AI-Supported Reflective Learning Environments: Toward Psychologically Informed Educational Design Using the PersonaMatrix Framework

This paper explores the application of the PersonaMatrix framework to the design of AI-supported reflective learning environments. Drawing on principles of developmental psychology, archetypal modeling, and adaptive instruction, the study proposes a conceptual architecture for educational systems that integrate psychologically informed AI components. The framework addresses how AI tutors can support reflective pedagogy, accommodate individual learner profiles based on psychodiagnostic data, and facilitate developmental learning pathways. Implications for EdTech design, learning analytics, and the evaluation of educational AI tools are discussed, with particular attention to psychologically sensitive and developmental contexts.