Analyzing the Landscape of Generative and Open AI in Education: A Text Analytics Exploration of Scopus Publications

Generative artificial intelligence (GAI) fundamentally reshaped the educational landscape by introducing innovative tools that enhanced learning outcomes and experiences. In this paper, a thorough examination of key terms surrounding GAI in education was undertaken from Scopus literature through sophisticated text analytics techniques such as word cloud generation and semantic analysis. The most used terms in the findings were “learning,” “ChatGPT,” and “language models,” representing the growing prominence of AI-driven educational solutions. Sentiment analysis revealed that authors had positive opinions about GAI and its potential benefits in developing individual learning paths and automating routines. However, concerns regarding ethics and job displacement were also highlighted. The study outlined important academic centers for GAI research, with significant contributions from Stanford University and publications in outlets such as “Lecture Notes in Computer Science.” Geographically, countries like China and India emerged as relevant in GAI research. Further thematic exploration demonstrated a predominantly positive attitude toward GAI's capability to transform pedagogical methodologies. Therefore, this research underscored the importance of text analytics in understanding GAI's role in education and called for strategic partnerships to advance innovation and equity in the global educational landscape.