Human-Centered AI

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11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

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Latest papers 108

May 28, 2026cs.AI

It`s All About Speed: AI`s Impact on Workflow in Music Production

In this paper, we present the results of an ethnographic study into the impact of AI and automated tools on music production workflow. Focusing specifically on professional participants who identified as recording engineers, mixers, and producers, we discuss their usage of common AI and automated software, as well as their sentiments on the proliferation of these tools. We discuss tensions that may be created between users and automated tools in key areas such as the need for speed and efficiency, controllability, and maintaining creative agency, and how these tensions may be alleviated through tool design.

Finn McClellan, Fabio Morreale

Waipapa Taumata Rau - University of Auckland, Auckland (Aotearoa - New Zealand) · Sony AI, Barcelona (Spain)

Workflow Automation Human-Centered AI Human-AI Collaboration

May 27, 2026cs.AI

The Illusion of Opting in AI-Mediated Consequential Decisions

Drawing on Ullmann-Margalit's concept of opting (transformative, irrevocable, and shadowed by foreclosed alternatives), we show that current AI systems raise a profound ethical problem that existing AI ethics has not fully captured: the illusion of opting, in which persons and groups encounter the deceptive appearance of meaningful consequential choice while the agency needed to become genuinely capable of choosing is weakened. Against approaches that treat AI primarily as an optimizer of already given ends, we argue that AI systems should be evaluated by whether they protect and cultivate meta-capacity against the illusion of opting: the socially and institutionally scaffolded agentive capacity through which means and ends can be formed, contested, revised, and owned. This reframing is especially urgent for disadvantaged populations, who are least able to absorb the costs of the illusion of opting when AI-mediated pathways misdirect behavior and action. We propose three normative imperatives for AI-mediated consequential decisions: existential honesty, which acknowledges the limits of prediction; ecological rationality, which situates guidance within heterogeneous lived ecologies; and counterfactual reparation, which acknowledges and repairs foreclosed alternatives when AI-mediated decision-making pathways fail.

Eugene Yu Ji

University of Waterloo · Mila – Quebec AI Institute

Human-Centered AI Human-AI Decision Making Responsible AI

May 26, 2026cs.AI

You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention

A central puzzle for the behavioural sciences and for human-facing artificial intelligence is the persistence of within-person variability. The same individual, presented with the same observable input, produces different outcomes on different occasions, and different individuals produce divergent outcomes that no observable covariate fully predicts. We argue that this variability belongs in the dynamic latent state of the person, and that human outcomes are controllable in a precise and operational sense through interventions that target the state and its weighting at the moment a decision is being formed. We define a state as the time-indexed weighting vector over the dimensions that govern how an individual's biology, physiology, and neuropsychology process the next event into a decision and an outcome. The relationship between state, decision, and outcome is causal rather than correlational. The weighting vector is dynamic at sub-daily timescales. The conscious channel through which outcomes are reportable is a narrow attentional bottleneck whose contents are themselves state-dependent. Taken together, these claims imply that the outcome of a given event is controllable, conditionally, on the state-trajectory at the time of intervention. We motivate the framework with six strands of established evidence (causal inference, predictive processing, allostasis, attentional bottleneck, chronobiology, computational psychiatry) and a 24-month observational base from a deployed behavioural platform spanning more than 200,000 consented users across four occupational personas (research period 2023 to 2026). We derive seven testable predictions, list six operational requirements for state-aware systems, and discuss implications for digital health, education, AI personalisation, and personal agency.

Suraj Biswas, Saurav Gupta, Pritam Mukherjee

Independent Researchers in Behavioural Modelling, Causal Inference, and Genomics · Assessli · Dots-In

Human-Centered AI Causal Intervention

May 24, 2026cs.HC

Intent Signal Theory: A Computational Framework for Intent-State Control in Human-AI Interaction

Current AI interaction models treat the prompt as the primary object of exchange, omitting a critical layer: the user's latent source intent, the goal state preceding and motivating the prompt. Here we introduce Intent Signal Theory (IST), a computational framework that formalises this missing intent layer. IST distinguishes four objects routinely conflated: latent source intent (I*), observable intent proxy (I-hat), encoded carrier (P), and model output (O). It formalises dimensional weights, encoding masks, structural and fidelity recovery scores, and public-private intent decomposition. The Theorem of Irreversible Intent Loss establishes that private intent absent from the carrier cannot be recovered beyond generic substitution. Evidence from four companion studies spanning six LLMs, three languages and three task domains shows structural-fidelity splits, human-validated metric dissociation, and weight-tolerance plateaus consistent with IST's predictions. IST reframes prompt engineering as intent-protocol design and identifies a computational layer that current AI systems lack.

