                                  rative methodologies that involve end-users throughout the entire lifecycle of the GenAI-
                                  based assistant development process, from the early design phases. This includes human-
                                  centered design methodologies, in which users actively participate from the ideation phase
                                  to the final implementation, ensuring that development aligns with users’ needs and ex-
                                  pectations. Value-Sensitive Design is also applicable, as it integrates ethical and moral
                                  principles directly into the design process. In addition, employing these approaches in itera-
                                  tive design frameworks and agile methodologies would support the continuous adaptation
                                  of UX guidelines to respond to changing user needs and contexts. This research could be
                                  extended to study user satisfaction, the efficiency of human-centered design processes, and,
                                  ultimately, its validation within human–GenAI assistant interactions.

                                  Author Contributions: Conceptualization, C.A.P., A.S. and J.C.E.; methodology, C.A.P., J.A.O., P.A.C.
                                  and J.C.E.; validation, C.A.P., J.C.E., J.A.O., J.S.D., D.A.C. and A.S.M.; formal analysis, J.A.O., J.C.E.,
                                  J.M.N.V., C.A.P. and F.D.l.P.; investigation, C.A.P., A.S., J.C.E., A.S.M. and P.A.C.; resources J.S.D.
                                  and D.A.C.; data curation, J.A.O. and J.C.E.; writing—original draft preparation C.A.P., A.S., J.C.E.
                                  and J.A.O.; writing—review and editing J.M.N.V., F.D.l.P. and C.A.P.; supervision, C.A.P. and A.S.;
                                  visualization, J.A.O. and J.C.E.; project administration, C.A.P., J.S.D. and F.D.l.P.; funding acquisition
                                  J.M.N.V. and F.D.l.P. All authors have read and agreed to the published version of the manuscript.

                                  Funding: This research is part of the International Chair Project on Trustworthy Artificial Intelligence
                                  and Demographic Challenge within the National Strategy for Artificial Intelligence (ENIA), in the
                                  framework of the European Recovery, Transformation and Resilience Plan. Reference: TSI-100933-
                                  2023-0001. This project is funded by the Secretary of State for Digitalization and Artificial Intelligence
                                  and by the European Union (Next Generation).

                                  Institutional Review Board Statement: This study was conducted in accordance with the Declaration
                                  of Helsinki and approved by the Institutional Review Board of Talentum Corporation with internal
                                  code 23INTER-461 (approved on January 2024) for studies involving humans.

                                  Informed Consent Statement: Informed consent was obtained from all subjects involved in
                                  this study.

                                  Data Availability Statement: All the original datasets corresponding to the three case studies are
                                  available in the following repository: https://github.com/Juankidd/toolkitgeai (accessed on 1
                                  November 2024).

                                  Conflicts of Interest: The authors declare that they have no known competing financial interests or
                                  personal relationships that could have influenced the work reported in this paper.

References
1.    Chen, A.; Liu, L.; Zhu, T. Advancing the democratization of generative artificial intelligence in healthcare: A narrative review. J.
      Hosp. Manag. Health Policy 2024, 8, 12. [CrossRef]
2.    Dessimoz, C.; Thomas, P.D. AI and the democratization of knowledge. Sci. Data 2024, 11, 268. [CrossRef] [PubMed]
3.    Rajaram, K.; Tinguely, P.N. Generative artificial intelligence in small and medium enterprises: Navigating its promises and
      challenges. Bus. Horiz. 2024, 67, 629–648. [CrossRef]
4.    Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoriet, J.; Jones, L.; Gomez, A.; Kaiser, L.; Polosukhin, I. Attention is all you need. Adv.
      Neural Inf. Process. Syst. 2017, 30, 11.
5.    Dix, A.; Finlay, J.; Abowd, G.D.; Beale, R. Human-Computer Interaction; Taylor & Francis, Inc.: Philadelphia, PA, USA, 2004.
6.    Xu, W.; Dainoff, M. Enabling human-centered AI: A new junction and shared journey between AI and HCI communities.
      Interactions 2023, 30, 42–47. [CrossRef]
7.    Xu, W.; Gao, Z. Enabling human-centered AI: A methodological perspective. In Proceedings of the 2024 IEEE 4th International
      Conference on Human-Machine Systems (ICHMS), Ontario, ON, Canada, 15–17 May 2024.
Informatics 2025, 12, 10                                                                                                                23 of 25



