<![CDATA[Purpose of the study: This study aims to map the research trends, intellectual structure, and emerging themes related to generative artificial intelligence (GenAI)-supported problem-solving in higher education. The study also identifies dominant contributors, thematic developments, collaboration patterns, and future research directions within this rapidly growing field. Methodology: This study employed a bibliometric analysis approach using 458 Scopus-indexed articles published between 2021 and 2026. Performance analysis and science mapping techniques were conducted using VOSviewer and Biblioshiny from the Bibliometrix package in RStudio. The analyses included co-occurrence, co-authorship, citation, overlay visualization, and thematic mapping to explore the knowledge structure of the field. Main Findings: The findings revealed a rapid increase in publications after 2023 following the emergence of ChatGPT and large language models (LLMs). China and the United States dominated publication productivity and citation impact. Keyword and thematic analyses identified problem-solving, students, and teaching as the main driving themes, while ChatGPT, AI literacy, metacognition, and human-AI collaboration emerged as rapidly developing research topics in higher education. Novelty/Originality of this study: This study provides one of the first comprehensive bibliometric mappings specifically focused on the intersection of GenAI and problem-solving in higher education. Unlike previous studies that broadly examined AI in education, it reveals the intellectual structure, thematic evolution, collaboration patterns, and emerging research directions in this field. The findings provide valuable insights for lecturers, educational evaluators, and curriculum developers in identifying emerging competencies, assessment priorities, and pedagogical strategies related to problem-solving and AI-enhanced learning in higher education.]]>
Copyrights © 2026
