Citation

Izani, M. and Razak, A. and Fouda, Basma and Alkhalidi, Abdulsamad and Alkaabi, Sultan Ahmed and Saajid, Mehdi (2025) Global Educator Typologies for ChatGPT Adoption: Data-Driven Insights into Support Gaps and AI-Enhanced Teaching. In: 2025 10th International Conference on Information Technology Trends (ITT), 06-07 November 2025, Dubai, United Arab Emirates.

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Abstract

Generative-AI tools such as ChatGPT are spreading rapidly through higher education, yet instructor uptake remains uneven and poorly characterised. Leveraging the openly licensed ChatGPT Teacher Survey (n=318 instructors, 25 countries, six continents), this study delivers a quantitative typology of educator responses and pinpoints the institutional factors that shape them. Four adoption indicators-prior exposure, perceived curriculum impact, perceived assessment impact, and institutional-support adequacy-were z-standardised and clustered via k-means. The three-cluster solution (silhouette =0.23; Calinski-Harabasz = 99.4) yielded AI Enthusiasts (24 %), Cautious Integrators (40 %), and Sceptics (36 %). Multinomial-logistic analysis with HC3-robust errors shows region is the only significant predictor: instructors in Australasia-Asia are 3.0× more likely to be Sceptics (95% CI [1.5, 6.2]), while European faculty are 2.7× more likely to be Integrators (CI [1.2, 6.1]); gender and teaching experience are non-significant. Support perceptions diverge sharply— 0 % of Enthusiasts versus 46 % of Integrators and 84% of Sceptics report inadequate institutional backing. Performance evaluations amplify this divide: in grading ChatGPT answers (n=141), Enthusiasts award a mean mark of 78/100, significantly higher than Integrators (66/100;F(2,138)=3.69,p=0.027,η2=0.05; Cohen’s d=0.48). Robustness checks-split-sample validation (Adjusted Rand =0.86), hierarchical clustering, and alternative imputation-confirm solution stability. These findings expose a substantial support gap for three-quarters of educators and demonstrate that regional context, not personal demographics, drives adoption stance. The typology framework offers actionable personas for targeted professional development and typology-aware AIsystem design; all code and derived data are openly shared for replication.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Generative AI, ChatGPT, Higher Education, Educator Typology, Cluster Analysis, Adoption Patterns, Institutional Support, Regional Differences, AI in Education, Machine Learning Applications
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Apr 2026 03:17
Last Modified:	20 Apr 2026 03:17
URII:	http://shdl.mmu.edu.my/id/eprint/15765

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