Interpretable dropout prediction: towards XAI-based personalized intervention
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Much extant research on AI has focused on students or administrators.
“Much of the extant research on AI has focused on students (e.g., Chan and Hu, 2023;Crompton and Burke, 2023) or administrators (e.g., Nagy and Molontay, 2024;Teng et al., 2023).”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Nagy and Molontay, 2024
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Cites
A Unified Approach to Interpreting Model Predictions
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“For systematic reviews, we refer to Avella et al (2016); Dutt et al (2017); Zawacki-Richter et al (2019); and Rastrollo-Guerrero et al (2020).”
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers
Schneider, J., Richner, R., & Riser, M. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00289-z · Cited by 48 · see citing works
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Sorted by citations
Human-centered evaluation of explainable AI applications: a systematic review
Kim, J., Maathuis, H., & Sent, D. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1456486 · Cited by 130 · see citing works
Liang, J., Stephens, J. M., & Brown, G. T. L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1522841 · Cited by 112 · see citing works
- Authors
- Nagy, M., & Molontay, R.
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 34
- Issue
- 2
- Pages
- 274-300
- ISSN
- 1560-4292
- Cited by
- 129
- References
- 4
- Language
- en
- Licence
- Funders
- Nemzeti Kutatási, Fejlesztési és Innovaciós Alap (OTKA PD-142585, KDP-2020)
- European Commission (RRF-2.3.1-21-2022-00004 Artificial Intelligence National Laboratory.)
- Budapest University of Technology and Economics
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