Beyond One-Size-Fits-All: GPT-Enabled Personalization of Academic Content for Neurodiverse Students

Abstract

This research examines the use of advanced Natural Language Processing (NLP) technology, specifically GPT-3.5/4 models, to enhance text-based learning for neurodiverse students within higher education. Recognizing that conventional pedagogical resources often fail to meet the distinct needs of learners with neurodevelopmental differences, our research explores the hypothesis that NLP-enabled personalization of academic content can facilitate effective, equitable, and engaging educational experiences for college students. We present AImpathizer, a tool designed to adapt academic content into representations more congruent with the cognitive styles of neurodiverse students. AImpathizer is designed using a user-centric approach, highlighting the challenges and needs of neurodiverse college students. AImpathizer aims to incorporate more accessibility modifications in comparison to traditional academic content. The outcomes of this research aim to provide empirical evidence supporting the integration of NLP tools into educational environments in synergy with Universal Design for Learning (UDL) framework, paving the way for more inclusive educational practices.

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