How is generative AI being positioned in higher education practice? A theory-informed hybrid bibliometric evidence map of pedagogical functions, domain patterns, and opportunity–risk framing
Quality and Quantity, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11135-026-03034-z
- Dergi Adı: Quality and Quantity
- Derginin Tarandığı İndeksler: Scopus, IBZ Online, ABI/INFORM, Index Islamicus, Political Science Complete, Psycinfo, Political Science Abstract (IPSA), Social Science Premium Collection (ProQuest), Health Research Premium Collection (ProQuest), Sociology Database (ProQuest), Sociology Source Ultimate (EBSCO)
- Anahtar Kelimeler: Curriculum design, Evidence mapping, Generative AI, Higher education, Pedagogical affordances
- TED Üniversitesi Adresli: Evet
Özet
While Generative Artificial Intelligence (GenAI) in higher education is an emerging field growing at a rapid pace, research remains in an early phase and is still scattered across different analytical and pedagogical interpretations. Literature still provides a less developed account of how the literature positions GenAI as a pedagogical actor across practice-oriented higher education contexts. Addressing this gap, this study reports on a theory-informed hybrid bibliometric evidence-mapping study on GenAI in higher education. A classical bibliometric analysis was combined with metadata-derived automated content analysis. A comprehensive search on the Web of Science Core Collection and Scopus resulted in a final corpus of 1695 unique publications. While the field was highly concentrated with a marked concentration of scholarship in the fields of medicine and health sciences, results indicate that a dominant pedagogical focus on assessment and feedback prevails. The results on secondary functions, however, reveal considerable differences between the domains. The findings also suggest an opportunity-dominant framing of GenAI and a strong empirical quantitative orientation. The study, thus, provides a practice-sensitive and more analytically nuanced view on how GenAI is being integrated into higher education. Importantly, findings reveal that GenAI is not treated as one uniform educational technology, but rather as being integrated through specific pedagogical affordances in different domains. Implications for this research include ideas for designing curricula on AI and related instructional strategies for responsible adoption of AI.