The DOI matches its Crossref record, with a small difference in title or year; 32 of the 32 references it cites were found in a registry or an index.
The effect of chatbots on learning: a meta-analysis of empirical research
Abstract
This meta-analysis aimed to comprehensively review empirical studies on the effect of chatbots on learning and quantitatively synthesize their findings to produce an overall effect size. Searching several databases yielded 28 eligible reports with 31 individual effect sizes. The results revealed a significant and medium effect (g = .48) of chatbots on learning. Further analyses indicated four significant moderators: the type of instruction in the comparison group, experimental duration, chatbot type, and chatbot tasks. The highest effect sizes emerged when the comparison group had no specific support, the experiment lasted only one session, and chatbots were task-focused and took care of frequently asked questions. These results suggest that chatbots can be more effective in certain cases within their overall contribution area to learning.
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“Secondly, the impact of GenAI varies significantly across different disciplines, a finding that diverges from Alemdag (2023).”
From Effects of Generative Artificial Intelligence on K-12 and Higher Education Students’ Learning Outcomes: A Meta-Analysis (2025) · cites this paper as Alemdag (2023)
Citations
Cites
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“A sensitivity analysis was also conducted to evaluate the robustness of the synthesized findings (Page et al., 2021).”
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4102_1 · Cited by 7,776 · cited 1×
“Learners with low self-regulation skills and prior knowledge can encounter problems in such environments and experience cognitive load while directing their learning processes with minimal guidance (Kirschner et al., 2006).”
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 4×
“Despite the latest advancements in chatbot technology that can perform various natural language tasks, Kasneci et al. (2023) underscore that “they can only serve as assistive tools to human learners and educators and cannot replace the teacher” (p. 5).”
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Viechtbauer, W., & Cheung, M. W.-L. (2010) · Research Synthesis Methods · doi:10.1002/jrsm.11 · Cited by 2,949
Analysing data and undertaking meta‐analyses
Deeks, J. J., Higgins, J. P., Altman, D. G., & on behalf of the Cochrane Statistical Methods Group (2019) · Cochrane Handbook for Systematic Reviews of Interventions · doi:10.1002/9781119536604.ch10 · Cited by 2,672 · cited 1×
“They are especially helpful in resolving controversies concerning contradictory studies and quantitatively identifying the reasons for different results (Deeks et al., 2022).”
Instructional Design Theories and Models
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Morris, S. B. (2008) · Organizational Research Methods · doi:10.1177/1094428106291059 · Cited by 2,133
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 1×
“Even after new chatbots were trained with a large amount of data, similar concerns were reported, and new issues (e.g. plagiarism, cheating, and different responses to the same questions) arose (Farrokhnia et al., 2023; Tlili et al., 2023).”
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Cited by
Liu, X., Guo, B., He, W., & Hu, X. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251329185 · Cited by 89
Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-12-2023-0236 · Cited by 84
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The Impact of ChatGPT on Students' Academic Achievement: A Meta‐Analysis
Liu, Z., Zuo, H., & Lu, Y. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70096 · Cited by 44
Zhu, Y., Zhu, C., Wu, T., Wang, S., Zhou, Y., Chen, J., Wu, F., & Li, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12898-3 · Cited by 33
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Metadata
- Authors
- Alemdag, E.
- Affiliations
- Technische Universität Dresden
- ORCID
- Alemdag, Ecenaz
- Venue
- Journal of Research on Technology in Education
- Publisher
- Informa UK Limited
- Published
- 2023-09-12
- Volume
- 57
- Issue
- 2
- Pages
- 459-481
- ISSN
- 1539-1523
- 1945-0818
- Cited by
- 67
- References
- 32
- Language
- en
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
