Bibliometric Analysis of Research Trends on STEM-Based AI Chatbots in Energy Materials to Improve Students' Conceptual Understanding

Abstract

The integration of STEM‑based AI chatbots in energy education has emerged as a promising approach to enhance students' conceptual understanding. However, a systematic mapping of the research landscape in this interdisciplinary field is still lacking. This study conducts a bibliometric analysis to map research trends on STEM‑based AI chatbots for improving conceptual understanding in energy education. The analysis covers publications indexed in the Scopus database from 2022 to 2026. A total of 343 documents were analyzed using Bibliometrix (an R‑based package) to identify publication trends, most frequent keywords, and most relevant sources. The results show a negative annual growth rate of -16.4%, indicating a recent decline in publication output. The most frequent keywords include "engineering education", "students", "curricula", "STEM education", "teaching", "renewable energy", and "sustainable development". The most productive source is the ASEE Annual Conference and Exposition (52 documents). The findings confirm that the research focus is on engineering pedagogy, learner‑centered approaches, and sustainability themes. However, the decline in growth and low average citations (2.706 per document) suggest that the field is still emerging and may be shifting toward new AI technologies. This study provides a recent baseline for researchers, educators, and policymakers interested in AI‑driven STEM education for energy topics. Future research should expand data sources, investigate the causes of declining publication growth, and conduct empirical studies to validate the effectiveness of chatbot interventions.