8.    Capel, T.; Brereton, M. What is Human-Centered about Human-Centered AI? A Map of the Research Landscape. In Proceedings
      of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany, 23–28 April 2023.
9.    Sison, A.J.G.; Daza, M.T.; Gozalo-Brizuela, R.; Garrido-Merchán, E.C. ChatGPT: More Than a “Weapon of Mass Deception” Ethical
      Challenges and Responses from the Human-Centered Artificial Intelligence (HCAI) Perspective. Int. J. Hum. Comput. Interact.
      2023, 40, 4853–4872. [CrossRef]
10.   Casteleiro-Pitrez, J. Generative artificial intelligence image tools among future designers: A usability, user experience, and
      emotional analysis. Digital 2024, 4, 316–332. [CrossRef]
11.   Takaffoli, M.; Li, S.; Mäkelä, V. Generative AI in User Experience Design and Research: How Do UX Practitioners, Teams, and
      Companies Use GenAI in Industry? In Proceedings of the 2024 ACM Designing Interactive Systems Conference, Copenhagen,
      Denmark, 1–5 July 2024.
12.   Peláez, C.; Solano, A.; Nuñez, J.; Castro, D.; Cardona, J.; Duque, J.; Espinosa, J.; Montaño, A.; De la Prieta, F. Designing User Expe-
      rience in the Context of Human-Centered AI and Generative Artificial Intelligence: A Systematic Review. In Proceedings of the
      International Symposium on Distributed Computing and Artificial Intelligence—DCAI 2024, Salamanca, Spain, 26–29 June 2024.
13.   Bingley, W.J.; Curtis, C.; Lockey, S.; Bialkowski, A.; Gillespie, N.; Haslam, S.A.; Worthy, P. Where is the human in human-centered
      AI? Insights from developer priorities and user experiences. Comput. Hum. Behav. 2023, 141, 107617. [CrossRef]
14.   Baabdullah, A.M.; Alalwan, A.A.; Algharabat, R.S.; Metri, B.; Rana, N.P. Virtual agents and flow experience: An empirical
      examination of AI-powered chatbots. Technol. Forecast. Soc. Chang. 2022, 181, 121772. [CrossRef]
15.   Gill, S.S.; Xu, M.; Patros, P.; Wu, H.; Kaur, R.; Kaur, K.; Buyya, R. Transformative effects of ChatGPT on modern education:
      Emerging Era of AI Chatbots. Internet Things Cyber-Phys. Syst. 2024, 4, 19–23. [CrossRef]
16.   Banh, L.; Strobel, G.; Markets, E. Generative artificial intelligence. Electron. Mark. 2023, 33, 63. [CrossRef]
17.   Peruchini, M.; da Silva, G.M.; Teixeira, J.M. Between artificial intelligence and customer experience: A literature review on the
      intersection. Discov. Artif. Intell. 2024, 4, 4. [CrossRef]
18.   York, E. Evaluating ChatGPT: Generative AI in UX Design and Web Development Pedagogy. In Proceedings of the 41st ACM
      International Conference on Design of Communication, Orlando, FL, USA, 26–28 October 2023.
19.   Liu, Y.; Siau, K. Generative Artificial Intelligence and Metaverse: Future of Work, Future of Society, and Future of Humanity. In
      Proceedings of the AI-generated Content, Proceedings of the first International Conference, AIGC 2023, Shanghai, China, 25–26
      August 2023; Springer: Singapore, 2023.
20.   Liu, F.; Zhang, M.; Budiu, R. AI as a UX Assistant. 27 October 2023. Available online: https://www.nngroup.com/articles/ai-
      roles-ux/ (accessed on 21 February 2024).
21.   Qadri, R.; Shelby, R.; Bennett, C.L.; Denton, E. AI’s Regimes of Representation: A Community-centered Study of Text-to-Image
      Models in South Asia. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, Chicago, IL
      USA, 12–15 June 2023.
22.   Wang, X.; Attal, M.I.; Rafiq, U.; Hubner-Benz, S. Turning Large Language Models into AI Assistants for Startups Using
      Prompt Patterns. In Proceedings of the International Conference on Agile Software Development, Copenhagen, Denmark,
      13–17 June 2022.
23.   Shah, C.S.; Mathur, S.; Vishnoi, S.K. Continuance Intention of ChatGPT Use by Students. In Proceedings of the International
      Working Conference on Transfer and Diffusion of IT, Nagpur, India, 15–16 December 2023.
24.   Li, J.; Cao, H.; Lin, L.; Hou, Y.; Zhu, R.; El Ali, A. User experience design professionals’ perceptions of generative artificial intelli-
      gence. In Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 11–16 May 2024.
25.   Kim, P.W. A Framework to Overcome the Dark Side of Generative Artificial Intelligence (GAI) Like ChatGPT in Social Media and
      Education. IEEE Trans. Comput. Soc. Syst. 2023, 11, 5266–5274. [CrossRef]
26.   Asadi, A.R. LLMs in Design Thinking: Autoethnographic Insights and Design Implications. In Proceedings of the 2023 5th World
      Symposium on Software Engineering, Tokyo, Japan, 22–24 September 2023.
27.   Sun, J.; Liao, Q.V.; Agarwal, M.M.M.; Houde, S.; Talamadupula, K.; Weisz, J.D. Investigating explainability of generative AI for
      code through scenario-based design. In Proceedings of the 27th International Conference on Intelligent User Interfaces, Helsinki,
      Finland, 22–25 March 2022.
28.   Oniani, D.; Hilsman, J.; Peng, Y.; Poropatich, R.K.; Pamplin, J.C.; Legault, G.L.; Wang, Y. Adopting and expanding ethical
      principles for generative artificial intelligence from military to healthcare. NPJ Digit. Med. 2023, 6, 225. [CrossRef] [PubMed]
29.   Shin, D.; Ahmad, N. Algorithmic nudge: An approach to designing human-centered generative artificial intelligence. Computer
      2023, 56, 95–99. [CrossRef]
30.   Tlili, A.; Shehata, B.; Adarkwah, M.A.; Bozkurt, A.; Hickey, D.T.; Huang, R.; Agyemang, B. What if the devil is my guardian angel:
      ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. 2023, 10, 15. [CrossRef]
31.   Fisher, J. Centering the Human: Digital Humanism and the Practice of Using Generative AI in the Authoring of Interactive Digital
      Narratives. In Proceedings of the International Conference on Interactive Digital Storytelling, Kobe, Japan, 11–15 November 2023.
Informatics 2025, 12, 10                                                                                                            24 of 25



