Searcharxiv⌕ Search

arXiv subjects

Lixiang Yan

Publications and source records attributed to Lixiang Yan.

At least 19 recordsLinked to original sources
arXiv2026

Socially fluent AI decouples conversational signals from source identity in online interaction

Socially fluent agentic AI can now participate in online interaction in ways that resemble ordinary human conversation, potentially weakening people's ability to infer who is human from conversational signals alone. We tested this possibility in synchronous text-based group interaction by embedding undisclosed AI agents as ordinary teammates across analytical, creative, and ethical tasks. Across 786 participants who made 1,572 post-interaction identity judgments, people did not distinguish AI from human teammates above chance. This failure did not arise because the interaction lacked identity-relevant information. Conversational behaviour contained robust cues that differentiated AI from humans and supported highly accurate computational classification. Instead, participants relied on familiar suspicion heuristics, including response speed, fluency, and perceived scriptedness, that were only weakly related to actual identity. Representational analyses further showed that judgments were organised around subjective impressions rather than the behavioural structure encoding ground truth. This dissociation creates new vulnerabilities to coordinated AI agents that can influence and manipulate online discourse at scale.

cs.HC↗
arXiv2026

Building AI Companions that Prioritise Learning over Performance

Large language models (LLMs) are rapidly transforming knowledge work by improving the quality and efficiency of tasks such as writing, coding, and data analysis. However, their growing use in education has exposed a learning-performance paradox: while they can enhance short-term task performance, they may also undermine genuine learning, including cognitive growth, knowledge transfer, and metacognitive development. This paper addresses the question of how artificial intelligence should be designed and used to support learning rather than merely improve immediate outputs. We introduce the concept of AI learning companions, defined as adaptive, pedagogically informed, LLM-powered agents designed for integration into learning environments. We propose a framework for their design built on three interrelated foundations: a pedagogical foundation focused on how students learn with AI, an adaptive foundation focused on how AI learns about students, and a responsible design foundation ensuring systems remain transparent, accountable, inclusive, and secure. The framework is illustrated through five case studies spanning diverse educational contexts, levels, and tool designs, revealing both the promise and current limitations of existing tools. We conclude that there is a necessary shift away from LLMs designed for task-oriented performance, and beyond simply prompting them to act as tutors, toward deliberately developed AI learning companions that are pedagogically sound, adapt to their learners, and foster durable understanding, metacognitive growth, and learner agency.

cs.HC↗
arXiv2026

Agentivism: a learning theory for the age of artificial intelligence

Learning theories have historically changed when the conditions of learning evolved. Generative and agentic AI create a new condition by allowing learners to delegate explanation, writing, problem solving, and other cognitive work to systems that can generate, recommend, and sometimes act on the learner's behalf. This creates a fundamental challenge for learning theory: successful performance can no longer be assumed to indicate learning. Learners may complete tasks effectively with AI support while developing less understanding, weaker judgment, and limited transferable capability. We argue that this problem is not fully captured by existing learning theories. Behaviourism, cognitivism, constructivism, and connectivism remain important, but they do not directly explain when AI-assisted performance becomes durable human capability. We propose Agentivism, a learning theory for human-AI interaction. Agentivism defines learning as durable growth in human capability through selective delegation to AI, epistemic monitoring and verification of AI contributions, reconstructive internalization of AI-assisted outputs, and transfer under reduced support. The importance of Agentivism lies in explaining how learning remains possible when intelligent delegation is easy and human-AI interaction is becoming a persistent and expanding part of human learning.

cs.AI↗
arXiv2026

From School AI Readiness to Student AI Literacy: A National Multilevel Mediation Analysis of Institutional Capacity and Teacher Capability

