Exploring the impact of GAI-assisted feedback on pre-service teachers’ situational engagement and performance in inquiry-based online discussion
Open accessAbstract
Generative Artificial Intelligence (GAI) enables engagement in logical and meaningful conversations with humans. This quasi-experimental study explored how incorporating GAI into inquiry-based online discussions affected pre-service teachers’ situational engagement, knowledge-building performance, and cognitive load. Participants were assigned to an experimental group equipped with a GAI chatbot for feedback during discussions, and a control group. Both groups were divided into smaller groups for conducting inquiry-based online discussions, and quantitative and qualitative data were collected and analysed. Results show that: (1) GAI-assisted feedback significantly enhanced behavioural and social engagement and knowledge-building performance; (2) no significant difference was found in groups’ cognitive and emotional engagement and post-course written summaries; (3) GAI-assisted feedback did not increase participants’ cognitive load significantly; (4) GAI-assisted feedback helped generate new ideas, facilitate social interaction, and enhance discussion and presentation skills. The integration of GAI-assisted feedback may support cognitive and social development within inquiry-based online discussions.
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In online learning, GenAI enables output to provide personalized learning resources based on students' different styles of input, thus promoting group and peer-to-peer interaction, learning engagement, and effectiveness.
“In online learning, GenAI enables output to provide personalized learning resources based on students’ different styles of input, thus promoting group and peer-to-peer interaction, learning engagement, and effectiveness (Lu and Ba, 2025).”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Lu and Ba, 2025
The Discussion reports that groups using higher-quality prompts received more personalised GAI feedback, and that the feedback facilitated social interaction and online group discussion performance. The Abstract reports improved behavioural and social engagement and knowledge-building performance among pre-service teachers. The paper describes personalised feedback, not personalised learning resources tailored to students’ different input styles, and its findings are specific to inquiry-based online discussions rather than online learning generally. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe introduction of GenAI alters the form of traditional teacher-student interaction; some students may prefer to autonomously use GenAI to find answers rather than engage frequently with teachers.
“Furthermore, the introduction of GenAI alters the form of traditional TSI; some students may prefer to autonomously use GenAI to find answers rather than engage frequently with teachers (Lu and Ba, 2025).”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Lu and Ba, 2025
The paper describes students receiving feedback through a GAI chatbot and discusses ChatGPT as a complement to human feedback (Abstract and Discussion; pages not provided). This supports a change in how students can obtain feedback, but the paper does not report that students prefer using GenAI to find answers instead of interacting frequently with teachers. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Self-citationShadiev, R., Shi, G., Altınay, F., Zhussupova, R. F., Li, Y., Yang, J., Lu, K., Kerimbayev, N., & Dashnyam, B. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2700637
Feedback in STEM Higher Education: A Systematic Literature Review
Pirzada, P., Chen, Y., & Chai, K. K. (2026) · Education Sciences · doi:10.3390/educsci16081205
Li, P., Kinshuk, K., Huang, Y., & Wu, T. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2711652
The Use of GenAI to Enhance Student Engagement in Online Discussions in Higher Education
Yuan, J., & Yang, D. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1017300
Zhang, S., & Guo, T. (2026) · Journal of Computing in Higher Education · doi:10.1007/s12528-026-09505-4
Chu, X., Dai, H., & Zhai, X. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70089
Lu, G., Zhang, H., Ba, S., & Yang, L. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261494805
Self-citationHow Does Generative AI Support Inquiry-Based Learning? A Systematic Review
Yahya, F., Rusijono, R., Dewi, U., Khasanah, S. U., Rusdi, L., Amalia, D., Hamdiah, M., & Nisak, N. S. (2026) · F1000Research · doi:10.12688/f1000research.192825.1
Bai, Y.-Q. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2736124
- Authors
- Lu, G., & Ba, S.
- Affiliations
- Northwest Normal University (CN)
- Education University of Hong Kong (HK)
- Corresponding author
- Ba, Shen
- ORCID
- Venue
- Educational Psychology
- Publisher
- Informa UK Limited
- Published
- 2025-04-09
- Volume
- 46
- Issue
- 1
- Pages
- 1-26
- ISSN
- 0144-3410 (ISSN-L)
- 1469-5820
- OpenAlex
- W4409282833
- Cited by
- 24
- References
- 90
- Citations by year
- 2026: 17 · 2025: 7
- FWCI
- 24.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Educational Psychology (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China
- Start-up Research Grant
- Teaching Development Grant
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- generative AI, AI-generated feedback, pre-service teachers, inquiry-based learning, online discourse, cognitive load, knowledge construction, social engagement, teacher professional development
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