Collaborative AI-Integrated Model for Reviewing Educational Literature

The increasing complexity of networked research demands approaches that combine rigor, efficiency, and collaboration. In this context, artificial intelligence (AI) emerges as a strategic ally in the analysis and organization of scientific literature, facilitating the construction of a robust state-of-the-art framework to support decisions. The present study focuses on evaluating a model for the use of AI that facilitates collaborative literature review by integrating AI tools. The present study employed a descriptive, non-experimental, cross-sectional design. Participants (N = 10) completed a purpose-built questionnaire comprising twenty-five indicators on specific aspects of the model’s use. The participants indicated a high level of knowledge regarding ICT use (M = 8.3; SD = 1.25). The results showed that the System Usability Scale for the tools demonstrated variability; Google Drive scored the highest (M = 77.75; SD = 11.45), while Rayyan.AI scored the lowest (M = 66.00; SD = 20.69). While the findings indicated that AI enhances the efficiency of documentary research and the development of ethical and digital competencies, the participants expressed a need for further training in AI tools to optimize the usability of those integrated into the model. The proposed model CAIM-REL proves to be replicable and holds potential for collaborative research.