Artificial intelligence (AI) is increasingly embedded in vocational education systems, yet empirical evidence linking institutional AI readiness to student learning outcomes remains limited. This study develops and tests a 2-2-1 cross-level mediation framework examining how school-level AI readiness is associated with student AI literacy through aggregated teacher mechanisms. Using linked survey data from 1,007 vocational institutions, 156,125 teachers, and 2,379,546 students nationwide, multilevel models were estimated to assess direct, indirect, and contextual effects. Results indicate that overall school AI readiness is positively associated with student AI literacy after adjusting for institutional and regional characteristics. When examined independently, all readiness dimensions show positive associations, while simultaneous modelling suggests that readiness operates as an integrated organisational configuration. Cross-level mediation analyses reveal that aggregated teacher-perceived AI capability partially mediates the relationship between institutional readiness and student literacy, whereas general attitudinal acceptance measures do not demonstrate stable transmission effects. Robustness analyses further show that this readiness-capability-literacy pathway remains structurally stable across heterogeneous regional AI development contexts and under alternative modelling specifications. These findings reposition institutional AI readiness as a multilevel organisational condition linked to student AI literacy, identify collective teacher capability as its central transmission mechanism, and underscore the need to align infrastructural investment with sustained professional capacity development.

cs.CY↗
arXiv2026

Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics

Co-located practical learning leaves evidence in visible actions around patients, task resources and room zones, but these traces are often recovered through live observation or retrospective video review. Fixed wide-angle video could reduce sensing burden, yet a debriefing pipeline must do more than detect behaviours: it must maintain detection after small camera-position shifts, relate the detector-derived behaviour trace to instructor-labelled outcomes and preserve room-zone context. This study evaluates a fixed-camera pipeline in repeated nursing simulation. Using a harmonised six-code taxonomy, we tested YOLO26 target-only training and two-stage source-to-target adaptation across two same-room side-view data sources. We then converted detections from 51 instructor-labelled sessions into one-second behaviour and behaviour-zone traces for rate, ordered-network, transition-network and sequence analyses. Two-stage adaptation improved mean mAP50 from 0.815 to 0.848 for the 2021 target view and from 0.690 to 0.855 for the smaller 2022 target view; with a balanced target quota of \(N = 22\), the 2022 model reached 0.850 mAP50. In the detector-derived behaviour trace analyses, higher phone use characterised low task-performance sessions. Zone labels changed the interpretation of patient interaction: primary patient-care-zone interaction was stronger in higher-performance sessions, while secondary-zone interaction was stronger in lower-performance sessions. Ordered and transition network models showed that ordered room-zone relations contributed beyond behaviour frequency, with the strongest task-performance classifier using zoned and co-presence features. The resulting trace is most appropriate for searchable simulation debriefing, where instructors inspect detected moments rather than receive automated assessment scores.

cs.HC↗
arXiv2025

The Social Blindspot in Human-AI Collaboration: How Undetected AI Personas Reshape Team Dynamics

As generative AI systems become increasingly embedded in collaborative work, they are evolving from visible tools into human-like communicative actors that participate socially rather than merely providing information. Yet little is known about how such agents shape team dynamics when their artificial nature is not recognised, a growing concern as human-like AI is deployed at scale in education, organisations, and civic contexts where collaboration underpins collective outcomes. In a large-scale mixed-design experiment (N = 905), we examined how AI teammates with distinct communicative personas, supportive or contrarian, affected collaboration across analytical, creative, and ethical tasks. Participants worked in triads that were fully human or hybrid human-AI teams, without being informed of AI involvement. Results show that participants had limited ability to detect AI teammates, yet AI personas exerted robust social effects. Contrarian personas reduced psychological safety and discussion quality, whereas supportive personas improved discussion quality without affecting safety. These effects persisted after accounting for individual differences in detectability, revealing a dissociation between influence and awareness that we term the social blindspot. Linguistic analyses confirmed that personas were enacted through systematic differences in affective and relational language, with partial mediation for discussion quality but largely direct effects on psychological safety. Together, the findings demonstrate that AI systems can tacitly regulate collaborative norms through persona-level cues, even when users remain unaware of their presence. We argue that persona design constitutes a form of social governance in hybrid teams, with implications for the responsible deployment of AI in collective settings.

