{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T07:44:55Z","timestamp":1782719095030,"version":"3.54.5"},"reference-count":77,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Behavioral Sciences"],"abstract":"<jats:p>Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021\u20132026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (\u03ba = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848\u20130.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL\u2019s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field\u2019s priority research needs.<\/jats:p>","DOI":"10.3390\/bs16071050","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T01:03:56Z","timestamp":1782349436000},"page":"1050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effectiveness of AI-Supported Game-Based Learning: A Systematic Review of Outcomes, Challenges, and Future Directions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4686-3106","authenticated-orcid":false,"given":"\u0130smail","family":"Ka\u015farc\u0131","sequence":"first","affiliation":[{"name":"Faculty of Education, Eski\u015fehir Osmangazi University, 26040 Odunpazar\u0131, Eski\u015fehir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4732-6879","authenticated-orcid":false,"given":"Ey\u00fcp","family":"Yurt","sequence":"additional","affiliation":[{"name":"Faculty of Education, Bursa Uluda\u011f University, 16059 Nil\u00fcfer, Bursa, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1526595","DOI":"10.1155\/hbe2\/1526595","article-title":"Can hypercasual VR games (HVRGs) improve experience and performance of adult learners? 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