The DOI matches its Crossref record.
Harnessing generative AI to boost active retrieval and retention in MOOCs with spaced repetition
Open accessAbstract
This study explores the use of artificial intelligence to improve active retrieval and retention in Massive Open Online Courses (MOOCs). We developed an AI-driven system that generates learning cards to facilitate active recall and utilize spaced repetition. The system was tested with a group of students and instructors, and the results showed significant improvements in memory retention and overall satisfaction. Expert reviews and participant feedback highlighted the system’s effectiveness in producing accurate and relevant learning cards. Despite promising outcomes, challenges with personalization and language complexity were identified, which will be addressed in future work. This research underscores the potential of AI-driven tools to enhance online learning, offering scalable and personalized solutions that can improve educational outcomes and engagement in MOOCs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Emotional Artificial Intelligence in Education: A Systematic Review and Meta-Analysis
Zhang, H., Liu, Y., Jiang, M., Chen, J., Wang, M., & PAAS, F. G. W. C. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10086-4 · Cited by 58
Assessing the adoption of ChatGPT as a learning tool for college students: An ethical perspective
Ma, W. W. K. (2026) · Knowledge Management & E-Learning An International Journal · doi:10.34105/j.kmel.2026.18.008 · Cited by 1
Espinel, J. C. D., Casquete, A. L. G., & Garcet, Y. B. (2025) · Revista Española de Educación Médica · doi:10.6018/edumed.685191
Durotunnasihah, D., Sujarwati, I., & Kasmaini, K. (2026) · Journal Of Education And Teaching Learning (JETL) · doi:10.51178/jetl.v8i2.3359
Analysis of Student Retention in Grade XI at MAN 1 Makassar in Biology Learning
Janna, M., Palennari, M., & Bahri, A. (2026) · Biology Teaching and Learning · doi:10.35580/btl.v9i1.76819
Metadata
- Authors
- Bachiri, Y.-A., Mouncif, H., & Bouikhalene, B.
- Affiliations
- Sidi Mohamed Ben Abdellah University (MA)
- Université Sultan Moulay Slimane (MA)
- Venue
- Knowledge Management & E-Learning An International Journal
- Published
- 2025-08-18
- Pages
- 391
- ISSN
- 2073-7904 (ISSN-L)
- OpenAlex
- W4413301136
- Cited by
- 3
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Knowledge Management & E-Learning An International Journal (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, active recall, spaced repetition, memory retention, MOOCs, learning cards, personalized learning, online learning, student satisfaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
