Blended peer-led research curriculum with AI integration improves postgraduate students’ academic performance and satisfaction: a quasi-experimental mixed-methods study

TL;DR

A blended, peer-led, AI-integrated research methods course improved postgraduate dental students' satisfaction and learning experience compared to traditional lectures.

Contribution

This study introduces a novel blended, peer-led, AI-integrated curriculum for postgraduate dental research education and evaluates its effectiveness using mixed methods.

Findings

Intervention students reported significantly higher satisfaction compared to the control group.

Qualitative feedback highlighted improved clarity, collaboration, and preparedness for thesis work.

Satisfaction was moderately correlated with academic performance in both groups.

Abstract

Research methods courses in health professions education are often perceived as abstract, didactic, and disconnected from practice, resulting in low engagement and limited skill transfer. Innovative pedagogies that integrate flipped learning, peer critique, and artificial intelligence (AI) may enhance learning but remain underexplored in postgraduate dental education. This study evaluated the effectiveness of a blended, peer-led, AI-integrated research methods curriculum compared with a traditional lecture-based approach.

A quasi-experimental mixed-methods study was conducted during the Winter–Summer 2025 semester at the Faculty of Dentistry, King Abdulaziz University. First-year postgraduate dental students enrolled in a research methods course were assigned to either an intervention group (n = 26) that received a blended curriculum incorporating flipped video lectures, gamified peer critiques, Critical Appraisal Skills Programme (CASP)-based appraisals, faculty-led discussions, AI-assisted feedback, and proposal development, or a control group (n = 26) receiving traditional lectures. Quantitative outcomes included academic performance, critique score, and student satisfaction (Likert survey). Qualitative data were collected through focus groups and reflective responses, analyzed thematically.

Intervention students reported significantly higher overall satisfaction than the control group (Mean ± SD: 4.35 ± 1.56 vs. 3.35 ± 1.89; p = 0.043; 95% CI: 0.06 to 1.94). Differences in critique scores (9.23 ± 1.12 vs. 8.77 ± 0.96) and total academic performance (92.21 ± 18.50 vs. 85.50 ± 22.30) favored the intervention group but did not reach statistical significance. Satisfaction was moderately correlated with academic performance (around r ~ 0.6 in both groups, p < 0.001). Qualitative findings provided explanatory insight into these patterns, highlighting perceived clarity of course organization, active and collaborative learning, and improved preparedness for thesis work, alongside challenges related to workload intensity and course logistics.

A blended, peer-led, AI-integrated research methods curriculum was associated with higher student satisfaction and more positive learning experiences compared with traditional lectures, while performance differences were modest. Mixed-methods integration revealed how engagement, structure, and perceived relevance shaped learning beyond what quantitative measures alone captured. Although resource-intensive, this model offers a promising framework for enhancing postgraduate research methods education. Further multi-institutional and longitudinal studies are needed to examine sustainability and generalizability.

The online version contains supplementary material available at 10.1186/s12909-026-08576-2.

Figures