cs.HC↗
arXiv2025

Emergent Learner Agency in Implicit Human-AI Collaboration: How Supportive and Contrarian AI Personas Reshape Interaction

As agentic artificial intelligence (AI) systems move beyond tool-like support toward more autonomous, team-like roles, a central question concerns the extent to which such systems can meaningfully participate in collaborative learning. Emerging work suggests that agentic AI may adopt distinct interactional personas, such as supportive or contrarian roles, yet little is known about how these personas shape learner agency and group dynamics when AI operates as an undisclosed teammate. This study investigates how supportive and contrarian AI personas influence emergent learner agency, discourse patterns, and experiential outcomes in implicit human-AI creative collaboration. A total of 224 university students were randomly assigned to 97 online triads in human-only, supportive-AI, or contrarian-AI conditions. Teams completed an individual-group-individual creative movie-plot task via a 10-minute text chat. Discourse was coded using a creative-regulatory framework and analysed via transition network analysis, sequential pattern mining, and Gaussian mixture clustering to characterise emergent agency patterns. These patterns were linked to cognitive load, psychological safety, teamwork satisfaction, and creative performance. The findings revealed that contrarian AI produced challenge- and reflection-rich discourse indicative of productive friction, whereas supportive AI promoted agreement-centred trajectories. However, while contrarian personas stimulated critical engagement, they reduced teamwork satisfaction and psychological safety without yielding corresponding gains in creative performance. Educational designs need to balance epistemic challenge with the preservation of the affective climate to ensure that hybrid collaboration supports rather than undermines the learner experience.

cs.HC↗
arXiv2025

When Machines Join the Moral Circle: The Persona Effect of Generative AI Agents in Collaborative Reasoning

Generative AI is increasingly positioned as a peer in collaborative learning, yet its effects on ethical deliberation remain unclear. We report a between-subjects experiment with university students (N=217) who discussed an autonomous-vehicle dilemma in triads under three conditions: human-only control, supportive AI teammate, or contrarian AI teammate. Using moral foundations lexicons, argumentative coding from the augmentative knowledge construction framework, semantic trajectory modelling with BERTopic and dynamic time warping, and epistemic network analysis, we traced how AI personas reshape moral discourse. Supportive AIs increased grounded/qualified claims relative to control, consolidating integrative reasoning around care/fairness, while contrarian AIs modestly broadened moral framing and sustained value pluralism. Both AI conditions reduced thematic drift compared with human-only groups, indicating more stable topical focus. Post-discussion justification complexity was only weakly predicted by moral framing and reasoning quality, and shifts in final moral decisions were driven primarily by participants' initial stance rather than condition. Overall, AI teammates altered the process, the distribution and connection of moral frames and argument quality, more than the outcome of moral choice, highlighting the potential of generative AI agents as teammates for eliciting reflective, pluralistic moral reasoning in collaborative learning.

cs.HC↗
arXiv2025

Agentic AI as Undercover Teammates: Argumentative Knowledge Construction in Hybrid Human-AI Collaborative Learning

Generative artificial intelligence (AI) agents are increasingly embedded in collaborative learning environments, yet their impact on the processes of argumentative knowledge construction remains insufficiently understood. Emerging conceptualisations of agentic AI and artificial agency suggest that such systems possess bounded autonomy, interactivity, and adaptability, allowing them to engage as epistemic participants rather than mere instructional tools. Building on this theoretical foundation, the present study investigates how agentic AI, designed as undercover teammates with either supportive or contrarian personas, shapes the epistemic and social dynamics of collaborative reasoning. Drawing on Weinberger and Fischer's (2006) four-dimensional framework, participation, epistemic reasoning, argument structure, and social modes of co-construction, we analysed synchronous discourse data from 212 human and 64 AI participants (92 triads) engaged in an analytical problem-solving task. Mixed-effects and epistemic network analyses revealed that AI teammates maintained balanced participation but substantially reorganised epistemic and social processes: supportive personas promoted conceptual integration and consensus-oriented reasoning, whereas contrarian personas provoked critical elaboration and conflict-driven negotiation. Epistemic adequacy, rather than participation volume, predicted individual learning gains, indicating that agentic AI's educational value lies in enhancing the quality and coordination of reasoning rather than amplifying discourse quantity. These findings extend CSCL theory by conceptualising agentic AI as epistemic and social participants, bounded yet adaptive collaborators that redistribute cognitive and argumentative labour in hybrid human-AI learning environments.

