Research-Based Learning Integration with Artificial Intelligence Research Assistants to Enhance Research Competencies

This study aimed to design and validate a Research-Based Learning (RBL) management model integrating an Artificial Intelligence Research Assistant (AIRA) to enhance research competencies in higher education. Using a research and development approach, the study systematically synthesized literature on Research-Based Learning, AI-assisted research practices, research competency frameworks, and the 6M quality management model. Eight commonly identified RBL stages were analytically consolidated into a canonical five-stage process: Identifying Research Problems, Designing Research Plans, Implementing and Data Collection, AI-Supported Analysis and Interpretation, and Publishing and Reflecting. Artificial intelligence was embedded across all stages as a collaborative research assistant, providing cognitive, procedural, and analytical support while preserving human agency and ethical responsibility. To ensure feasibility and sustainability, the learning process was governed by the 6M framework: Man, Machine, Material, Money, Method, and Measurement, transforming the model into a comprehensive research learning management system. The proposed (6M) RBL-AI model was evaluated by experts to assess its conceptual validity, structural coherence, and practical applicability. The results indicated a very high level of overall suitability (M = 4.89, SD = 0.31), confirming the model’s robustness and feasibility for graduate-level research learning. The key contribution of this study lies in its process-level integration of AI into a research-based learning management framework, rather than in tool-level adoption. The model offers a scalable, pedagogically grounded approach to developing both core research competencies and AI-assisted research competencies in the AI era.