Mapping the Research Landscape of Artificial Intelligence Innovation for Dropout Prevention and Learning Opportunity Loss Mitigation Policy
DOI:
https://doi.org/10.33394/jk.v12i3.21069Keywords:
Artificial Intelligence, Bibliometric Analysis, Dropout Prevention, Explainable Artificial Intelligence, Learning Loss MitigationAbstract
This study aims to map the research landscape of Explainable Artificial Intelligence (XAI) for dropout prevention and learning loss mitigation. The study employed bibliometric science mapping to identify publication trends, conceptual structures, and emerging research directions. A Scopus search using the query TITLE-ABS-KEY("Explainable Artificial Intelligence" OR "Explainable AI") AND TITLE-ABS-KEY(Dropout OR "Learning Loss Mitigation") initially returned 207 records. No restrictions on language, document type, or publication year were applied during the retrieval process. Seven records with missing author metadata were excluded, leaving 200 documents published between 2020 and 2026 for analysis using Biblioshiny and VOSviewer. The results revealed rapid publication growth, with an annual growth rate of 57.04%, peaking in 2025 with 99 publications. Dominant themes included deep learning, explainable AI, learning systems, machine learning, dropout prediction, SHAP, LIME, interpretability, and uncertainty, while three broad conceptual clusters centered on deep learning, explainable artificial intelligence, and contrastive learning. Learning loss mitigation did not form a stable research cluster, indicating that the field has progressed more rapidly in explaining dropout prediction than in establishing connections between XAI and longitudinal learning decline. Theoretically, this study identifies a research agenda that integrates learning loss indicators with explainability, uncertainty, and fairness. Practically, it suggests that education policymakers should prioritize human-supervised early-warning systems whose explanations can be translated into timely and actionable interventions.
References
Abulibdeh, A. (2025). A systematic and bibliometric review of artificial intelligence in sustainable education: Current trends and future research directions. Sustainable Futures, 10, 101033.
Adnan, M., Uddin, M. I., Khan, E., Alharithi, F. S., Amin, S., & Alzahrani, A. A. (2022). Earliest possible global and local interpretation of students’ performance in virtual learning environment by leveraging explainable AI. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3227072
Al Husaeni, D. F., Widiaty, I., Mulyanti, B., Abdullah, A. G., Riza, L. S., Suherman, A., & Al Husaeni, D. N. (2025). Trends and impacts of artificial intelligence application in the development of computational thinking skills. Informatics in Education, 24(2), 261–298. https://doi.org/10.15388/infedu.2025.14
Albugami, S., Wali, A., & Almagrabi, H. (2026). Predicting student dropout in Saudi universities using machine learning and explainable AI. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3490
Alsuhaimi, R., & Almatrafi, O. (2024). Identifying learners’ confusion in a MOOC forum across domains using explainable deep transfer learning. Information, 15(11), 681. https://doi.org/10.3390/info15110681
Baranyi, M., Nagy, M., & Molontay, R. (2020). Interpretable deep learning for university dropout prediction. Proceedings of the 21st Annual Conference on Information Technology Education. https://doi.org/10.1145/3368308.3415382
Barboza, A., Huamani, J., Santisteban, J., Maculan, N., & Mauricio, D. (2026). Prediction and explainability of school dropout in Peru using machine learning and explainable artificial intelligence. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3675564
Bayrakceken, E., Çinici, M., & Alkan, Ö. (2025). School dropout among children aged 5–17 in Türkiye and related factors: A logistic regression analysis. BMC Pediatrics, 25, Article 661. https://doi.org/10.1186/s12887-025-06034-4
Bettahi, A., Belouadha, F.-Z., & Harroud, H. (2025). A modular and explainable machine learning pipeline for student dropout prediction in higher education. Algorithms, 18(10), 662. https://doi.org/10.3390/a18100662
Biswas, S., & Bhattacharya, K. (2025). Gender heterogeneity in secondary school dropout due to financial shocks: Evidence from India. International Journal of Educational Development, 119, Article 103452. https://doi.org/10.1016/j.ijedudev.2025.103452
Bulut, O., Wongvorachan, T., He, S., & Lee, S. (2024). Enhancing high-school dropout identification: A collaborative approach integrating human and machine insights. Discover Education. https://doi.org/10.1007/s44217-024-00209-4
Camargos, R. C., & Silveira, I. F. (2025). Mitigating cognitive biases in predicting student dropout: Global and local explainability with Explainable Boosting Machine. Journal of Universal Computer Science. https://doi.org/10.3897/jucs.131773
Chen, H. E., Sun, D., Hsu, T. C., Yang, Y., & Sun, J. (2023). Visualising trends in computational thinking research from 2012 to 2021: A bibliometric analysis. Thinking Skills and Creativity, 47, Article 101224.
Cheniti-Belcadhi, L., Hadyaoui, A., & Mitwally, M. A. A. (2025). Ontology-based adaptive assessment in digital education: Inclusive, transparent, and fair AI for assessment. In [Book/proceedings title not provided] (pp. 203–238). https://doi.org/10.4018/979-8-3373-2130-1.ch007
Chitti, M., Chitti, P., & Jayabalan, M. (2020). Need for interpretable student performance prediction. Proceedings of the International Conference on Developments in eSystems Engineering. https://doi.org/10.1109/DeSE51703.2020.9450735
Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023). Should learning analytics models include sensitive attributes? Explaining the why. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/TLT.2022.3226474
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., ... & Ranjan, R. (2023). Explainable AI (XAI): Core ideas, techniques, and solutions. ACM computing surveys, 55(9), 1-33.