cs.HC↗
arXiv2025

From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning

The role of Artificial Intelligence (AI) in education is undergoing a rapid transformation, moving beyond its historical function as an instructional tool towards a new potential as an active participant in the learning process. This shift is driven by the emergence of agentic AI, autonomous systems capable of proactive, goal-directed action. However, the field lacks a robust conceptual framework to understand, design, and evaluate this new paradigm of human-AI interaction in learning. This paper addresses this gap by proposing a novel conceptual framework (the APCP framework) that charts the transition from AI as a tool to AI as a collaborative partner. We present a four-level model of escalating AI agency within human-AI collaborative learning: (1) the AI as an Adaptive Instrument, (2) the AI as a Proactive Assistant, (3) the AI as a Co-Learner, and (4) the AI as a Peer Collaborator. Grounded in sociocultural theories of learning and Computer-Supported Collaborative Learning (CSCL), this framework provides a structured vocabulary for analysing the shifting roles and responsibilities between human and AI agents. The paper further engages in a critical discussion of the philosophical underpinnings of collaboration, examining whether an AI, lacking genuine consciousness or shared intentionality, can be considered a true collaborator. We conclude that while AI may not achieve authentic phenomenological partnership, it can be designed as a highly effective functional collaborator. This distinction has significant implications for pedagogy, instructional design, and the future research agenda for AI in education, urging a shift in focus towards creating learning environments that harness the complementary strengths of both human and AI.

cs.HC↗
arXiv2025

Dean of LLM Tutors: A Framework for Automated Quality Review of AI-generated Feedback

Large language model (LLM) tutors are increasingly used to generate educational feedback, but existing research has focused mainly on feedback generation rather than feedback evaluation. As a result, LLM-generated feedback may offer limited pedagogical value and carry risks of hallucination. The current study introduces DeanLLM, an automated review framework for comprehensively evaluating feedback generated by LLM tutors before it is shared with students. We developed a 16-dimension evaluation framework covering feedback content, educational effectiveness, and hallucination risks, and validated it using using human-expert annotations of LLM-generated tutor feedback on synthetic computer science assignment submissions derived from real coursework. We then examined whether LLMs could serve as automated LLM-generated tutor feedback reviewers, and used the best-performing reviewer to benchmark tutor feedback generated by 10 commercial LLMs. Psychometric analyses supported the reliability of the proposed framework and showed that human reviewers tended to evaluate feedback holistically, whereas the LLM reviewer separated rubric dimensions more mechanically. Standard zero-shot and few-shot prompting showed limited agreement with human experts for content-quality judgments. Supervised fine-tuning of GPT-4.1 with human-labelled examples containing scores only, without explanatory rationales, achieved the strongest alignment with expert judgments. Reasoning LLMs were particularly effective at hallucination detection and produced automated tutor feedback with stronger educational effectiveness and factuality than lightweight models. The findings indicate that DeanLLM offers a scalable way for automatically improving the reliability and safety of LLM tutor feedback, while also demonstrating that reviewer calibration and model choice remain critical for educational deployment.

cs.CY↗
arXiv2025

SCALEFeedback: A Large-Scale Dataset of Synthetic Computer Science Assignments for LLM-generated Educational Feedback Research

