Reconceptualizing learning sciences in the era of artificial intelligence: Toward hybrid intelligence and adaptive learning systems.

PURPOSE: Artificial intelligence (AI) is rapidly transforming education through intelligent tutoring systems, adaptive learning platforms, learning analytics, and large language models. However, research on Learning Sciences, AI in Education, learning analytics, adaptive learning systems, and Hybrid Intelligence has largely progressed across partially separate research streams, resulting in fragmented theoretical perspectives and limited conceptual integration. This systematic review aims to synthesize the existing literature and develop an evidence-informed Hybrid Intelligence Framework that explains the relationships among Learning Sciences, AI technologies, learning analytics, and adaptive learning systems. DESIGN/METHODOLOGY/APPROACH: A qualitative systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. Literature published between 2004 and March 2026 was retrieved through a focused, intersection-based search across seven databases: Scopus, Web of Science, ScienceDirect, SpringerLink, Wiley Online Library, ACM Digital Library, and Google Scholar. Following duplicate removal and eligibility screening, 32 studies met the inclusion criteria. Data were synthesized using Braun and Clarke's six-phase thematic analysis. FINDINGS: Four interrelated themes emerged from the thematic synthesis: (1) epistemological fragmentation within Learning Sciences, (2) theory-technology contradictions between pedagogical theories and AI-supported educational systems, (3) the datafication of learning through learning analytics and adaptive technologies, and (4) Hybrid Intelligence as an emerging educational paradigm emphasizing collaboration among learners, educators, and intelligent systems. Building on these themes, the review proposes a Hybrid Intelligence Framework that integrates Learning Sciences, Artificial Intelligence, Learning Analytics, and adaptive learning systems into a coherent conceptual model for human-centred, ethical, explainable, and evidence-informed AI-supported education. ORIGINALITY/VALUE: This review advances the literature by moving beyond isolated examinations of AI technologies or learning theories to provide an integrative conceptual synthesis of their interrelationships. The proposed Hybrid Intelligence Framework offers a theoretically grounded foundation for future empirical research, instructional design, educational policy, and the development of adaptive learning environments that integrate human expertise with artificial intelligence while supporting learner agency and pedagogical integrity.