32.   Mao, Y.; Rafner, J.; Wang, Y.; Sherson, J. A Hybrid Intelligence Approach to Training Generative Design Assistants: Partnership
      Between Human Experts and AI Enhanced Co-Creative Tools. Front. Artif. Intell. Appl. 2023, 368, 108–123.
33.   Guo, M.; Zhang, X.; Zhuang, Y.; Chen, J.; Wang, P.; Gao, Z. Exploring the Intersection of Complex Aesthetics and Generative
      AI for Promoting Cultural Creativity in Rural China after the Post-Pandemic. In AI-Generated Content, Proceedings of the First
      International Conference, AIGC 2023, Shanghai, China, 25–26 August 2023; Springer Nature: Singapore, 2023; pp. 313–331.
34.   Geerts, G. A design science research methodology and its application to accounting information systems research. Int. J. Account.
      Inf. Syst. 2011, 12, 142–151. [CrossRef]
35.   Adikari, S.; McDonald, C.; Campbell, J. A design science framework for designing and assessing user experience. In Proceedings
      of the Human-Computer Interaction, Design and Development Approaches: 14th International Conference, HCI International,
      Orlando, FL, USA, 9–14 July 2011.
36.   Hollander, M.; Wolfe, D. Nonparametric Statistical Methods: Solutions Manual to Accompany; Wiley-Interscience: Hoboken, NJ,
      USA, 1999.
37.   IDEO. IDEO Method Cards: 51 Ways to Inspire Design; William Stout: San Francisco, CA, USA, 2003.
38.   Friedman, B.; Hendry, D. The envisioning cards: A toolkit for catalyzing humanistic and technical imaginations. In Proceedings
      of the SIGCHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 30 April–5 May 2012.
39.   Friedman, B.; Hendry, D. Value Sensitive Design: Shaping Technology with Moral Imagination; MIT Press: Boston, MA, USA, 2019.
40.   Sarsenbayeva, Z.; van Berkel, N.; Hettiachchi, D.; Tag, B.; Velloso, E.; Goncalves, J.; Kostakos, V. Mapping 20 years of accessibility
      research in HCI: A co-word analysis. Int. J. Hum.-Comput. Stud. 2023, 175, 103018. [CrossRef]
41.   Hassan, A. Factors Affecting the Use of ChatGPT in Mass Communication. In Emerging Trends and Innovation in Business and
      Finance; Springer Nature: Singapore, 2023; pp. 671–685.
42.   Brandtzaeg, P.B.; You, Y.; Wang, X.; Lao, Y. “Good” and “Bad” Machine Agency in the Context of Human-AI Communication:
      The Case of ChatGPT. In Proceedings of the International Conference on Human-Computer Interaction, Copenhagen, Denmark,
      23–28 July 2023.
43.   Rana, N.P.; Pillai, R.; Sivathanu, B.; Malik, N. Assessing the nexus of Generative AI adoption, ethical considerations and
      organizational performance. Technovation 2024, 135, 103064. [CrossRef]
44.   Creely, E. The possibilities; limitations, and dangers of generative AI in language learning and literacy practices. In Proceedings
      of the International Graduate Research Symposium 2023, Hanoi, Vietnam, 27–29 October 2023.
45.   Weisz, J.D.; Muller, M.; He, J.; Houde, S. Toward General Design Principles for Generative AI Applications. 13 January 2023.
      Available online: https://arxiv.org/abs/2301.05578 (accessed on 17 February 2024).
