Blended Learning based Artificial Intelligence Mapping to Optimize Learning Performance

• Published in: 2023 XIII International Conference on Virtual Campus (JICV) pp. 1 - 4
• Main Authors: Pranolo, Andri, Syafitri, Andini Isti, Sularso, Ying, Haihua, Nuryana, Zalik, Pratolo, Bambang Widi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.09.2023
• Subjects: Artificial Intelligence; Bibliometrics; Blended learning; Collaboration; Decision making; Education; Learning (artificial intelligence); Learning environment; Soft sensors
• DOI: 10.1109/JICV59748.2023.10565725

Blended Learning is gaining popularity as artificial intelligence and technology are integrated into education. This research provides a bibliometric analysis of Blended Learning based Artificial Intelligence in Education. This study employs bibliometrics to identify field tendencies, influential authors, highly-cited articles, and theme clusters. Data collection used Scopus.com to select relevant sources that found 439 documents. The scholarly articles and conference papers are analyzed to disclose publication patterns and research output for Blended Learning-based AI in Education. The research show lists the top publication country, prolific authors, and highly cited publications determined by bibliometric analysis. The research shows that Blended Learning and AI education research are interdisciplinary; consequently, co-authorship networks and collaborative patterns are investigated. Thematic clusters and term co-occurrence maps uncover themes and emerging trends. These insights assist in identifying research gaps, opportunities, and prospective fields of study. This research promotes Blended Learning based AI in education. To summarize, AI-enhanced learning environments by recognizing researchers and comprehending the field's evolution. The findings assist academics, educators, and policymakers collaborate on AI education development and make evidence-based decisions.