Issue 2026-W34

Covers:2026-08-17 – 2026-08-23

Collected weekly from a hand-picked list of academic sources, and only studies with real human participants. Original titles and official links are always kept. Chinese summaries are machine-generated and exist only to help you decide what to open.

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What AI is doing to people, this week

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48 matching stories

Evaluation in the Age of AI: Output as Evidence of Learning

Source summaryThe rapid adoption of artificial intelligence (AI), particularly large language models (LLMs), has fundamentally disrupted how learning is demonstrated and evaluated in higher education. Tasks that once served as proxies for understanding-such as writing essays, solving problem sets, or producing computer code-can now be generated superficially by AI systems with minimal human effort. This…

Poetic Heritage for Culturally Grounded Emotional Support: An Interaction Design Framework and Its Multimodal Agentic Instantiation

Source summaryDigital systems increasingly mediate emotional support, yet their interactions often remain culturally generic. Accordingly, we examine how a poetic tradition can be operationalized as a culturally grounded interactive medium and how generative AI can support such engagement. The resulting interaction design framework translates staged literature-based support and tradition-specific poetic…

"I want to be pushed, I want to grow": Enabling social workers to design evaluations of LLM augmentation in their work

Source summaryWorkers are increasingly asked to adopt AI systems to assist their work, yet are rarely given a voice in defining what meaningful AI augmentation should look like or how to evaluate for it. In this paper, we propose worker-driven AI measurement---a bottom-up approach to AI evaluation where workers collaboratively shape decisions about which tasks AI should augment, what "successful" augmentation…

Probing How Users Interact with Turn-Level Design Frictions for AI Chatbots

Source summaryAI chatbots can help people write faster, but they can also encourage overreliance by making it easy to turn minimal input into usable text. We study turn-level design friction: intentional constraints added to each chatbot exchange that slow, limit, or redirect how users request, access, or use model responses. We designed six friction probes, organized around three mechanisms: eliciting user…

CAIA in Practice: Field Evaluation of an AI-Assisted Support System for Text-Based Online Counselling

Source summaryRising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a crucial low-threshold channel for accessing care. This paper presents CAIA, a co-designed AI-based tool suite that demonstrates responsible AI integration into counselling practice through seven LLM-driven functions enhanced by retrieval-augmented…

AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversations

Source summaryConversational AI increasingly shapes consequential decisions, yet users have limited support for recognizing and resisting manipulation. We present AI Watchdog, a browser-based agent interface that monitors live conversations, detects five dark-pattern categories, including sycophancy, brand bias, anthropomorphization, sneaking, and harmful generation, and alerts users when they occur. Its…

Who Bears the Cost of Honesty? A FAccT Workshop Synthesis and Research Agenda for Equitable AI Disclosure

Source summaryAI disclosure is increasingly promoted and sometimes required as a route to transparency, accountability, provenance, and trust. Yet disclosure can also expose AI users to suspicion, stigma (e.g., competence penalties), and surveillance, affecting minoritized groups in particular. This paper reports on Who Bears the Cost of Honesty?, a CRAFT workshop at the 2026 ACM Conference on Fairness,…

AI-Augmented Inquiry and Regulation in Hybrid Systems: A Control Allocation Architecture for Preserving Epistemic Agency in Hybrid Human-AI Cognition

Source summaryGenerative artificial intelligence (genAI) systems are increasingly integral to epistemic processes such as hypothesis generation, explanation construction, and decision-making. Although they reliably enhance performance, emerging evidence reveals a metacognitive dilemma: as external generative capacity increases, internal monitoring, calibration, and cognitive engagement may decline. This…

Event-Time Confounding Under Bursty Human Dynamics

Source summaryStudies of digital behavior often align users at moments they choose, such as opening an AI assistant, clicking a recommendation, or visiting a product page, and interpret higher activity afterward as an event effect. We show how this creates an endogenous time zero: the event occurs during an ongoing task episode, so the aligned curve can trace episode continuation rather than a response to the…

Supporting The Many Lives of Personal Data with Rebite: LLM-Powered Goal-Directed Framing in Food Journaling

Source summaryPeople's health and tracking goals frequently change, but most personal informatics systems struggle to adapt, leading people to abandon their data and start over. We propose goal-directed framing, an approach that repositions goals within personal informatics systems. Instead of fixing the meaning of data at capture time, the approach frames the collected data through the current goal and…

Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support

Source summaryTrust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value. Yet research in this area lacks validated psychometric instruments for assessing user perceptions in affective AI contexts and large-scale evidence on how trust formation varies across user segments. To address these gaps, we develop and validate a seven-construct…

From Search Agents to Dissemination Interfaces: Understanding Human Trust in Health Information from Conversational Search

Source summaryLarge Language Models (LLMs) deployed through Conversational User Interfaces (CUIs) are transforming health information-seeking by offering immediate, interactive experiences compared to traditional search engines like Google. However, how trust is influenced by both the types of search agents and the interface used to disseminate the information remains underexplored. This research integrates…