Gang Peng

Huizhou Lateni AI Technology Co., Ltd., Huizhou, China · Huizhou University, Huizhou, China

Human-Centered AI User Modeling Prompt Engineering

May 22, 2026cs.HC

CultivAgents: Cultivating Relationship-Centered Multi-Agent Systems for Personalized Gardening

Gardening is critical to support well-being, cultural continuity, and food autonomy, yet existing digital tools often provide generic advice that overlooks gardeners' skills, local ecologies, seasons, and cultural contexts. We introduce CultivAgents, a relationship-centered multi-agent system for personalized, socio-culturally grounded gardening support. Grounded in ethics of care, CultivAgents coordinates multiple specialized agents: an Experience Agent that adapts guidance to users' skill levels, an Environmental Agent that grounds advice in local and seasonal conditions, and an Ethnobotanical Agent that connects plants to cultural knowledge and histories. We evaluated CultivAgents through a three-phase mixed-methods study with domain experts (n=3), HCI researchers (n=7), and community gardeners (n=5), analyzing expert feedback, pre/post surveys, and participatory design activities. Results suggest that CultivAgents helped gardeners translate interest into situated action: community gardeners reported increased confidence (3.00 to 3.60), motivation (4.00 to 4.40), and trust in acting on AI advice (3.20 to 4.00). Participants valued hyperlocal ecological guidance and complementary agent perspectives, while also identifying limits in cultural specificity, ecological grounding, and agent coordination. The work advances relationship-centered AI, offering design implications for multi-agent systems that support food sovereignty, community resilience, and cultural preservation.

Yiyang Wang, Moeiini Reilly, Britney Johnson +3

Georgia Institute of Technology Atlanta, Georgia, USA · Massachusetts Institute of Technology Cambridge, Massachusetts, USA

Human-Centered AI Multi-Agent Systems

May 20, 2026cs.HC

Addressing the Synergy Gap: The Six Elements of the Design Space

AI is now embedded in healthcare, finance, policy, and many other domains, yet genuine human-AI synergy - combined performance that exceeds what either party achieves alone - is uncommon. Meta-analyses show that AI assistance tends to improve human performance compared to working alone, but studies finding true synergy are scarce. We call this persistent shortfall the synergy gap. Most current work treats human-AI combination as an engineering problem and concentrates on interpretability, trust calibration, or interface design. These matter, but they cover only part of what determines whether combination works. Closing the synergy gap, we argue, requires explicit engagement with a wider design space. We map that space through six interconnected elements: sociotechnical context, decision-making frameworks, human decision participants, AI capabilities, interaction, and holistic evaluation. For each element, we describe what it covers, how it shapes the others in practice, and what it implies for design. The result is a shared vocabulary for practitioners building hybrid systems, an analytical lens for researchers studying combination patterns, and a starting point for evaluators interested in the full quality of human-AI decision-making rather than accuracy alone.

Tommaso Turchi, Ben Wilson, Matt Roach +2

Department of Computer Science University of Pisa Pisa, Italy · Computational Foundry Swansea University Swansea, United Kingdom · Cardiff Metropolitan University, United Kingdom & Computational Foundry, Swansea University +1

Human-in-the-Loop Evaluation Human-Centered AI Human-AI Decision Making Human-AI Collaboration

May 20, 2026cs.HC

Designing Conversations with the Dead: How People Engage with Generative Ghosts

We examine how people experience two choices in the design of generative ghosts, AI systems that are trained on data of the dead: representation, where an AI speaks about a deceased person in the third person, and reincarnation, where the AI speaks as the deceased in the first person. Through a qualitative user study with 16 participants, we explore how each shaped authenticity, affect, and risk. Reincarnation was preferred for its immediacy, but participants shared fears of over-reliance. Representation was preferred for engaging with memory over conversational presence, though participants often ignored this distinction, engaging in dialogue despite third-person framing. Across both modes, participants privileged affective resonance over factual fidelity. We conclude by showing how factors such as tone, language, and conversational rhythm -- factors unique to the user's memory of the deceased -- shape interactions with generative ghosts, and argue that those interactions are always collaborative.

Jack Manning, Daniel Sullivan, Dylan Thomas Doyle +2

University of Colorado Boulder, USA

Human-AI Interaction Human-Centered AI Conversational Agents Human-AI Collaboration

May 19, 2026cs.HC

Zhinong AI: A Design-Science Study of an AI-Enabled Agricultural Decision-Support Platform for Smallholder Production

Artificial intelligence is increasingly moving from single-purpose agricultural recognition tools toward integrated decision-support systems that connect information access, diagnosis, task execution and post-action feedback. This paper presents a design-science case study of the Zhinong AI Agricultural Decision Platform, a farmer-facing system that integrates agricultural information push services, natural-language question answering, image-based crop disease diagnosis, plot and farming-calendar management, workflow orchestration, a Hainan Free Trade Port agricultural service zone and an age-friendly care mode. Based on public project materials, policy context and prior research on smart agriculture, machine learning and design science, the paper constructs a layered system architecture and a closed-loop decision process summarized as sensing, analysis, planning, execution and feedback. It further proposes a function-pain-point mapping matrix, an evaluation indicator system and a governance framework covering data provenance, model risk, expert review, privacy and adoption risk. The study does not claim measured field performance because production logs, controlled user studies and expert-labeled local image datasets were not available at the time of writing. Instead, the contribution is a structured research framework for transforming an AI agricultural prototype into an empirically testable, accountable and localized decision-support infrastructure for smallholder production.

