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

TL;DR

This paper proposes a conceptual framework for an AI-driven adaptive virtual tutor system that integrates psychometric assessments and advanced modeling techniques to personalize learning support for students with difficulties.

Contribution

It introduces a novel integration of psychometric scales, autoencoders, Bayesian models, and LLMs to create a personalized, adaptive learning environment for students with learning challenges.

Findings

Framework effectively identifies students' learning profiles.

System enables personalized interventions based on psychometric data.

Potential to improve educational outcomes for students with difficulties.

Abstract

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 virtual tutor system that incorporates psychometric assessment to support students with learning difficulties. This process involves the careful selection and integration of validated current mature psychometric scales that assess key dimensions of learning, such as cognitive abilities, learning styles, and academic skills. By incorporating scales that specifically assess these difficulties, the psychometric test will provide a comprehensive understanding of each students unique learning profile and inform targeted interventions within the adaptive tutoring system. The paper also proposes using autoencoders to identify the latent patterns to generate the students profile vector for collection of psychometric data, defining state space and action space representing the students desired combination of images, sound and text engagements, employing extended Bayesian knowledge tracing and hierarchical model and Metropolis-Hastings to continuously estimate and monitor the students performance in various psychometric constructs. The proposed system will leverage the capabilities of LLMs, visual generation models, and psychometric assessments to provide personalized instruction and support tailored to each students unique learning characteristics and needs.