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Cited by 116Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in EducationInnovative Teaching and Learning Methods

The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.

Unpacking help-seeking process through multimodal learning analytics: A comparative study of ChatGPT vs Human expert

Open access
Chen, Angxuan, Xiang, Mengtong, Zhou, Junyi, Jia, Jiyou, Shang, Junjie, Li, Xinyu, Gašević, Dragan, Fan, Yizhou·2025·Computers & Education
DOI 10.1016/j.compedu.2024.105198
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Abstract

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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Research works printed but not linked: 3

  1. Atchley, 2024, Vol. 9, 20
  2. Winne, 2000, Chapter 16 - measuring self-regulated learning, 531
  3. Zirar, 2023, Exploring the impact of language models, such as chatgpt, on student learning and assessment, The Review of Education, 11, 3

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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)
DOI
10.1016/j.compedu.2024.105198
OpenAlex
W4404361386
Cited by
115
References
71
Citations by year
2026: 79 · 2025: 34
FWCI
14.93
Type
journal article
Language
en
Open access
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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