Artificial intelligence and active b-learning methodologies in higher education: a bibliometric analysis
TL;DR
A bibliometric analysis of recent literature on the integration of AI into active b-learning methodologies in the university context suggests that the convergence between AI and active learning represents a promising path for pedagogical innovation in Higher Education.
Abstract
Abstract: Artificial Intelligence (AI) emerged as a transformative technology in Higher Education, reshaping active and hybrid learning methodologies. This study conducts a bibliometric analysis of recent literature on the integration of AI into active b-learning methodologies in the university context. A six-stage bibliometric methodology was employed, using Scopus-indexed publications from 2015 to 2024. PRISMA criteria were applied for study selection, and VOSviewer, Bibliometrix, and Microsoft Excel were used to process and visualize the data. Results indicate exponential growth in scientific output related to AI and active methodologies, particularly from 2020 onwards. A total of 167 relevant documents were identified, with conference papers being the most frequent publication type. The dominant themes included personalized learning, intelligent gamification, automated flipped classrooms, and AI-supported project-based learning. The analysis also identified the most influential authors, journals, and documents, as well as key academic collaboration networks. Findings suggest that the convergence between AI and active learning represents a promising path for pedagogical innovation in Higher Education. However, several challenges remain, including ethical considerations, technical limitations, and teacher training. This study offers a comprehensive overview of the current state of research, highlighting the need for further investigation into pedagogical, ethical, and contextual impacts of digital transformation in education.