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It ’ s just distributed computing: Rethinking AI governanceIndia (TRAI · 2025What we now lump under the unitary label “artificial intelligence” is not a single technology, but a highly varied set of machine learning appli­ cations enabled and supported by a globally ubiquitous system of distributed computing. The paper introduces a 4 part conceptual framework for analyzing the structure of that system, which it labels the digital ecosystem. What we now call “AI” is then shown to be a general functionality of distributed computing. "AI” has been present in primitive forms from the origins of digital computing in the 1950s. Three short case studies show that large-scale machine learning applications have been present in the digital ecosystem ever since the rise of the Internet. and provoked the same public policy concerns that we now associate with “AI.” The governance problems of “AI” are really caused by the development of this digital ecosystem, not by LLMs or other recent applications of machine learning. The paper then examines five recent proposals to “govern AI”and maps them to the constituent elements of the digital ecosystem model. This mapping shows that real-world attempts to assert governance authority over AI capabilities requires systemic control of all four elements of the digital ecosystem: data, computing power, networks and software. “Governing AI,” in other words, means total control of distributed computing. A better alternative is to focus governance and regulation upon specific appli­ cations of machine learning. An application-specific approach to governance allows for a more decentralized, freer and more effective method of solving policy conflicts.

UX of AI & PersonalizationResearch Papers

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

A review of AI teaching and learning from 2000 to 2020Davy Tsz Kit Ng, Min Lee et al. · 2022In recent years, with the popularity of AI technologies in our everyday life, research- ers have begun to discuss an emerging term “AI literacy”. However, there is a lack of review to understand how AI teaching and learning (AITL) research looks like over the past two decades to provide the research basis for AI literacy education. To summarize the empirical findings from the literature, this systematic literature review conducts a thematic and content analysis of 49 publications from 2000 to 2020 to pave the way for recent AI literacy education. The related pedagogical mod- els, teaching tools and challenges identified help set the stage for today’s AI literacy. The results show that AITL focused more on computer science education at the uni- versity level before 2021. Teaching AI had not become popular in K-12 classrooms at that time due to a lack of age-appropriate teaching tools for scaffolding support. However, the pedagogies learnt from the review are valuable for educators to reflect how they should develop students’ AI literacy today. Educators have adopted collab- orative project-based learning approaches, featuring activities like software develop- ment, problem-solving, tinkering with robots, and using game elements. However, most of the activities require programming prerequisites and are not ready to scaf- fold students’ AI understandings. With suitable teaching tools and pedagogical sup- port in recent years, teaching AI shifts from technology-oriented to interdisciplinary design. Moreover, global initiatives have started to include AI literacy in the lat- est educational standards and strategic initiatives. These findings provide a research foundation to inform educators and researchers the growth of AI literacy education that can help them to design pedagogical strategies and curricula that use suitable technologies to better prepare students to become responsible educated citizens for today’s growing AI economy.

UX of AI & PersonalizationResearch Papers

Towards Algorithmic Experience: Initial Efforts for Social Media ContextsOscar Alvarado · 2018Algorithms influence most of our daily activities, decisions, and they guide our behaviors. It has been argued that algorithms even have a direct impact on democratic societies. Human - Computer Interaction research needs to develop analytical tools for describing the interaction with, and experience of algorithms. Based on user participatory workshops focused on scrutinizing Facebook’s newsfeed, an algorithm-influenced social media, we propose the concept of Algorithmic Experience (AX) as an analytic framing for making the interaction with and experience of algorithms explicit. Connecting it to design, we articulate five functional categories of AX that are particularly important to cater for in social media: profiling transparency and management, algorithmic awareness and control, and selective algorithmic memory. Author Keywords Algorithms; algorithmic experience; social media; user- centered design; research through design ACM Classification Keywords H.5.2. User Interfaces: User-centered design

UX of AI & PersonalizationResearch Papers

Emotional MachinesJordi Vallverdú · 2019Artificial Intelligence (AI) is the motor that fuels the most profound revolution in the history of humankind. The new era of information society [10], even called "the Fourth Revolution" [8], has produced deep changes into sciences, economies, and societies [9]. This revolution is not only about formal heuristics, or the called "algorithmic society", but also is explicitly related to the creation of strong interactions between humans and machines. Consequently, these machines must be able to deal with the most intrinsic feature of human nature: emotions. If emotions had been historically considered as disturbing elements of human rationality, the neurological revolution and new experimental tools such as magnetic scanning of the brain (with great success for fMRI) made possible a complete Copernican turn into the evaluation of the role of emotions in cognitive frameworks [13,14]. This process led experts in natural and artificial cognition to consider the necessity of including emotions into their models [37]. Affective computing or social robotics [7,44] increased not only the necessity of studies about how to improve the emotional interaction between humans and machines [57,59] but also how to design cognitive architectures which included biomimetic elements related with emo-

Emotional DesignResearch Papers

Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to DesignQian Yang · 2020Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many chal­ lenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to unde­ sired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended conse­ quences. What makes AI different? What makes human-AI interaction appear particularly difficult to design? This paper investigates these questions. We synthesize prior research, our own design and research experience, and our observations when teaching human-AI interaction. We identify two sources of AI’s distinctive design challenges: 1) uncertainty surround­ ing AI’s capabilities, 2) AI’s output complexity, spanning from simple to adaptive complex. We identify four levels of AI sys­ tems. On each level, designers encounter a different subset of the design challenges. We demonstrate how these findings reveal new insights for designers, researchers, and design tool makers in productively addressing the challenges of human-AI interaction going forward. Author Keywords User experience, artificial intelligence, sketching, prototyping. CCS Concepts •Human-centered computing → Human computer inter­ action (HCI); Interaction design process and methods;

HCI & Adaptive InterfacesResearch PapersUX of AI & Personalization