Atom Learning Model (ALM): how a real classroom got tokenised

Source summaryThe Atom Learning Model (ALM) tokenises a school curriculum. Two secondary mathematics textbooks were read by machine into 1,934 atoms, each one thing a learner can do in a single step, ordered by 4,616 machine-written prerequisite links. Both sides of a lesson are then expressed in that one structure: a question is a set of atoms plus everything beneath them, a child's ability is a score between…

Beyond the Traceback: Using LLMs for Adaptive Explanations of Programming Errors

Source summaryProgramming error messages are critical for software development, yet they remain difficult for novice programmers to interpret. While Large Language Models (LLMs) can rewrite these errors into clearer explanations, it remains unclear whether increased readability improves objective debugging performance or how explanation styles should align with programmer skill. We present a multi-stage…

The Belief Update Gate: Separating Inertia from Learning in Human-AI Interaction

Source summaryRepeated human-AI interaction is often analyzed through pooled belief-updating slopes: users observe AI successes and failures, revise reported beliefs in the feedback-consistent direction, but appear conservative on average. We show that such averages can obscure an important distinction between whether an elicited belief report changes at all and how it changes conditional on movement. We refer…

The Legibility Gap: How Gender Equity Interventions Redistribute Recognition Across Cultures

Source summaryEfforts to promote gender equity in science increasingly rely on name-based inference to quantify representation and guide policy and behavior. Yet linguistic cues that signal gender vary across cultures and are often obscured when names are transliterated into English. Here we identify a pattern we call the "legibility gap": when gender is inferred from names, equity interventions systematically…

Interaction Effects Between Learner Characteristics and Dialogue Format in TTS Dialogue-Based Lessons

Source summaryThis study examined how learner characteristics affect motivation, learning outcomes, and overall evaluation in three types of dialogue-based lessons---(1) teacher--student, (2) student--student, and (3) teacher--teacher---generated using a large language model (LLM) and Text-to-Speech (TTS) technology. In particular, we focused on the interaction effects between dialogue format and learners'…

Chat First, Worry Later: Understanding Individuals' Privacy Perceptions Using ChatGPT in a Work Context

Source summaryGenerative Artificial Intelligence (GenAI) tools like ChatGPT, which can generate human-like responses from vast amounts of textual data, are increasingly transforming work routines across various fields, including education, healthcare, and IT. This integration, however, raises privacy concerns and questions the readiness of both environments and individuals. To investigate this issue, we…

Disentangling Threads: Exploring the Potential of LLM-Supported Discussion Forum Analysis for Community Insight

Source summaryOnline discussion forums enable people from diverse backgrounds to share ideas, feedback, and perspectives. These organic discussions can help researchers understand communities' collective viewpoints, but insights are often difficult to uncover given their freeform reply structure. Large language models (LLMs) support qualitative text analysis but can misalign with researchers' analytical intent…

Parasocial Engagement With Social Media Influencers and Mental Health Outcomes: Systematic Review and Meta-Analysis

Source summaryBackground: Social media influencers occupy a pervasive role in billions of users’ daily digital lives, particularly among adolescents and young adults. Audiences develop parasocial engagement with these figures, including parasocial relationships (PSRs) and parasocial interactions (PSIs). Despite growing concern about their mental health implications, no prior meta-analysis has quantitatively…

Designing Human-mediated AI Guidance: Ready Together for Personalized Family Emergency Preparedness

Source summaryArtificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decision support. However, in some contexts, AI-generated information may not be suitable for direct delivery to the final recipient. Instead, it may need to be interpreted, adapted, and communicated by a human who understands the recipient's needs, emotional state,…

Modeling AI Overreliance as a Complex Adaptive System

Source summaryWhether AI assistance helps or harms a population depends less on the model's accuracy than on whether people rely on it appropriately trusting it when it is right and checking it when it is not. Yet reliance is usually studied one user at a time. We model it as a population process: agents repeatedly solve a task alone, accept an AI answer, or verify it, updating a Bayesian belief about AI…

AI fear of missing out (AI FOMO): Desire without action under social pressure

Source summaryBig Data & Society, Volume 13, Issue 3, July-September 2026. Fear of missing out (FOMO) is known to drive engagement. But does this apply to AI FOMO, the fear of falling behind with artificial intelligence (AI)? This commentary argues that it does not. Many people feel this fear intensely yet still do not act. ...

Two-sided receptivity to conversational AI agents in online dating: Bilingual survey data from Fledge.Love

Source summaryAutonomous conversational agents and generative-AI features are being added to online dating platforms faster than public evidence about user attitudes can accumulate, and the scarcest evidence concerns the receiving side: how people react when the profiles, messages, or conversation partners they encounter are machine-generated. We release two anonymized survey datasets collected from active…

Does Listening Matter? Backchanneling and Nodding in AI Clone

Source summaryAI clones that imitate a specific person typically reproduce what the person says and how they sound, but not how they listen. We investigate whether adding multimodal listening behaviors gives such a clone more presence and authenticity. We integrated verbal backchannels and head nodding, driven by real-time prediction models, into an AI clone equipped with voice cloning and LLM-based responses.…

