Examining factors influencing university students’ adoption of generative artificial intelligence: a cross-country study
Bibliographic record
Abstract
The introduction of Generative Artificial Intelligence (GenAI) has transformed the way university students learn. To understand the factors that affect the adoption of GenAI among university students, we proposed a comprehensive research model based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2), along with personal factors customized for GenAI. We conducted a cross-sectional survey to collect data from university students in Malaysia and China through an online questionnaire, yielding a total of 500 valid responses. The data were analyzed using the Partial Least Squares method to assess the influence of various factors on GenAI adoption. Our findings reveal notable differences in the factors affecting GenAI adoption between the two countries, with the Malaysian group showing a more diverse range of influencing factors compared to the Chinese group. This study highlights the importance of considering country-specific differences when devising strategies for the adoption of GenAI. By integrating UTAUT2 with personal factors and conducting a cross-country comparative analysis, this study offers significant insights into how factors influencing GenAI adoption vary between countries. These insights can be valuable for university stakeholders.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".