Zhaoyang Li, Jiaqi Liu, Ruijie Zhang

University of Sanya · Hebei International Studies University

AI-Assisted Decision Making Human-Centered AI AI Governance

May 19, 2026cs.HC

The Accessibility Capability Boundary: Operational Limits and Expansion Potential of AI-Generated Browser-Native Accessibility Systems

As large language models (LLMs) demonstrate increasing competence in synthesizing functional user interfaces, a fundamental question emerges in accessibility computing: \textit{how far can AI-driven accessibility systems go?} This paper introduces the \textit{Accessibility Capability Boundary} (ACB), a formal framework for reasoning about the operational limits and expansion potential of autonomous accessibility systems, and grounds this theory in a real-world systems artifact. We model accessibility not as a binary compliance property but as a dynamic, multidimensional capability space constrained by measurable variables including deployment latency, cognitive load, infrastructure dependency, offline persistence, interaction complexity, and adaptability. We argue that AI-generated, browser-native systems constructed as single-file HTML artifacts leveraging standard browser APIs may dramatically shift the ACB outward by reducing deployment friction to near-zero and enabling rapid, context-specific interface adaptation. We ground our theoretical framework in the analysis of two real-world exploratory prototypes. The first is an AI-generated browser-native accessibility interface deployed for a blind user in Nepal. The second is a fully functional, open-source webcam alignment assistant for visually impaired users, serving as a concrete systems artifact. Through formal definitions, propositions, and a comparative evaluation matrix, we characterize the regions of the accessibility capability space that such systems can and cannot reach. We further identify remaining computational, infrastructural, and verification constraints that constitute the hard boundaries of this paradigm. This work contributes a theoretical foundation for understanding the scalable limits of autonomous accessibility computing and proposes a research agenda for future work in accessibility-aware AI systems.

Rizwan Jahangir, Daisuke Ishii

NUST Business School, NUST, Islamabad, Pakistan · Kiara Inc., Tokyo, Japan

Human-Centered AI

May 16, 2026cs.AI

Voices in the Loop: Mapping Participatory AI

Participatory approaches to artificial intelligence are increasingly documented across public, civic, and humanitarian settings, but evidence about how participation is organized remains fragmented. This paper reports on the construction of an open repository and interactive atlas of participatory AI initiatives, using records harmonized from Maga~na and Shilton's Trustworthy AI corpus, and additional audited cases from research and practice. We contribute three elements. First, we specify a reproducible protocol for discovery, vetting, harmonization, geocoding, provenance tracking, and release-based publication of participatory AI records. Second, we report corpus-level patterns in geography, participation tiers, lifecycle loci, organizational form, verification status, and remaining documentation gaps. Documented initiatives remain concentrated in a small number of countries, while participation is most often coded at problem formulation, evaluation, and governance rather than model development or training. Third, we show how the atlas operationalizes a design and governance framework for participatory-by-default AI infrastructures through versioned releases, record-linked issue and annotation channels, schema feedback workflows, and redaction or restricted-disclosure requests. The atlas is intended to support comparative research, policy learning, and community scrutiny through a living inventory that can be updated, contested, and reused.

Rashid Mushkani

Right to AI · Mila – Québec AI Institute, Canada

Human-Centered AI AI Governance

May 15, 2026cs.CY

Will AI Agents Free Us From Meaningless Work? A Human-Centered Analysis

Some claim that AI agents will free workers from the boring parts of their jobs, yet little is known about how workers themselves identify which tasks should be automated. Prior research focuses on occupations, overlooking that workers experience varying levels of meaning across tasks within the same role. We address this gap with a task-level analysis grounded in Graeber's theory of bullshit jobs. Using ratings from 202 workers on 171 workplace tasks, we (1) validate a five-item scale of perceived bullshitness, (2) show that perceived bullshitness strongly predicts desire for AI delegation, and (3) find that such tasks are also seen as requiring less human oversight. Together, these findings suggest that tasks perceived as bullshit are natural candidates for AI delegation, aligning worker preferences with perceived feasibility.

