Mahir Akgun, Sacip Toker (2026) — 27th International Conference on AI in Education 📄 Full text (arXiv)
Akgun and Toker (2026) examine whether learning gains from GenAI-enabled adaptive pretesting persist over a seven-week retention period. Undergraduate participants completed adaptive AI-assisted pretesting, received instruction, took a baseline assessment, and were randomly assigned to three follow-up conditions: adaptive spaced retrieval practice, fixed spaced retrieval practice, or learner-directed AI-supported study. Multivariate analyses showed significant effects of condition on posttest performance and practice effort, with both retrieval-based conditions significantly outperforming learner-directed study. The findings indicate that while AI-adaptive pretesting can elevate initial understanding — especially for higher-order reasoning — sustained learning critically depends on how subsequent AI-supported practice is structured. This RCT contributes to the adaptive-learning literature by showing that initial gains are fragile without structured retrieval, and has practical implications for designing personalized-learning sequences that maximize learning-gains and promote ai-learning-transfer.
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