Teaching and learning effectiveness in the AI era: A bibliometric analysis of educational trends

Abstract

Background/purpose.    Teaching and learning effectiveness has become an increasingly important issue in contemporary education, particularly due to the rapid integration of digital technology and artificial intelligence in learning environments. This study aimed to analyse global research trends, dominant themes, and emerging issues related to teaching and learning effectiveness during 2020–2026 through bibliometric mapping analysis.

Materials/methods.    This study employed a bibliometric approach using data retrieved from Google Scholar through the Publish or Perish application. A total of 500 publications were initially collected, and 450 relevant publications were selected for analysis. The data were exported in RIS and CSV formats and analysed using Microsoft Excel and VOSviewer to identify publication trends, citation patterns, and co-occurrence networks of research terms.

Results.  The findings revealed a significant increase in publications on teaching and learning effectiveness, particularly in 2024 and 2025. Highly cited studies were dominated by topics related to blended learning, artificial intelligence, student engagement, and educational technology. Network visualization identified several dominant research clusters, including blended learning environments, artificial intelligence integration, learner engagement, teaching effectiveness, gamification, and systematic review studies. Overlay visualization indicated that artificial intelligence, ChatGPT, and hybrid learning represent emerging research trends in recent years.

Conclusion.    Research on teaching and learning effectiveness is increasingly oriented toward technology-enhanced, learner-centered, and AI-supported educational practices. The findings highlight the growing importance of adaptive and innovative instructional strategies in improving educational quality and student learning outcomes in the digital era.