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Unpacking help-seeking process through multimodal learning analytics: A comparative study of ChatGPT vs Human expert
Open accessAbstract
Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners’ help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI. • The help-seeking process of learners seeking help from AI is non-linear. • Learners' help-seeking from human experts is more linear and aligns with theory. • Learners seeking help from ChatGPT showing pragmatic help-seeking activities. • Learners seeking help from human experts were more proactive in metacognition. • Scaffolding is needed to better facilitate learning with AI.
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“Second, technology acceptance, learning outcomes, and user attitudes have often been examined separately, leaving the process links among search mode, task trust, source verification, and learning transfer insufficiently explained (García-Alonso et al., 2024; Chen et al., 2025).”
From Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions (2026) · cites this paper as Chen et al., 2025
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Metadata
- Authors
- Chen, A., Xiang, M., Zhou, J., Jia, J., Shang, J., Li, X., Gašević, D., & Fan, Y.
- Affiliations
- Peking University (CN)
- Monash University (AU)
- Corresponding author
- Fan, Yizhou
- ORCID
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 226
- Pages
- 105198
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4404361386
- Cited by
- 115
- References
- 71
- Citations by year
- 2026: 79 · 2025: 34
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Education (publishedVersion, cc-by)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by)
- HAL (submittedVersion)
- Funders
- National Natural Science Foundation of China (62407001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- help-seeking, ChatGPT, generative AI, student self-regulation, essay writing, feedback evaluation, metacognition, overreliance on AI, social pressure, multimodal learning analytics, eye tracking, process mining, conversational data
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