Davide Ghia, Jaspreet Ranjit, Tania Cerquitelli +1

Politecnico di Torino · Italy · University of Southern California +3

Human-Centered AI Human Oversight of AI AI and Labor

May 14, 2026cs.HC

Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News

As generative AI becomes increasingly integrated into journalism, designing effective AI-use disclosures that inform readers without imposing unnecessary burden is a key challenge. While prior research has primarily focused on trust and credibility, the impact of disclosures on readers' attentional and cognitive load remains underexplored. To address this gap, we conducted a

3\times2\times2

mixed factorial study manipulating the level of AI-use disclosure detail (none, one-line, detailed), news type (politics, lifestyle), and role of AI (editing, partial content generation), measuring load via NASA-TLX and eye-tracking. Our results reveal a significant attentional cost: one-line disclosures resulted in significantly higher fixation durations and saccade counts, particularly for AI-edited content. Detailed disclosures did not impose additional burden. Drawing on Information-Gap Theory, we argue that brief labels may trigger increased visual scrutiny by alerting readers to AI use without providing enough information. NASA-TLX scores and pupil diameter showed no significant differences across conditions, suggesting that AI-use disclosures do not impose cognitive burden regardless of the detail level. Interview insights contextualize these findings and reveal a strong preference for detailed or ``detail-on-demand'' designs. Our findings inform the design of gaze-informed adaptive disclosure interfaces that dynamically adjust transparency levels based on readers' attentional patterns and news context.

Pooja Prajod, Hannes Cools, Thomas Röggla +2

Centrum Wiskunde & Informatica Amsterdam, The Netherlands · University of Amsterdam Amsterdam, The Netherlands · TU Delft +1

Visual Attention Human-Centered AI

May 14, 2026cs.AI

Metis AI: The Overlooked Middle Zone Between AI-Native and World-Movers

The dominant discourse on AI limitations frames the boundary of AI capability as a divide between digital tasks (where AI excels) and physical tasks (where embodiment is required). We argue this framing misses the most consequential boundary: the one within digital tasks. We identify a class of tasks we call Metis AI, named for the Greek concept of metis (practical, contextual knowledge), that are performed entirely on computers yet resist reliable AI automation. These tasks are not computationally intractable; they are institutionally, socially, and normatively entangled in ways that defeat algorithmic approaches. We distinguish constitutive metis (knowledge destroyed by the act of formalization) from operational metis (system-specific familiarity that automation can progressively absorb), and propose five structural characteristics that define the Metis AI zone: consequential irreversibility, relational irreducibility, normative open texture, adversarial co-evolution, and accountability anchoring. We ground each in established theory from across the social sciences, philosophy, and humanitarian practice, argue that these characteristics are properties of the tasks themselves rather than limitations of current models, and show that the appropriate design response is not better automation but centaur architectures in which humans lead and AI supports.

Xiang Li

Massachusetts General Hospital

Human-Centered AI Human Oversight of AI Human-AI Collaboration

May 13, 2026cs.AI

Position: Assistive Agents Need Accessibility Alignment

Assistive agents for Blind and Visually Impaired (BVI) users require accessibility alignment as a first-class design objective. Despite rapid progress in agentic AI, most systems are designed and evaluated under assumptions of sighted interaction, low-cost verification, and tolerable trial-and-error, leading to systematic failures in assistive scenarios that cannot be resolved by model scaling or post-hoc interface adaptations alone. Drawing on an analysis of 778 assistance task instances from prior work, we show that current agentic AI remain prone to failure in assistive scenarios due to mismatches between sighted-user design assumptions and the verification, risk, and interaction constraints faced by BVI users. We argue that accessibility should be treated as an alignment problem rather than a peripheral usability concern. To this end, we introduce accessibility alignment and propose a lifecycle-oriented design pipeline for accessibility-aligned assistive agents, spanning user research, system design, deployment and post-deployment iteration. We conclude that BVI-centered assistive tasks provide a critical stress test for agentic AI and motivate a broader shift toward inclusive agent design.

Jie Hu, Changyuan Yan, Yu Zheng +2

School of Artificial Intelligence and Robotics, Hunan University, Changsha, China

AI Alignment Human-Centered AI Agentic AI

May 13, 2026cs.HC

Beyond Anthropomorphism: Exploring the Roles of Perceived Non-humanity and Structural Similarity in Deep Self-Disclosure Toward Generative AI

This study investigates deep self-disclosure toward generative AI by examining perceived non-humanity and structural similarity as psychological factors beyond anthropomorphism. Perceived non-humanity may reduce evaluation apprehension, whereas structural similarity refers to the perceived logical alignment between a user's thinking and AI responses. Using cross-sectional survey data from 2,400 participants collected in 2025, this study analyzed associations with both the occurrence and depth of self-disclosure. Logistic regression indicated that the group high in both perceptions (Segment D) showed a significantly higher likelihood of disclosure than the baseline group (Segment A; OR = 11.35). ANOVA further showed significant between-group differences in disclosure depth. The findings suggest that trust-related behavior in deep self-disclosure may involve factors other than anthropomorphic perception. Because the study is exploratory and based on self-reported survey data, the results should be interpreted as associative rather than causal, and future longitudinal or experimental research is needed.