Using Large Language Models (LLMs) to give educational feedback to students for their assignments has attracted much attention in the AI in Education (AIED) field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In this paper, we introduce a synthetic computer science university assignment dataset for LLM-based educational feedback research, called SCALEFeedback (Synthetic Computer science Assignments for LLM Educational Feedback Research). The dataset is generated via Sophisticated Assignment Mimicry (SAM) framework specifically designed to synthesise this dataset and that utilizes one-to-one LLM-based imitation from real assignment descriptions, rubrics, and student submissions. Our open-source dataset contains 10,000 synthetic student submissions spanning 155 assignments across 59 university-level computer science courses. Technical validation confirmed that the synthetic dataset closely resembles real data while successfully eliminating personally identifiable information present in the source material. The creation of this dataset is a valuable contribution to researchers who aim to develop LLM-based generalisable methods for offering high-quality, automated educational feedback in a scalable way.

cs.CY↗
arXiv2025

Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support

Generative Artificial Intelligence (GenAI) holds a potential to advance existing educational technologies with capabilities to automatically generate personalised scaffolds that support students' self-regulated learning (SRL). While advancements in large language models (LLMs) promise improvements in the adaptability and quality of educational technologies for SRL, there remain concerns about the hallucinations in content generated by LLMs, which can compromise both the learning experience and ethical standards. To address these challenges, we proposed GenAI-enabled approaches for evaluating personalised SRL scaffolds before they are presented to students, aiming for reducing hallucinations and improving the overall quality of LLM-generated personalised scaffolds. Specifically, two approaches are investigated. The first approach involved developing a multi-agent system approach for reliability evaluation to assess the extent to which LLM-generated scaffolds accurately target relevant SRL processes. The second approach utilised the "LLM-as-a-Judge" technique for quality evaluation that evaluates LLM-generated scaffolds for their helpfulness in supporting students. We constructed evaluation datasets, and compared our results with single-agent LLM systems and machine learning approach baselines. Our findings indicate that the reliability evaluation approach is highly effective and outperforms the baselines, showing almost perfect alignment with human experts' evaluations. Moreover, both proposed evaluation approaches can be harnessed to effectively reduce hallucinations. Additionally, we identified and discussed bias limitations of the "LLM-as-a-Judge" technique in evaluating LLM-generated scaffolds. We suggest incorporating these approaches into GenAI-powered personalised SRL scaffolding systems to mitigate hallucination issues and improve the overall scaffolding quality.

cs.CY↗
arXiv2025

FLoRA: An Advanced AI-Powered Engine to Facilitate Hybrid Human-AI Regulated Learning

Self-Regulated Learning (SRL), defined as learners' ability to systematically plan, monitor, and regulate their learning activities, is crucial for sustained academic achievement and lifelong learning competencies. Emerging AI developments profoundly influence SRL interactions by potentially either diminishing or strengthening learners' opportunities to exercise their own regulatory skills. Recent literature emphasizes a balanced approach termed Hybrid Human-AI Regulated Learning (HHAIRL), in which AI provides targeted, timely scaffolding while preserving the learners' role as active decision-makers and reflective monitors of their learning process. Central to HHAIRL is the integration of adaptive and personalized learning systems; by modelling each learner's knowledge and self-regulation patterns, AI can deliver contextually relevant scaffolds that support learners during all phases of the SRL process. Nevertheless, existing digital tools frequently fall short, lacking adaptability and personalisation, focusing narrowly on isolated SRL phases, and insufficiently supporting meaningful human-AI interactions. In response, this paper introduces the enhanced FLoRA Engine, which incorporates advanced generative AI features and state-of-the-art learning analytics, and grounds in solid educational theories. The FLoRA Engine offers tools such as collaborative writing, multi-agent chatbots, and detailed learning trace logging to support dynamic, adaptive scaffolding of self-regulation tailored to individual needs in real time. We further present a summary of several research studies that provide the validations for and illustrate how these tools can be utilized in real-world educational and experimental contexts. These studies demonstrate the effectiveness of FLoRA Engine in fostering SRL, providing both theoretical insights and practical solutions for the future of AI-enhanced learning contexts.

