Design of a Framework for Integrated Evaluation Model of Metacognition and Deeper Learning in the Perspective of AIED

This paper proposes a creative, AI-driven integrated learning evaluation model that assesses metacognition and deeper learning through multimodal data analysis. Existing research faces challenges in learning data effectiveness, limited measuring tools and methods, and absence of feedback optimization loop. To address these issues, our ubiquitous multidimensional model integrate conscious and unconscious learning data using hybrid reasoning neural networks, generating interpretable representations aligned with metacognitive and deeper learning elements. This approach enables comprehensive assessment, automated feedback, and iterative optimization to enhance students' selfregulation and support students' personal learning needs. By advancing AI in Education (AIED), our integrated evaluation model explores new path for dynamic educational interventions and personalized pedagogies. This research contributes to the field by addressing validity issues, integrating qualitative and quantitative methods, and the loop with feedback optimization.