Unlocking the Future of Education: Empirical Insights into the Adoption of Generative AI in Higher Education
Bibliographic record
Abstract
This study investigates the factors influencing the adoption and use of generative AI technologies (GenAI) in higher education through a comprehensive survey of 592 university professors across the USA and Canada, using both quantitative and qualitative data. The results reveal that educators primarily benefit from using GenAI to create course materials more efficiently and enhance students’ learning outcomes. However, significant concerns persist regarding the accuracy of AI-generated content and the privacy and security of data. The qualitative analysis further identified six common themes: efficiency and time-saving, creativity and innovation, engagement with technology, support in research and learning, skepticism or uncertainty, and contextual dependency. Our findings also indicate that perceived enjoyment and performance expectancy are the most crucial drivers for adopting GenAI, whereas perceived risk substantially deters educators from integrating these technologies. Additionally, age negatively affects use and amplifies the effect of perceived risk on intention; female educators report higher intention than males, with social influence exerting a stronger positive and perceived risk a stronger negative association with intention among female educators. This research substantially contributes to the Information Systems (IS) literature by empirically examining GenAI use in higher education with a large sample size. The study not only highlights the practical benefits and risks associated with GenAI but also provides a nuanced understanding of the psychological factors influencing educators’ decisions. These findings offer actionable insights for developers to address educators’ concerns and for educational institutions to develop strategies that facilitate the effective and responsible integration of GenAI technologies in academic settings.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".