Satoru Shibuya

Graduate School of Business and Finance, Waseda University

Human-AI Interaction Human-Centered AI Trust in AI

May 13, 2026cs.HC

"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive personalized information about their bodies and mental states. However, there is currently little research on how these predictive AI features and explanations affect users' lived experiences. This paper examines human-AI entanglement in MCTAs through 14 semi-structured user interviews and a group autoethnography. These methods uncover the processes leading to this phenomenon. Our results reveal that: (1) users understand their lived experiences in light of AI predictions, although these predictions can be faulty due to imperfect logging practices, (2) the user interface features and AI explanations do not support awareness or critical engagement with this entanglement and meaning-making, and (3) non-normative MCTA users report a sense of isolation in this entangled interaction. Based on our findings, we propose design implications for predictive AI features and explanations.

Wendy Zhou, Pelin Karaturhan, Alexandra Weilenmann +1

Department of Computer Science and Engineering, Chalmers University of Technology University of Gothenburg Gothenburg, Sweden · IT University of Copenhagen Copenhagen, Denmark · Department of Applied Information Technology, University of Gothenburg Gothenburg, Sweden

Explainable Artificial Intelligence Human-AI Interaction Human-Centered AI

May 11, 2026cs.AI

Positive Alignment: Artificial Intelligence for Human Flourishing

Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete. What we call Positive Alignment is the development of AI systems that (i) actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way while (ii) remaining safe and cooperative. It is a distinct and necessary agenda within AI alignment research. We argue that several existing failures of alignment (e.g., engagement hacking, loss of human autonomy, failures in truth-seeking, low epistemic humility, error correction, lack of diverse viewpoints, and being primarily reactive rather than proactive) may be better addressed through positive alignment, including cultivating virtues and maximizing human flourishing. We highlight a range of challenges, open questions, and technical directions (e.g., data filtering and upsampling, pre- and post-training, evaluations, collaborative value collection) for different phases of the LLM and agents lifecycle. We end with design principles for promoting disagreement and decentralization through contextual grounding, community customization, continual adaptation, and polycentric governance; that is, many legitimate centers of oversight rather than one institutional or moral chokepoint.

Ruben Laukkonen, Seb Krier, Chloé Bakalar +13

Department of Psychiatry, University of Oxford · 2Flourishing Intelligence Program, Centre for Eudaimonia and Human Flourishing,2026 Linacre College, University of Oxford · 4LIFE +10

LLM Alignment AI Alignment Human-Centered AI AI Governance Pluralistic Alignment Value Alignment

May 8, 2026cs.HC

Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios

AI measurement science has a wide variety of methodologies and measurements for comparing AI systems, resulting in what often appear to be "apples-to-oranges" comparisons across AI evaluations. To move toward "apples-to-apples" comparisons in real-world AI evaluations, this work advocates for methodological transparency in evaluation scenarios, operational grounding, and human-centered design (HCD) principles. We propose a repeatable process for transforming high-level use cases to detailed scenarios by eliciting use cases from subject matter experts (SMEs) via a structured AI Use Case Worksheet with six key elements: use case, sector, user (direct and indirect), intended outcomes, expected impacts (positive and negative), and KPIs and metrics. We demonstrate utility of the worksheet and process in the U.S. financial services sector. This paper reports on example high-level AI use cases identified by financial services sector SMEs: cyber defense enablement, developer productivity, financial crime aggregation, suspicious activity report (SAR) filing, credit memo generation, and internal call center support. These AI use cases provided are illustrative of the process and not exhaustive. Central to our work is a three-stage expansion pipeline combining LLM prompting with human reviews to generate 107 scenarios from those use cases elicited from SMEs. This process integrates iterative human reviews at every juncture to ensure operational grounding: for scenario titles and descriptions; for core scenario elements like users, benefits and risks, and metrics; and for scenario narratives and evaluation objectives. Human checkpoints ensure scenarios remain reflective of real-world usage and human needs. We describe a validation rubric to assess scenario quality. By defining key scenario components, this work supports a more consistent and meaningful paradigm for human-centered AI evaluations.

Yee-Yin Choong, Kristen Greene, Alice Qian +6

National Institute of Standards and Technollogy · Gaithersburg, MD, USA · Carnegie Mellon University +1

Human-Centered AI

May 7, 2026cs.CL

Reflections and New Directions for Human-Centered Large Language Models

Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and science. With this rise in global influence comes greater urgency to build, evaluate, and deploy these systems in a manner that prioritizes not only technical capabilities but also human priorities. This work presents a framework for developing Human-Centered Large Language Models (HCLLMs), which integrates perspectives from Natural Language Processing (NLP), Human-Computer Interaction (HCI), and responsible AI. Considering the ethics, economics, and technical objectives of language modeling, we argue that model developers need to address human concerns, preferences, values, and goals, not only during a cursory post-training stage, but rather with rigor and care at every stage of the pipeline. This paper offers human-centered insights and recommendations for developers at each stage, from system design to data sourcing, model training, evaluation, and responsible deployment. Then we conclude with a case study, applying these insights to understand the future of work with HCLLMs.

