What Can Student-AI Dialogues Tell Us About Students' Self-Regulated Learning? An exploratory framework

• URL: http://arxiv.org/abs/2601.11576v1
• Date: Tue, 30 Dec 2025 15:50:27 GMT
• Title: What Can Student-AI Dialogues Tell Us About Students' Self-Regulated Learning? An exploratory framework
• Authors: Long Zhang, Fangwei Lin, Weilin Wang,
• Abstract summary: The rise of Human-AI Collaborative Learning (HAICL) is shifting education toward dialogue-centric paradigms.<n>This study investigates whether the student-AI dialogue can serve as a valid, non-interrupted data source for Self-Regulated Learning assessment.
• Score: 2.7367574061168747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of Human-AI Collaborative Learning (HAICL) is shifting education toward dialogue-centric paradigms, creating an urgent need for new assessment methods. Evaluating Self-Regulated Learning (SRL) in this context presents new challenges, as the limitations of conventional approaches become more apparent. Questionnaires remain interrupted, while the utility of non-interrupted metrics like clickstream data is diminishing as more learning activity occurs within the dialogue. This study therefore investigates whether the student-AI dialogue can serve as a valid, non-interrupted data source for SRL assessment. We analyzed 421 dialogue logs from 98 university students interacting with a generative AI (GenAI) learning partner. Using large language model embeddings and clustering, we identified 22 dialogue patterns and quantified each student's interaction as a profile of alignment scores, which were analyzed against their Online Self-Regulated Learning Questionnaire (OSLQ) scores. Findings revealed a significant positive association between proactive dialogue patterns (e.g., post-class knowledge integration) and overall SRL. Conversely, reactive patterns (e.g., foundational pre-class questions) were significantly and negatively associated with overall SRL and its sub-processes. A group comparison substantiated these results, with low-SRL students showing significantly higher alignment with reactive patterns than their high-SRL counterparts. This study proposed the Dialogue-Based Human-AI Self-Regulated Learning (DHASRL) framework, a practical methodology for embedding SRL assessment directly within the HAICL dialogue to enable real-time monitoring and scaffolding of student regulation.

Related papers

• Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning [66.52010873968383]
  We introduce a conversational agent that interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training.<n>The experimental results across four widely used conversational benchmarks demonstrate the effectiveness of our methods.
  arXiv  Detail & Related papers  (2026-01-19T14:55:54Z)
• What Students Ask, How a Generative AI Assistant Responds: Exploring Higher Education Students' Dialogues on Learning Analytics Feedback [0.3562673545689596]
  Learning analytics dashboards (LADs) aim to support students' regulation of learning by translating complex data into feedback.<n>Students, especially those with lower self-regulated learning (SRL) competence, often struggle to engage with and interpret analytics feedback.<n>We explored authentic dialogues between students and GenAI assistant integrated into LAD during a 10-week semester.
  arXiv  Detail & Related papers  (2026-01-08T13:17:44Z)
• How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction [6.649393350057383]
  This study systematically investigates the structural and behavioral differences between AI-simulated and authentic human tutoring dialogues.<n>Results show that human dialogues are significantly superior to their AI counterparts in utterance length, as well as in questioning (I-Q) and general feedback (F-F) behaviors.
  arXiv  Detail & Related papers  (2025-09-02T03:18:39Z)
• Teaching Language Models To Gather Information Proactively [53.85419549904644]
  Large language models (LLMs) are increasingly expected to function as collaborative partners.<n>In this work, we introduce a new task paradigm: proactive information gathering.<n>We design a scalable framework that generates partially specified, real-world tasks, masking key information.<n>Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information.
  arXiv  Detail & Related papers  (2025-07-28T23:50:09Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models [51.75805497456226]
  This work focuses on the factual consistency issue with the help of the dialogue summarization task. Our evaluation shows that, on average, 26.8% of the summaries generated by LLMs contain factual inconsistency. To stimulate and enhance the dialogue comprehension ability of LLMs, we propose a fine-tuning paradigm with auto-constructed multi-task data.
  arXiv  Detail & Related papers  (2023-11-13T09:32:12Z)
• Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning [23.149638288383347]
  Current state-of-the-art systems do this by training agents with supervised learning or online reinforcement learning (RL) We propose an offline RL framework to improve the persona consistency of dialogue systems.
  arXiv  Detail & Related papers  (2023-10-16T18:05:54Z)
• CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning [85.3987745097806]
  offline reinforcement learning can be used to train dialogue agents entirely using static datasets collected from human speakers. Experiments show that recently developed offline RL methods can be combined with language models to yield realistic dialogue agents.
  arXiv  Detail & Related papers  (2022-04-18T17:43:21Z)
• Conversational Semantic Role Labeling [55.73065201984987]
  We propose the conversational SRL task, where an argument can be the dialogue participants, a phrase in the dialogue history or the current sentence. As the existing SRL datasets are in the sentence level, we manually annotate semantic roles for 3,000 chit-chat dialogues. Our initial study by applying CSRL to two mainstream conversational tasks, dialogue response generation and dialogue context rewriting, also confirms the usefulness of CSRL.
  arXiv  Detail & Related papers  (2021-04-11T07:45:04Z)
• Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning [8.744026064255337]
  We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), to realize the dialogue policy for automatic curriculum learning. The teacher model arranges a meaningful ordered curriculum and automatically adjusts it by monitoring the learning progress of the dialogue agent. Experiments show that the ACL-DQN significantly improves the effectiveness and stability of dialogue tasks with a statistically significant margin.
  arXiv  Detail & Related papers  (2020-12-28T02:44:49Z)
• Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues [88.73739515457116]
  We introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination. We jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. Experiment results indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection.
  arXiv  Detail & Related papers  (2020-09-14T08:44:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.