AI-Supported Gamification in E-Learning: A Systematic Review of Adaptive Architectures and Cognitive Outcomes

The rapid expansion of artificial intelligence (AI) in digital education has transformed gamification from a motivational strategy into a data-driven, adaptive learning paradigm. This systematic review conceptualizes AI-supported gamification as an information-centered ecosystem integrating learning analytics, behavioral modeling, adaptive algorithms, and intelligent feedback mechanisms to enhance cognitive development and critical thinking. Following PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, ScienceDirect, Google Scholar, and ResearchGate. Peer-reviewed empirical studies published between 2020 and 2025 were considered. Studies were included if they examined gamification in educational contexts with AI-driven or adaptive system components, while non-educational contexts, duplicates, and non-English publications were excluded. After screening and eligibility assessment, 100 studies were included in the final synthesis. The review examines how AI-driven personalization, neurotechnology, predictive modeling, and generative systems reshape the design and effectiveness of gamified e-learning environments. Architectural patterns identified include recommender systems, real-time behavioral adaptation, affect-aware feedback loops, and algorithmic content generation. Across the reviewed studies, AI-supported gamified systems were frequently associated with increased engagement and moderate improvements in executive functions, higher-order reasoning, and adaptive learning pathways. However, challenges related to system transparency, data governance, algorithmic bias, cognitive load management, and equitable access remain significant. The review was not registered. By framing gamification as an adaptive information system rather than solely a pedagogical intervention, this study proposes a structured taxonomy of AI-driven gamified architectures—including data acquisition, user modeling, predictive analytics, and adaptive feedback layers—and outlines research priorities for scalable, ethically grounded, and data-informed e-learning ecosystems.