Caleb Ziems, Dora Zhao, Rose E. Wang +55

LLM Alignment Human-Centered AI

May 7, 2026cs.LG

Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility

Generative AI systems achieve impressive performance on standard benchmarks yet fail to deliver real-world utility, a disconnect we identify across 28 deployment cases spanning education, healthcare, software engineering, and law. We argue that this benchmark utility gap arises from three recurring failures in evaluation practice: proxy displacement, temporal collapse, and distributional concealment. Motivated by these observations, we argue that generative AI evaluation requires a paradigm shift from static benchmark-centered transparency toward stakeholder, goal, and context-conditioned utility transparency grounded in human outcome trajectories. Existing evaluations primarily characterize properties of model outputs, while deployment success depends on whether interaction with AI improves stakeholders' ability to achieve their goals over time. The missing construct is therefore utility: the change in a stakeholder's capability induced through sustained interaction with an AI system within a deployment context. To operationalize this perspective, we propose SCU-GenEval, a four-stage evaluation framework consisting of stakeholder-goal mapping, construct-indicator specification, mechanism modeling, and longitudinal utility measurement. To make these stages practically deployable, we introduce three supporting instruments: structured deployment protocols, context-conditioned user simulators, and persona- and goal-conditioned proxy metrics. We conclude with domain-specific calls to action, arguing that progress in generative AI must be evaluated through measurable improvements in human outcomes rather than benchmark performance alone.

Ishani Mondal, Shweta Bhardwaj

University of Maryland, College Park, USA

Human-Centered AI Generative AI Evaluation

May 6, 2026cs.CY

Guidelines for Designing AI Technologies to Support Adult Learning

AI-powered educational technologies have demonstrated measurable benefits for learners, but their design and evaluation have largely centered on K-12 contexts. As a result, many AI-supported learning systems remain poorly aligned with the needs, constraints, and goals of adult learners. To better understand how AI systems function in adult education, this paper examines the deployment of several AI learning technologies developed within a multidisciplinary, national research institute in the United States focused on adult learning and online education. Drawing on longitudinal deployment data, we conducted a reflexive thematic analysis to identify recurring challenges and design considerations across systems. These insights were synthesized into a set of 19 design guidelines intended to inform future AI-supported adult learning technologies. We demonstrate the utility of these guidelines through a heuristic evaluation of the deployed systems. Lastly, we present a guideline exploration tool that aids in the ideation of technologies by connecting the guidelines to stakeholder statements surfaced in the analysis process.

Jennifer M. Reddig, Glen R. Smith, Sanaz Ahmadzadeh Siyahrood +16

Georgia Institute of Technology · Vanderbilt University · Georgia State University +1

Educational Technology AI in Education Human-Centered AI

May 5, 2026cs.HC

Deco: Extending Cherished Physical Objects into AI Companion Agents through Dual Embodiment

Physical objects (e.g., plush toys) can transcend materiality to become emotional anchors and provide companionship. However, these bonds remain one-sided because most physical objects cannot reciprocate. AI companions offer responsiveness and personalization, but typically entail building bonds from scratch. We investigated how AI companions might inherit and extend users' existing bonds with physical objects. A formative study (N=9) informed four design principles (Faithful Identity, Calibrated Agency, Ambient Presence, Reciprocal Memory), shaping our Dual-Embodiment Companion Framework. We instantiated it as Deco to create digital embodiments of physical companions. In a within-subjects lab study (N=25), Deco was rated higher than a personalized digital-only companion on six companion-related measures (all p<.01). A subsequent seven-day field deployment (N=17) showed sustained engagement, higher post-deployment well-being (p=.040), and three key relational patterns: digital activities retroactively vitalized physical objects, bond deepening centered on emotional engagement depth, and participants sustained bonds while navigating companions' AI nature. Dual embodiment offers a promising framework for revitalizing physical objects with AI agents.

Zhihan Jiang, Mengyuan Millie Wu, Ruishi Zou +14

Columbia University, United States · University of Notre Dame, United States

Human-AI Interaction Human-Centered AI Human-Object Interaction

May 3, 2026cs.HC

Cripping AI: Reimagining AI Through Lived Disability Experiences

Drawing on crip theory, this paper proposes cripping AI as a guiding framework to center lived disability experiences in AI research and development. Moving beyond calls to make AI "accessible" to people with disabilities, cripping AI seeks to: (1) reveal and dismantle ableist assumptions embedded in how AI is imagined, designed, and evaluated; (2) center disabled ways of knowing (i.e., cripistemologies); (3) respect disabled labor in co-creating accessible practices. We demonstrate how to apply our framework with three cases: deafness and sign language AI, blindness and visual assistive AI, and stuttering and speech AI. We end by outlining three directions for future work, including cripping AI with diverse human bodyminds, across the entire AI pipeline and ecosystem, and in collaboration with other justice-oriented AI efforts.

