Artificial Intelligence in Educational Games for Personalized Learning

Artificial intelligence can enable educational games to select activities, regulate progression, and deliver feedback in response to students’ difficulties. Successful assisted gameplay, however, does not establish independent mastery. This critical narrative and thematic review examines how adaptation should be interpreted and evaluated when independently demonstrated learning is the primary endpoint. Targeted web discovery documented in the original manuscript was supplemented through October 31, 2026. The selective corpus comprises 28 sources, including 11 game-specific empirical publications and three adjacent empirical studies; 22 sources (78.6%) date from 2021–2026. A contemporary systematic review provides broader field coverage, whereas the present contribution concerns independent mastery, over-assistance, uncertain learner-state inference, teacher supervision, and prospective evaluation. Selected primary studies show favorable, mixed, and null findings under different instructional conditions. Increased correctness need not indicate misconception repair, and strong fixed support can limit the incremental benefit of adaptation. Activities, pace, and feedback are therefore treated as distinct instructional decisions. An author-developed, unvalidated adaptation cycle and proposed metrics specify assistance exposure, severity of feedback errors, independent assessment, retention, transfer, implementation fidelity, and educational opportunity. No new empirical experiment or pooled effect estimate is reported. The proposed adoption criterion is retained and transferable learning after unnecessary assistance is withdrawn, alongside acceptable workload, accessibility, privacy, and student agency.