This systematic review synthesizes 54 peer-reviewed studies published between 2019 and 2025 that examine how artificial intelligence (AI) and extended reality (XR) technologies are integrated to support adaptive and personalized learning. The studies were analyzed across multiple dimensions, including learning contexts, AI applications, adaptive input parameters, software and hardware used, and evaluation methods. The findings indicate growing research interest in AI–XR integration, with the majority of studies focused on procedural training and STEM education. Across these studies, AI is frequently used in multifaceted roles, most notably as a provider of real-time adaptive feedback, conversational agent, and a generator of instructional content. Despite these promising developments, the review identifies several critical limitations. While generative AI, particularly large language models (LLMs) such as GPT, has been widely used for conversational interactions, learner profile data remains largely underutilized. Inputs such as prior knowledge and motivation are rarely incorporated. Most implementations rely on a single adaptive strategy, typically driven by performance-based measures such as pre-quiz scores or task completion. As a result, they do not fully exploit the multimodal sensing capabilities of XR platforms (e.g., eye tracking, gesture recognition, environmental tracking), which could support context-sensitive, dynamically generated 3D content aligned with when, where, and how learners need support. Current evaluations of AI–XR systems also remain dominated by short-term performance outcomes, with limited attention to knowledge transfer and critical thinking. These findings highlight key opportunities for designing context-aware, learner-centered AI–XR systems and call for future research that more fully leverages multimodal data, incorporates richer learner profile information, and is grounded in explicit pedagogical models.
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