AI-Driven Student Feedback Systems: Implementing Machine Learning Models for Personalized Assessment and Learning Pathways

Abstract

The integration of AI in education has revolutionized student assessment and feedback systems, enabling personalized learning pathways tailored to individual needs. The opacity of AI-driven feedback mechanisms presents significant challenges in transparency, trust, and pedagogical alignment. XAI has emerged as a critical solution to enhance interpretability, ensuring that students and educators can understand, validate, and act upon AI-generated assessments. This chapter explores cutting-edge techniques for explainable AI in student feedback systems, including attention mechanisms in NLP, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). It also examines human-AI interaction, algorithmic authority, and ethical considerations in AI-driven assessments. Through case studies of personalized student evaluation platforms, this research highlights the practical implications of XAI in fostering transparency, engagement, and equity in learning environments. The findings underscore the necessity of integrating interpretable AI models that align with pedagogical frameworks, ensuring that AI serves as a collaborative tool rather than an autonomous decision-maker. By bridging the gap between AI interpretability and pedagogical decision-making, this work advances the development of ethical, transparent, and student-centric AI-driven feedback systems.