Global Educator Typologies for ChatGPT Adoption: Data-Driven Insights into Support Gaps and AI-Enhanced Teaching
Bibliographic record
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 $(\mathrm{n}=318$ 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 $3.0 \times$ more likely to be Sceptics $(95 \%$ CI [1.5, 6.2]), while European faculty are $2.7 \times$ 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 $(66 / 100; F(2,138) =3.69, p=0.027, \eta^{2}=0.05$; 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".