Effect of AI-Generated Immediate Feedback on Student Hybrid Learning, Engagement, and Knowledge Retention

Artificial Intelligence (AI) integration in education has redefined how feedback is utilized. This quasi-experimental study (N = 180) examined the effect of AI-generated immediate feedback on hybrid learning, engagement, and knowledge retention across six Nigerian tertiary institutions. Data were gathered over 14 weeks using a validated Student Engagement Questionnaire ( ), Computer-Based Tests, and longitudinal LMS analytics. Findings show the AI-assisted group significantly outperformed the traditional group across all metrics (p < 0.001), achieving higher academic performance (78.6% vs. 62.4%), engagement (4.18 vs. 3.24), retention (4.35 vs. 3.02), and conceptual mastery (81.3% vs. 58.2%). A very large effect size for mastery (d = 2.28) proves that adaptive AI prompts drive deep content comprehension and eliminate grading delays. The study contributes to knowledge by extending feedback and mastery learning theories to the Global South, proving that advanced educational technology is scalable and highly effective within resource-constrained environments using a replicable mixed-methods framework. It is recommended that institutions adopt AI-assisted hybrid models with structured faculty training. Instructors should blend automated feedback with metacognitive prompts, while developers must enhance natural language processing, embed explainable AI (XAI), and maintain strict data privacy compliance to ensure algorithmic fairness.