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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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.
“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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Not All Students Engage Alike: Multi-Institution Patterns in GenAI Tutor Use
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Balancing AI and Autonomy: The Role of AI in Enhancing Calculus Education
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ANALISIS PERBANDINGAN DAN SENSITIVITAS METODE SAW DAN TOPSIS PADA SELEKSI PENERIMA BEASISWA
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- 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 · 2024: 2
- 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
- Physical Sciences › Computer Science › Information Systems › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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