Fu, Y., Weng, Z., & Wang, J. (2025). Examining AI use in educational contexts: A scoping meta-review and bibliometric analysis. International journal of artificial intelligence in education, 35(3), 1388-1444.
Gicheva, D., Edmunds, J., Eilam, N., Hutchins, B., & Arshavsky, N. (2025). Gender differences in college students’ academic performance during and after the COVID-19 pandemic: Is females’ recovery lagging behind? Economics Letters, 255, Article 112491. https://doi.org/10.1016/j.econlet.2025.112491
Harmey, S., & Moss, G. (2023). Learning disruption or learning loss: Using evidence from unplanned closures to inform returning to school after COVID-19. Educational Review, 75(4), 637-656.
Khan, L. H., Haque, M. A., Ibrahim, E. A., Mostakim, M., & Hossain, M. I. (2023). Interpretable MOOC dropout prediction using different ensemble methods and XAI. ICAAIC 2023. https://doi.org/10.1109/ICAAIC56838.2023.10140724
Košir, S., Aslan, M., & Lakshminarayanan, R. (2025). Adolescent dropout and school attachment: The moderating effects of gender. International Journal of Educational Development, 117, Article 103356. https://doi.org/10.1016/j.ijedudev.2025.103356
Lamsiyah, S., Nourbakhsh, A., El-Amrany, S., & Schommer, C. (2025). Privacy-preserving federated learning for student dropout prediction: Enhancing model transparency with explainable AI. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-98465-5_41
Lim, W. M., Kumar, S., & Donthu, N. (2024). How to combine and clean bibliometric data and use bibliometric tools synergistically: Guidelines using metaverse research. Journal of Business Research, 182, Article 114760.
Melo, E., Silva, I., Costa, D. G., Viegas, C. M. D., & Barros, T. M. (2022). On the use of eXplainable artificial intelligence to evaluate school dropout. Education Sciences. https://doi.org/10.3390/educsci12120845
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 55(5), 3503-3568.
Mustofa, S., Emon, Y. R., Mamun, S. B., Akhy, S. A., & Ahad, M. T. (2025). A novel AI-driven model for student dropout risk analysis with explainable AI insights. Computers and Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2024.100352
Nagy, M., & Molontay, R. (2024). Interpretable dropout prediction: Towards XAI-based personalized intervention. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00331-8
Page, E., Leonard-Kane, R., Kashefpakdel, E., Riggall, A., & Guerriero, S. (2021). Learning Loss, Learning Gains and Wellbeing: A Rapid Evidence Assessment. Education development trust.
Paul, R., Sarker, S., El Aouifi, H., Hussain, S., Baruah, A. K., & Gaftandzhieva, S. (2025). Analyzing dropout of students and an explainable prediction of academic performance utilizing artificial intelligence techniques. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1698505
Roman, N. V., Davidse, P. E., Human-Hendricks, A., Butler-Kruger, L., & Sonn, I. K. (2022). School dropout: Intentions, Motivations and self-efficacy of a sample of South Africa youth. Youth, 2(2), 126-137.
Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and learning analytics for data-driven pedagogical decisions and personalized interventions in education. Technology, knowledge and learning, 1-31.
Salmon, O. O., & Elyjoy, M. M. (2025). Towards Integration of AI Driven Predictive Model in identifying At-Risk Students in Online Learning Platforms. African Journal of Computing, Data Science and Informatics (AJCDSI), 1(1), 87-109.
Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126(6), 5113-5142.
Tariq, R., Orozco-del-Castillo, M. G., Zamir, M. T., Ramirez-Montoya, M. S., & Wilberforce, T. (2025). Explainable artificial intelligence for predictive modeling of student stress in higher education. Scientific Reports, 15, Article 38375. https://doi.org/10.1038/s41598-025-22171-3
Tiukhova, E., Vemuri, P., Flores, N. L., Islind, A. S., Óskarsdóttir, M., Poelmans, S., Baesens, B., & Snoeck, M. (2024). Explainable learning analytics: Assessing the stability of student success prediction models by means of explainable AI. Decision Support Systems. https://doi.org/10.1016/j.dss.2024.114229
Togo, E., Mwase-Vuma, T. W., Thakwalakwa, C., Millar, E., Markiewicz, M., Kadzamira, E., Kayange, J. J., Mkombe, D., & Fehringer, J. (2025). The thought that I could continue with school got lost: A qualitative exploration of the impact of COVID-19 on the education and sexual and reproductive health of Malawi youth. Reproductive Health, 22, Article 33. https://doi.org/10.1186/s12978-025-01972-9
Tsakeni, M., Nwafor, S. C., Mosia, M., & Egara, F. O. (2025). Mapping the scaffolding of metacognition and learning by AI tools in STEM classrooms: A bibliometric–systematic review approach (2005–2025). Journal of Intelligence, 13(11), 148.
Waseem, M., Abidin, S., Alam, M., Husain, M. S., Khan, M. Z., & Ashraf, Z. (2026). VertiDaX: An explainable hybrid deep learning model for dropout prediction with vertical integration. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3718
Zanellati, A., Zingaro, S. P., & Gabbrielli, M. (2024). Balancing performance and explainability in academic dropout prediction. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/TLT.2024.3425959
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
License and Publishing Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities tacitly or explicitly of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and publishing agreement.
Copyright
Authors who publish with JK agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
-
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
