The rapid integration of artificial intelligence (AI) into educational systems has generated significant interest in its potential to transform instructional effectiveness. Empirical evidence comparing AI-assisted learning with traditional teaching methods remains limited. This study investigates the impact of AI-supported instructional models on student academic performance and engagement using a controlled quantitative experimental design. A total of 150 students were assigned to three instructional conditions: AI tutoring, blended AI–human instruction, and traditional classroom teaching. Academic performance and engagement were measured following a structured instructional intervention. Independent sample t-tests and one-way ANOVA were applied to evaluate statistical differences across groups. Results indicate that AI-assisted environments significantly outperform traditional instruction in both performance and engagement metrics, with blended AI–human instruction producing the strongest outcomes. The findings provide robust quantitative evidence supporting the integration of AI as a collaborative educational tool rather than a replacement for educators. This study contributes to evidence-based educational innovation by demonstrating that strategically implemented AI systems enhance learning effectiveness while preserving the essential role of human pedagogy.
Nethravathi K. (2026) studied this question.