A GenAI-Based Adaptive Tutoring ana Intelligent Assessment Framework for Personalized Learning

Abstract

Modern academic institutions face a fundamental gap: instruction is designed for the average student, leaving individuals with specific weaknesses without any targeted support mechanism. Standardised course pipelines and identical assessments for all students have consistently failed to close this instructional gap. Advances across Artificial Intelligence (AI), Natural Language Processing (NLP), and Generative AI now make it feasible to construct learning environments that actively evolves as each student progresses [1]. This paper introduces EduMind, a unified tutoring and assessment platform designed around a dual-track evaluation model. Closed- form questions are evaluated using fixed-logic scoring for consistent results, while open-ended answers are assessed by computing meaning-level correspondence with expert reference responses. The combined output enables fine-grained identification of both proficient and deficient knowledge areas at the topic level [2][3]. EduMind's embedded Generative Al module transforms evaluation data directly into targeted instructional content, addressing identified gaps with structured explanations and study material packaged into downloadable PDF reports. The system demonstrates how assessment and tutoring can be unified into a seamless workflow, and remained operationally stable throughout all testing phases.