Developing an AI-Based Psychometric System for Assessing Learning Difficulties and Adaptive System to Overcome: A Qualitative and Conceptual Framework

This working paper proposes a conceptual framework for an adaptive AI-based virtual tutor system that integrates psychometric assessments to identify and support K-12 students with learning difficulties (dyslexia, dyscalculia, ADHD) through personalized instruction using LLMs, visual generation models, and techniques like Bayesian knowledge tracing. The system aims to create individualized learning profiles and deliver targeted interventions based on students' cognitive abilities, learning styles, and academic skills.

Learning difficulties pose significant challenges for students, impacting their academic performance and overall educational experience. These difficulties could sometimes put students into a downward spiral that lack of educational resources for personalized support consistently led to under-accommodation of students special needs, and the student lose opportunities in the longer term academic and work development. This research aims to propose a conceptual framework for an adaptive AI-based vi

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