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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2601.11576 |
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| _version_ | 1866914260192329728 |
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| author | Zhang, Long Lin, Fangwei Wang, Weilin |
| author_facet | Zhang, Long Lin, Fangwei Wang, Weilin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What Can Student-AI Dialogues Tell Us About Students' Self-Regulated Learning? An exploratory framework Zhang, Long Lin, Fangwei Wang, Weilin Computers and Society 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. |
| title | What Can Student-AI Dialogues Tell Us About Students' Self-Regulated Learning? An exploratory framework |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2601.11576 |