AIEAM: An AI-Enhanced Adaptive Learning Framework for Personalized Education in Higher Education

This study proposes AIEAM, an AI-Enhanced Adaptive Learning Framework designed to support personalized education in higher education. The framework integrates cognitive, metacognitive, and ethical components into a modular architecture comprising five core modules: learner profiling, adaptive path generation, real-time feedback and assessment, resource recommendation, and an ethical and trust layer. Grounded in established learning theories, AIEAM addresses the limitations of existing systems that often lack pedagogical transparency, multidimensional adaptivity, and ethical oversight. To validate the framework, two real-world systems—Squirrel AI and Carnegie Learning (MATHia)—are analyzed against AIEAM’s design principles. The analysis reveals strong alignment in functional components while highlighting gaps in metacognitive support and ethical integration. AIEAM contributes both a conceptual model and practical roadmap for developing adaptive learning environments that are scalable, interpretable, and pedagogically sound. The framework provides a foundation for future research and implementation strategies in AI-supported higher education learning systems.