From Assistance to Autonomy: AI Integration in Structured Research-Based Learning for Higher Education

Despite the growing interest in artificial intelligence (AI) for science education, little is known about its role within structured research-based learning (RBL) frameworks that balance technological assistance with developing independent research competencies. Existing studies often focus on AI as an isolated tool or a single-stage intervention, leaving a gap in understanding how AI can be systematically embedded across the research process without diminishing students’ cognitive engagement. This study addresses that gap by implementing the newly developed IFTAR model, which organizes RBL into five sequential phases—Identification, Find Literature, Determine Methodology, Accommodate/Analyze/Interpret Data, and Report & Present—with AI selectively integrated into the literature search and data analysis stages. A quasi-experimental, non-equivalent control group PreTest–PostTest design was conducted with ninety undergraduate physics education students assigned to one control and two experimental groups. Cognitive outcomes were measured using a validated instrument and analyzed through classical ANCOVA, rank-based ANCOVA, and robust ANCOVA to account for assumption violations. Across all analytical approaches, both experimental groups significantly outperformed the control group, with no significant difference between the experimental conditions. These findings demonstrate that phase-specific AI integration within a transparent and scaffolded RBL framework can enhance cognitive performance while preserving methodological autonomy, offering a replicable model for purposeful AI use in STEM higher education.