Multi-Tier Mentorship with AI-Assisted Development: Authentic Engineering for K-12 and Undergraduates

Source summaryK-12 students often possess creative engineering ideas but lack technical skills to build them, while undergraduates have coding expertise but few opportunities to lead real-world projects or mentor others. The rapid development of AI-assisted tools offers a potential bridge to connect these groups, yet the structure for effective K-12 and university collaborations remains underexplored. This…

MultiVerse: A Creator-Centered Approach to Steering Context-Adaptive Lyrics

Source summaryGenerative AI may enable new forms of context-aware creative expression by dynamically tailoring media content to its consumption context. For instance, AI systems could adapt song lyrics to the listener and their current activity. However, existing media adaptation systems primarily optimize for audience experience, often neglecting artists' intent, style, and preference. We address this…

LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University

Source summaryAs generative AI reshapes professional and educational practice, institutions face a challenge: how to support diverse learners, from non-coders to advanced students, in building confidence and practice with AI-supported problem solving. Most institutional responses bifurcate into conceptual workshops for general audiences or technical courses for computer science majors, leaving few spaces where…

When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation

Source summaryReadable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves. We investigated how source-text condition and output rendering relate to perceived translation quality, and how output and system appraisals relate to trust and stated disclosure willingness in a plain-text interface. A focal 2 * 2 comparison…

Generative AI in Youth Mental Health Apps: Rapid Review

Source summaryBackground: Mental health apps are frequently used as platforms for delivering digital health interventions to young people. New technology such as generative AI enables a wider range of engaging and more personalized features that can be included in mental health apps. However, there is limited insight into the opportunities for integrating generative AI into such apps. Objective: This rapid…

Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction

Source summaryAmid concerns that generative AI may standardize art interpretation, this paper examines whether LLM-based interaction can support plural art-historical narrative construction. We present Sanyu Studio, a multi-agent dialogue system that models 321 Sanyu oil paintings as agents with fact, interpretation, organization, and memory-filtering mechanisms. Based on a seven-day workshop with eight…

The Fabricated Front: Generative AI and the Opacity of Workplace Performance

Source summaryGenerative AI (GenAI) has become a fixture of workplace life. Current research asks chiefly what this implies for jobs and outputs, measured in productivity, displacement, or bias. What remains underexamined are the interactional reconfigurations that GenAI produces at work. The emerging concept of effort opacity has begun to fill this gap by highlighting the systematic decoupling of observable…

AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence

Source summaryThe integration of generative AI into web search delivers synthesized answers to user queries, changing how people navigate and assess information, while raising concerns about the downstream impacts on publishers who supply the underlying content. We conduct a preregistered field experiment (N=1,100) on Google Search, the dominant online search platform, to estimate the causal effects of AI…

Governing Delegation to Generative Artificial Intelligence: Human Direction, Work-Related Orientation, and Modes of Use

Source summaryDelegating cognitive operations to generative artificial intelligence redistributes execution and raises a governance problem: where human direction of the task remains. We distinguish two routes. Specified delegation places that direction before execution, through instructions, constraints, or criteria that delimit the task. Iterative coproduction places it during production, through…

A Recognition-Based Framework for Human–Robot Relationships

Source summaryPublication date: Available online 18 August 2026 Source: Computers in Human Behavior: Artificial Humans Author(s): Junichi Hoshino

Language Models Reproduce Human Reductionist Bias and Decision Inconsistency in Neurodevelopmental Disorders Assessment

Source summaryLarge language models (LLMs) are increasingly supporting complex mental-health decisions, which depend not only on factual evidence but also value-laden interpretations. We introduce a mixed-methods human-LLM auditing framework examining decision consistency, susceptibility to cognitive heuristics, declarative intellectual humility, and the concepts operationalized in support-allocation judgments…

Evaluating Beyond the Screen: Collective Assessment of AI-Generated Business Plans with Resource-Constrained Entrepreneurs

Source summaryEntrepreneurs increasingly use end-user generative AI technologies such as ChatGPT for high-stakes documents like loan applications and business plans, where AI-generated errors---a wrong price, a fabricated product---can affect loan or funding outcomes. Current approaches to supporting evaluation of AI-generated text assume a single user assessing output alone, on screen. This can be especially…

Love in the Age of AI: An Integrative Process Model of Romantic Human-Chatbot Relationships

Source summaryThe increasing ability of social chatbots to form deep and even romantic Human-Chatbot Re lationships (HCRs) has drawn growing academic attention. Yet, existing research remains fragmented, often examining individual stages such as initiation or dissolution in isolation, without tracing the full relational trajectory. Such fragmentation, however, hinders a holistic understanding of the interplay…

The User Side of AI Model Lifecycles: Evidence from the Keep4o Movement

Source summaryAI model lifecycles are commonly understood as a series of technical and organizational processes. Yet once a model enters sustained use, subsequent changes can also affect established user practices and user value. Using the Keep4o movement around GPT-4o as a case, this study examines post-deployment AI model lifecycle issues from the user side. We collected 61,846 public original posts on X…