cs.HC↗
arXiv2025

The Agency Gap in AI-Supported Writing: How Reactive and Proactive Agent Designs Shape Multimodal Reasoning

Generative AI is becoming part of academic writing, but its educational value depends on how control is shared between learner and system. This study examined an agency gap: performance differences that may arise when AI agent initiative is misaligned with learners' generative AI literacy. Seventy-nine medical and nursing students completed two multimodal analytical writing tasks using healthcare simulation data visualisations. They were randomly assigned to a reactive agent that responded only when prompted or a proactive agent that provided sequenced questions and feedback. Generative AI literacy was measured using the validated 20-item Generative AI Literacy Assessment Test (GLAT). Epistemic network analysis showed that proactive interaction created stronger links among conceptual reasoning, evidence use, and constructive engagement, whereas reactive interaction was more factual and procedural. Ordinal regression showed that generative AI literacy predicted immediate independent writing performance after support was removed, particularly for visual data integration, critical thinking, and overall quality. Condition-specific mediation estimates showed a literacy-performance association in the reactive condition but not in the proactive condition; however, the indirect effects and literacy-by-design interactions were not significant. This pattern is consistent with smaller literacy-related performance differences under proactive scaffolding, but it does not establish a compensatory causal effect. Learner reflections indicated that effective AI writing support requires contextual feedback, dialogic scaffolding, and calibration of initiative to learner needs and task complexity. These findings position interaction design as a potential mechanism for supporting equitable and agency-supportive educational AI agents.

cs.HC↗
arXiv2025

TeamVision: An AI-powered Learning Analytics System for Supporting Reflection in Team-based Healthcare Simulation

Healthcare simulations help learners develop teamwork and clinical skills in a risk-free setting, promoting reflection on real-world practices through structured debriefs. However, despite video's potential, it is hard to use, leaving a gap in providing concise, data-driven summaries for supporting effective debriefing. Addressing this, we present TeamVision, an AI-powered multimodal learning analytics (MMLA) system that captures voice presence, automated transcriptions, body rotation, and positioning data, offering educators a dashboard to guide debriefs immediately after simulations. We conducted an in-the-wild study with 56 teams (221 students) and recorded debriefs led by six teachers using TeamVision. Follow-up interviews with 15 students and five teachers explored perceptions of its usefulness, accuracy, and trustworthiness. This paper examines: i) how TeamVision was used in debriefing, ii) what educators found valuable and challenging, and iii) perceptions of its effectiveness. Results suggest TeamVision enables flexible debriefing and highlights the challenges and implications of using AI-powered systems in healthcare simulation.

cs.HC↗
arXiv2024

Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality

Students are increasingly relying on Generative AI (GAI) to support their writing-a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new challenges for teachers in assessing and supporting student learning, as they often lack insight into whether students are engaging in meaningful cognitive processes during writing or how much of the essay's quality can be attributed to those processes. This study aimed to help teachers better assess and support student learning in GAI-assisted writing by examining how different writing behaviors, especially those indicative of meaningful learning versus those that are not, impact essay quality. Using a dataset of 1,445 GAI-assisted writing sessions, we applied the cutting-edge method, X-Learner, to quantify the causal impact of three GAI-assisted writing behavioral patterns (i.e., seeking suggestions but not accepting them, seeking suggestions and accepting them as they are, and seeking suggestions and accepting them with modification) on four measures of essay quality (i.e., lexical sophistication, syntactic complexity, text cohesion, and linguistic bias). Our analysis showed that writers who frequently modified GAI-generated text-suggesting active engagement in higher-order cognitive processes-consistently improved the quality of their essays in terms of lexical sophistication, syntactic complexity, and text cohesion. In contrast, those who often accepted GAI-generated text without changes, primarily engaging in lower-order processes, saw a decrease in essay quality. Additionally, while human writers tend to introduce linguistic bias when writing independently, incorporating GAI-generated text-even without modification-can help mitigate this bias.

cs.HC↗