Artificial intelligence is reshaping education, yet research remains fragmented across technological streams and often conflates technical capability with demonstrated educational effectiveness. This narrative review synthesizes representative literature published mainly between 2010 and 2026 to trace the evolution of AI-enabled education from computer-assisted instruction toward adaptive, immersive, generative, and potentially more general forms of intelligence. It compares three major technological streams: intelligent tutoring and adaptive learning systems, extended reality (XR), and large language model-based generative AI. The review finds that intelligent tutoring systems support learner modeling, adaptive feedback, and personalization, but their evidence base is methodologically heterogeneous. XR shows comparatively more consolidated evidence, particularly through systematic reviews and meta-analyses, with generally positive but context-dependent effects on engagement and learning outcomes. Generative AI expands dialogic explanation, scaffolding, content generation, and personalized support, although learner-outcome evidence remains limited relative to capability benchmarking. Building on these findings, the review proposes a Technology–Mechanism–Outcome–Challenge framework and argues that future progress toward artificial general intelligence (AGI) should be evaluated through learning mechanisms, measurable outcomes, human oversight, equity, and responsible governance.

Cyber-Syndrome denotes the physical, social, and thinking disorders arising from problematic cyberspace interactions, with its thinking dimension encompassing cognitive deficits (e.g., attentional impairment, executive dysfunction) and affective disturbances (e.g., mood disorders, emotional dysregulation). Conventional assessment approaches, relying primarily on self-report questionnaires and laboratory-based tasks, are unable to capture the rapid, context-dependent fluctuations of mental states in real-world digital environments. Leveraging advances in computational psychiatry, affective computing, and machine learning, this paper presents a scoping review of computational approaches for inferring cognitive and affective states of Cyber-Syndrome from digital behavior. It situates Cyber-Syndrome within the Cyber-Physical-Social-Thinking (CPST) space framework, reviews the principal data sources, and covers the computational methods used to analyze them. It then synthesizes evidence from adolescent studies and examines the research institutions and open-source platforms that have driven methodological progress in the field. Finally, it discusses key challenges and outlines future directions essential for clinical translation and real-world deployment, particularly in adolescent mental health.

Cyber-Syndrome denotes the physical, social, and thinking disorders arising from problematic cyberspace interactions, with its thinking dimension encompassing cognitive deficits (e.g., attentional impairment, executive dysfunction) and affective disturbances (e.g., mood disorders, emotional dysregulation). Conventional assessment approaches, relying primarily on self-report questionnaires and laboratory-based tasks, are unable to capture the rapid, context-dependent fluctuations of mental states in real-world digital environments. Leveraging advances in computational psychiatry, affective computing, and machine learning, this paper presents a scoping review that maps, rather than validates, the computational approaches available for inferring the cognitive and affective states of Cyber-Syndrome from digital behavior. Because the construct still lacks established diagnostic instruments, the review is positioned as an assembly of the evidence base that its future operationalization will require rather than as a validation of it. It situates Cyber-Syndrome within the Cyber–Physical–Social–Thinking (CPST) space framework, reviews the principal data sources, and covers the computational methods used to analyze them. It then synthesizes evidence from adolescent studies and examines the research institutions and open-source platforms that have driven methodological progress in the field. Finally, it discusses key challenges and outlines future directions essential for clinical translation and real-world deployment, particularly in adolescent mental health.