Adaptive Learning Technologies and Educational Innovation for Future Skill Development

Technology and the economy are evolving at a rapid rate, and this is challenging conventional curricula and the way subjects are taught. AI-powered adaptive learning technologies offering personalized content, pacing, and feedback, based on an artificial model of the learner, are increasingly recommended as a solution. This conceptual paper reviews the latest literature on adaptive learning and proposes a comprehensive framework for the future development of skills. It brings cognitive theories into technologies for learner modeling, content sequencing, a four-level adaptive architecture with governance and interoperability protections, and future-ready competencies into algorithmic intervention pathways. It then delves into questions related to institutionality, inefficiency in teaching and measurement of skills, algorithmic bias, and the dehumanization of teaching and the ethics of education surveillance. The paper ends with suggestions for practitioners and policymakers and an agenda for further research.