46.   Sarraf, S.; Kar, A.K.; Janssen, M. How do system and user characteristics, along with anthropomorphism, impact cognitive
      absorption of chatbots–Introducing SUCCAST through a mixed methods study. Decis. Support Syst. 2024, 178, 114132. [CrossRef]
47.   Perri, L. What’s New in the 2023 Gartner Hype Cycle for Emerging Technologies. 23 August 2023. Available online: https://
      www.gartner.com/en/articles/what-s-new-in-the-2023-gartner-hype-cycle-for-emerging-technologies (accessed on 1 July 2024).
48.   Harley, H. Ideation in Practice: How Effective UX Teams Generate Ideas. 29 October 2017. Available online: https://www.
      nngroup.com/articles/ideation-in-practice/ (accessed on 19 November 2024).
49.   Object Management Group. Essence–Kernel and Language for Software Engineering Methods; OMG: Needham, MA, USA, 2018.
50.   Feng, K.K.; Li, T.W.; Zhang, A.X. Understanding collaborative practices and tools of professional UX practitioners in software
      organizations. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany, 23–28
      April 2023.
51.   Wagemans, J.; Elder, J.H.; Kubovy, M.; Palmer, S.E.; Peterson, M.A.; Singh, M.; von der Heydt, R. A century of Gestalt psychology
      in visual perception: I. Perceptual grouping and figure–ground organization. Psychol. Bull. 2012, 138, 1172–1217. [CrossRef]
      [PubMed]
52.   Johannesson, P.; Perjons, E. An Introduction to Design Science; Springer: Stockholm, Sweden, 2014.
53.   Team, R.C. A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022.
54.   Nunnally, J. Psychometric Theory, 2nd ed.; McGraw-Hill: New York, NY, USA, 1978.
55.   Jolliffe, I.T. Principal component analysis for special types of data. In Principal Component Analysis; Springer: New York, NY, USA,
      2002; pp. 338–372.
56.   Shneiderman, B. Human-Centered AI; Oxford University Press: Oxford, UK, 2022.
57.   Elliot, A.J.; Maier, M.A. Color psychology: Effects of perceiving color on psychological functioning in humans. Annu. Rev. Psychol.
      2014, 65, 95–120. [CrossRef] [PubMed]
58.   Kress, G.; van Leeuwen, T. Colour as a semiotic mode: Notes for a grammar of colour. Vis. Commun. 2002, 1, 343–368. [CrossRef]
59.   Roy, R.; Warren, J.P. Card-based design tools: A review and analysis of 155 card decks for designers and designing. Des. Stud.
      2019, 63, 125–154. [CrossRef]
Informatics 2025, 12, 10                                                                                                       25 of 25



60.   Petri, G.; von Wangenheim, C.G.; Borgatto, A.F. MEEGA+, systematic model to evaluate educational games. In Encyclopedia of
      Computer Graphics and Games; Springer International Publishing: Cham, Switzerland, 2024; pp. 1112–1119.
61.   Minge, M.; Thüring, M. The meCUE questionnaire (2.0): Meeting five basic requirements for lean and standardized UX assessment.
      In Proceedings of the Design, User Experience, and Usability: Theory and Practice: 7th International Conference, DUXU 2018,
      Held as Part of HCI International 2018, Las Vegas, NV, USA, 15–20 July 2018.


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