Xinru Tang, Ting-an Lin, Jingjin Li +1

University of California, Irvine, USA · AImpower.org, Mountain View, California, USA · University of Connecticut, Storrs, Connecticut, USA

Human-AI Interaction Human-Centered AI Responsible AI

May 2, 2026cs.HC

AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning

Tacit knowledge embedded in expert practice remains difficult to capture, formalise, and scale. While AI-driven educational systems have advanced personalisation, learner modelling, affective support, and self-regulated learning, they less often model the tacit reasoning and context-sensitive judgement that underpin expert practice in practice-based domains. This paper introduces the AI Expert Twin, a cognition-centric framework that models expert knowledge as structured, computable representations of procedural actions, semantic concepts, and decision processes. The framework also considers how value-laden preferences, trade-offs, and uncertainty shape expert judgement in practice. We formalise expert cognition as a three-layer representation and capture knowledge from experts under this model, laying the groundwork for integration into AI-powered educational system. A case study in a cultural heritage workshop demonstrates the feasibility of the approach in a real-world setting. The framework is designed to be transferable across domains such as vocational education and creative industries. By embedding expert heuristics into AI while maintaining transparency and learner agency, the AI Expert Twin offers a novel path towards scalable, practice-based learning and invites further research on ethical, human-centred applications of AI in education.

Annie Yuan, Xiaohua Chen, Kalina Yacef +1

The University of Sydney, NSW 2006, Australia · Beijing Institute of Fashion Technology, No. 2 Yinghua Road, Chaoyang District, Beijing, China, 100029

Computational Cognitive Modeling Cognitive Modeling AI in Education Human-Centered AI

May 1, 2026cs.CY

Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment

Generative AI (GenAI) is transforming workplaces at a rapid pace. This disproportionately affects vulnerable communities, including older workers (OWs) who re-enter the workforce through bridge employment prior to final retirement. Through in-depth semi-structured interviews with 21 professionals, we examine how OWs navigate GenAI-driven disruptions while pursuing bridge roles, focusing on their concerns about GenAI integration and their responses to these changes. Our findings show that OWs experienced both temporal and structural disruptions across all stages of the bridge employment decision-making process due to GenAI. In response, they reconfigured their tasks through different forms of boundary work aimed at restoring stability and continuity. We conceptualize these responses as AI resilience, which reshaped OWs' bridge employment decision-making into an ongoing process of negotiation and adaptation. We conclude by offering recommendations to reduce burnout among OWs by balancing individual-level AI resilience strategies with meso-level AI resilience collectives and macro-level adversarial and contestable AI-mediated organizational structures.

Aditya Nayak, Aakash Gautam, Rama Adithya Varanasi

University of Pittsburgh · New York University

Human-AI Interaction Human-Centered AI

Apr 30, 2026cs.AI

Consumer Attitudes Towards AI in Digital Health: A Mixed-Methods Survey in Australia

AI applications are increasingly being introduced into digital health. While technical performance has advanced rapidly, successful deployment mainly depends on consumer attitudes, especially to patient-facing applications. However, most existing research examines consumer attitudes towards healthcare AI at an abstract level rather than in response to concrete artefacts. We report a mixed-methods survey study in Australia (N=275) examining consumer readiness, acceptance, trust, and risk perceptions of healthcare AI, combined with a scenario-based evaluation of an AI-generated versus clinician-written consultation summary. Participants expressed moderate optimism and strong perceived usefulness and ease of use, but also substantial concerns about accuracy, safety, and data use. In the scenario task, the AI-generated summary was strongly preferred for quality, empathy, and overall usefulness, yet identification of the AI summary was near chance. Findings show that consumers judge AI through concrete communication quality and visible human governance, underscoring the need for clinically supervised deployment frameworks beyond technical performance alone.

Wei Zhou, Rashina Hoda, Joycelyn Ling

1*Faculty of Information Technology, Monash University, Wellington Rd, Clayton, 3800, VIC, Australia. · 2Digital Health CRC, George Street, Sydney, 2000, NSW, Australia.

Healthcare Human-Centered AI Trust in AI

Apr 29, 2026cs.HC

Persona-Based Process Design for Assistive Human-Robot Workplaces for Persons with Disabilities

Human-robot interaction is emerging as an important paradigm for integrating persons with disabilities into the workplace. While these systems can enable individuals to work, their design is mostly personalized, hindering widespread use beyond the individual user. The universal design paradigm is a central pillar of inclusive design, describing usability of systems by all. To incorporate universal design into process design for human-robot workplaces expert knowledge is required that is often not available. To simplify process design of human-robot workplaces, we propose a persona-based design approach. First, typical impairments prevalent in the workforce or particularly relevant for the processes are abstracted into personas with disabilities. The work process is subdivided into sequential actions. For each action and persona, strategies are developed to reach the action goal by a design thinking approach. The resulting actions are ordered by level of robot assistance, i.e. robot involvement, and implemented in a behavior tree. Therefore, the macro-behavior of the workplace may adapt to individual personas online. We demonstrate the method in a collaborative box folding process with a total of seven personas with disabilities. The persona-based process design shows promising results by generating more comprehensive process strategies while enabling adaptive behavior in the sense of universal design.

