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The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge WorkersAdvait Sarkar · 2025The rise of Generative AI (GenAI) in knowledge workflows raises questions about its impact on critical thinking skills and practices. We survey 319 knowledge workers to investigate 1) when and how they perceive the enaction of critical thinking when using GenAI, and 2) when and why GenAI affects their effort to do so. Participants shared 936 first-hand examples of using GenAI in work tasks. Quantitatively, when considering both task- and user-specific factors, a user’s task-specific self-confidence and confidence in GenAI are predictive of whether critical thinking is enacted and the effort of doing so in GenAI-assisted tasks. Specifically, higher confidence in GenAI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking. Qualitatively, GenAI shifts the nature of critical thinking toward information verification, response integration, and task stewardship. Our insights reveal new design challenges and opportunities for developing GenAI tools for knowledge work. CCS Concepts • Human-centered computing →Empirical studies in HCI.

UX of AI & PersonalizationResearch Papers

Unveiling the landscape of generative artificial intelligence in education: a comprehensive taxonomy of applications, challenges, and future prospectsInformation Technologies · 2024The rapid advancement of Generative Artificial Intelligence (GenAI) models, particularly ChatGPT, has sparked widespread discussion among educators and researchers regarding their potential implications for education. This study presents a comprehensive taxonomy of GenAI in academia and education, encompassing a wide range of applications, challenges, ethical considerations, and future prospects. Drawing on a scoping review of 453 articles, including the 50 most cited works throughout 2023, the taxonomy provides a state-of-the-art analysis of the current landscape of GenAI in education. The taxonomy offers a theoretical framework that aligns with the current discourse in GenAI and education, providing a critical evaluation of the existing literature and proposing innovative perspectives and solu- tions. The practical implications of the taxonomy for educators, researchers, and policymakers are highlighted, emphasizing the need for ethical considerations and informed policies to maximize the benefits of GenAI while minimizing its risks and negative impacts.

UX of AI & PersonalizationResearch Papers

Analyzing behavioral intentions toward Generative Artificial Intelligence: the case of ChatGPTDongyan Nan · 2024Generative artificial intelligence (AI) is an innovative AI technology that has garnered considerable attention worldwide. This study aimed to facilitate the development of such technologies by examining the factors affecting individuals’ inten­ tions toward generative AI (e.g., ChatGPT). Concretely, we developed a causal model by extending the expectation confirmation model with information system success theory, privacy concerns, and perceived innovativeness. Then, we tested the model by analyzing survey-based data from 252 Korean ChatGPT users. As a result, we found that anteced­ ent variables -information quality, system quality, privacy concerns, and perceived innovativeness- play notable roles in affecting users’ intentions to continually use and recommend generative AI ChatGPT. Overall, the current research is one of the first attempts to track the variables influencing individuals’ intentions to continually use and recommend in the context of generative AI ChatGPT.

UX of AI & PersonalizationResearch Papers

Application of Artificial Intelligence in Interactive UI DesignSha Liang · 2024The purpose of this study is to deeply explore the influence of artificial intelligence (AI) application in interactive User Interface (UI) design, and evaluate its effect in conceptual design, prototype design and final design through experiments and analysis. By introducing generative design tools, machine learning algorithms and sentiment analysis technology, this study evaluates the role of AI in improving design efficiency, personalizing user experience, increasing creativity and enhancing emotional resonance of users. In the evaluation of design effect, it is found that the effect score is significantly improved after the introduction of AI in each design stage. The design scheme generated by the designer through AI is more in line with the needs of users, the design accuracy is improved, and the creativity is also increased compared with the traditional design. In terms of personalized user experience, we use machine learning algorithm to adjust interface elements according to user behavior to provide a more personalized user experience. The experimental results show that personalized design can significantly improve user satisfaction and interaction efficiency. Through the emotional analysis technology, the language and emotional tone of the design are adjusted, and the emotional resonance of users is improved. Designers can better express their emotions and make the design more in line with the emotional needs of users. On the whole, this study provides an in-depth empirical study on the application of AI in UI design, and provides beneficial enlightenment for future intelligent design and user experience research.

UX of AI & PersonalizationResearch Papers

Human-Machine Co-Creation in the Rise of AIWai Lok Woo · 2020A note from Melanie Ooi: In this issue, Wai Lok Woo shares with us some insights into how we will evolve with the rise of artificial intelligence. It is an extremely engaging column that applies to almost all of our work in instrumentation and measurement today. I hope you will enjoy reading this as much as I did.

UX of AI & PersonalizationResearch Papers

Identifying the Intersections: User Experience + Research Scientist Collaboration in a Generative Machine Learning InterfaceClaire Kayacik, Sherol Chen et al. · 2019Creative generative machine learning interfaces are stronger when multiple actors bearing different points of view actively contribute to them. User experience (UX) research and design involvement in the creation of machine learning (ML) models help ML research scientists to more effectively identify human needs that ML models will fulfill. The People and AI Research (PAIR) group within Google developed a novel program method in which UXers are embedded into an ML research group for three months to provide a human-centered perspective on the creation of ML models. The first full-time cohort of UXers were embedded in a team of ML research scientists focused on deep generative models to assist in music composition. Here, we discuss the structure and goals of the program, challenges we faced during execution, and insights gained as a result of the process. We offer practical suggestions Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). CHI’19 Extended Abstracts, May 4–9, 2019, Glasgow, Scotland UK © 2019 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-5971-9/19/05. https://doi.org/10.1145/3290607.3299059 CHI 2019 Case Study CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK CS09, Page 1 for how to foster communication between UX and ML research teams and recommended UX design processes for building creative generative machine learning interfaces. CCS CONCEPTS • Human-centered computing; • Human computer interaction; • Interaction paradigms; • Collaborative interaction;

UX of AI & PersonalizationResearch Papers

Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI LiteratureShuning Zhang, Hui Wang et al. · 2025As Artificial Intelligence (AI) increasingly becomes an active collaborator in co-creation, understanding the distribution and dynamic of agency is paramount. The Human-Computer Interaction (HCI) perspective is crucial for this analysis, as it uniquely reveals the interaction dynamics and specific control mechanisms that dictate how agency manifests in practice. Despite this importance, a systematic synthesis mapping agency configurations and control mechanisms within the HCI/CSCW literature is lacking. Addressing this gap, we reviewed 134 papers from top-tier HCI/CSCW venues (e.g., CHI, UIST, CSCW) over the past 20 years. This review yields four primary contributions: (1) an integrated theoretical framework structuring agency patterns, control mechanisms, and interaction contexts, (2) a comprehensive operational catalog of control mechanisms detailing how agency is implemented; (3) an actionable cross-context map linking agency configurations to diverse co-creative practices; and (4) grounded implications and guidance for future CSCW research and the design of co-creative systems, addressing aspects like trust and ethics.

HCI & Adaptive InterfacesResearch Papers