Research Trend and Impact on Student Learning using Artificial Intelligence-Based Emotion Recognation: Systematic Bibliometric Analysis

Emotions have a significant function in the learning process. Good emotions such as enjoyment and curiosity can increase attention and comprehension, whereas negative emotions such as fear and boredom might impair academic achievement. Therefore, identifying and managing students' emotions is a critical step in developing an effective learning environment. In recent years, there has been a substantial progress in the application of artificial intelligence to detect emotions through facial expression analysis. Systematic literature reviews based on bibliometric analysis of artificial intelligence-based emotion recognition studies are still difficult to locate. This paper aims to undertake bibliometric and visualization analyses with VOSviewer for artificial intelligence-based emotion recognition. The evaluation includes 230 scholarly publications on emotion detection investigations indexed by Scopus quartiles Q1–Q4 from the Scopus database in the recent decade, namely 2014–2024. This bibliometric analysis has major implications for increasing academic, practical, and policy-related elements of artificial intelligence-based emotion recognition in education. By recognizing trends and major contributions in this subject, researchers, practitioners, and policymakers may work together to develop a more flexible and supportive learning environment, thereby boosting the overall quality of education.