Reimagining Education: A Comparative Study of Artificial Intelligence and Large Language Model-based Learning Systems and Traditional Education

Abstract

This study presents a comparative analysis of Artificial Intelligence (AI) and Large Language Model (LLM)-based learning systems versus traditional education methods, focusing on comprehension, retention, personalization, and critical thinking. With AI and LLM technologies transforming instructional design, it is essential to examine their pedagogical effectiveness and implications for higher education. A convergent mixed-methods approach was employed, combining quantitative and qualitative data collected from 350 students and faculty across different Indian academic institutions. A structured survey assessed perceptions of learning effectiveness, engagement, and assessment accuracy across both systems. Findings indicate that nearly half of the respondents viewed AI/LLM-based systems as more effective in enhancing comprehension and retention, while a majority recognized their strength in personalization and adaptive feedback. Traditional methods, however, remained valuable for fostering mentorship, ethical awareness, and collaborative learning. The study highlights the complementary strengths of AI-driven and traditional approaches, suggesting that hybrid learning models can maximize educational outcomes by integrating technological adaptability with human-centred pedagogy. The study contributes empirical evidence on the comparative effectiveness of AI/LLM-based and traditional learning approaches and provides practical insights for educators, institutional leaders, and policymakers to support the ethical, effective, and sustainable adoption of AI-enabled learning in higher education.