What question did this study set out to answer?

This study aims to examine how AI-generated immediate feedback affects hybrid learning experiences, student engagement, and knowledge retention.

July 30, 2026Iconic Research and Engineering Journals0 citations

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

EFEdward Stephen Onyema, PhD, C.Eng, MIEEE, FIPMD PUPhD Ejimofor Ihekeremma A. U.OCOkonkwo Oxford Collins, Msc, C.Eng

Key Points

This study aims to examine how AI-generated immediate feedback affects hybrid learning experiences, student engagement, and knowledge retention.
Quasi-experimental design with N=180 students from six Nigerian tertiary institutions.
Data collection over 14 weeks using surveys, computer-based tests, and LMS analytics.
Analysis of academic performance, engagement scores, and retention metrics among AI-assisted and traditional learning groups.
AI-assisted group achieved 78.6% academic performance compared to 62.4% in the traditional group (p < 0.001).
Engagement scores were significantly higher in the AI group (4.18) versus the traditional group (3.24).
Retention rates were 4.35 for AI-assisted learning and 3.02 for traditional methods.

Abstract

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.

Ask AI

Helpful

Bookmark

View Full Paper