Nils Mandischer, Daria Eckert and, Lars Mikelsons

Chair of Mechatronics, University of Augsburg, 86159 Augsburg, Germany

Human-Robot Collaboration Human-Centered AI Assistive Robotics Human-Robot Interaction

Apr 29, 2026cs.HC

Culturally Aware GenAI Risks for Youth: Perspectives from Youth, Parents, and Teachers in a Non-Western Context

Generative AI tools are widely used by youth and have introduced new privacy and safety challenges. While prior research has explored youth's safety in GenAI within western context, it often overlooks the cultural, religious, and social dimensions of technology use that strongly shape youths digital experiences in countries like Saudi Arabia. To address the gap, this study explores children (aged 7 to 17), parents and teachers interactions with GenAI tools and risk perceptions through non-western lens. Through a mixed methods approach, we analyzed 736 Reddit and 1,262 X(Twitter) posts and conducted interviews with 31 Saudi Arabian participants (8 youth, 13 parents, 10 teachers). Our findings highlight context dependent and relational privacy and safety of GenAI from non-western context which often formed by communal structure and prescribed norms. We found significant risks tied to youths disclosure of personal and family information, which conflict with culturally rooted expectations of modesty, privacy, and honor, particularly when youth seek emotional support from GenAI. These risks further compounded by socio economic factors such as cost-saving practices leading to the use of shared GenAI accounts (e.g.ChatGPT) within families or even among strangers. We provide design implication reflecting on parents and teachers expectation of how youth should use GenAI. This work lays groundwork for inclusive, context sensitive parental controls that adhere to cultural norms and values.

Aljawharah Alzahrani, Tory Park, Tanusree Sharma

Pennsylvania State University

LLM Safety Human-Centered AI AI Safety Responsible AI

Apr 28, 2026cs.HC

Emotive Architectures: The Role of LLMs in Adjusting Work Environments

In remote and hybrid work contexts, the integration of physical and digital environments is revolutionizing spatial experiences, collaboration, and interpersonal interactions. This study examines three fundamental spatial conditions: the physical environment, characterized by material and sensory attributes; the virtual environment, influenced by immersive technologies; and their fusion into hybrid environments where digital and physical components interact dynamically. The increasing number of AI tools in contemporary society, extensively utilized in both professional and personal spheres, has led to a varied landscape of developing technologies. For instance, ChatGPT has emerged as one of the most downloaded applications, a statistically substantiated fact that demonstrates the swift incorporation of language-based AI into daily life. It also underscores the function of large language models (LLMs) as meaningful bridges between concepts at reading emotional and behavioral signals via natural language. These models provide real-time modifications such as altering illumination, acoustics, or interface configurations, converting static settings into dynamic, emotionally receptive environments. We investigate the integration of language models into professional settings and their potential to enhance user experience by promoting focus, well-being, and engagement. The study investigates ethical concerns, including privacy, emotional tracking, and user agency, emphasizing the importance of inclusive and transparent design. This research formulates a framework for creating co-adaptive environments that merge technological innovation with human-centered experiences, offering a fresh viewpoint on responsive and supportive hybrid workspaces.

Lara Vartziotis, Tina Vartziotis, Frank Beutenmueller +4

National Technical University of Athens, Greece · TWT GmbH Science & Innovation, Stuttgart, Germany · Massachusetts Institute of Technology, Cambridge USA

Human-Centered AI Human-AI Collaboration Affective Computing

Apr 28, 2026cs.HC

Generative UI as an Accessibility Bridge: Lessons from C2C E-Commerce

Web accessibility rests on static standards and developer compliance. That model frays in platforms where content is user-generated: photos arrive blurry or off-frame, descriptions skip size and condition, and page structure shifts from listing to listing. Drawing on six studies conducted between 2022 and 2025 with blind, low-vision, and older adult users of customer-to-customer (C2C) marketplaces, I argue that generative UI can produce adapted interfaces at the point of use, addressing barriers that static design cannot anticipate. Three interventions from this program -- HTML regeneration for screen readers, conversational guidance for older sellers, and audio-guided photo framing for blind sellers -- demonstrate how runtime generation can bridge gaps that standards leave open. I outline what these findings imply for HCI practice: generative UI extends beyond the screen, complements rather than replaces ability-based design, and shifts the designer's role from specifying layouts to specifying policies. This is an expanded arXiv version of a position paper accepted at the CHI 2026 workshop "What does Generative UI mean for HCI Practice?"

Bektur Ryskeldiev

Mercari R4D, Japan · University of Tsukuba, Japan

Web Application Generation Human-Centered AI