The impact of attitude toward artificial intelligence on teaching satisfaction: The mediating role of teachers’ self-efficacy of teaching innovation skills among chinese faculty in Thai universities
Bibliographic record
Abstract
With the expansion of Sino–Thai higher education cooperation, many Chinese faculty members are teaching in Thai universities. The integration of artificial intelligence (AI) into teaching offers opportunities for innovation but also presents challenges, especially in cross-cultural contexts. Understanding how Attitude toward Artificial Intelligence (AIA) influence Teaching Satisfaction (TS) through Teachers’ Self-Efficacy of Teaching Innovation Skills (TISE) is therefore essential. This study surveyed 475 Chinese faculty in Thai universities to examine the relationships among AIA, TISE, and TS. Structural equation modeling (SEM) was employed to assess the proposed mediation model, in which AIA was hypothesized to exert an indirect effect on TS via TISE. Reliability and validity analyses were conducted to ensure the robustness of the measurement instruments. Findings show that AIA has a weak but significant direct effect on TS (β = 0.084, p < .05), but a much stronger total effect (β = 0.441, p < .001). AIA significantly predicts TISE (β = 0.573, p < .001), and TISE strongly predicts TS (β = 0.658, p < .001). Mediation analysis indicates that TISE accounts for 81.6% of the total effect of AIA on TS, confirming a dominant partial mediation. The model demonstrates good fit indices (χ²/df = 3.959, RMSEA = 0.079, CFI = 0.936, SRMR = 0.042). Investigation of AI adoption among cross-cultural faculty represents the key contribution of this study to the literature, validating the TISE scale, and extending theoretical perspectives on faculty development. Practical recommendations include strengthening AI-related training, establishing institutional support mechanisms, and refining faculty evaluation to improve teaching satisfaction and facilitate the intelligent transformation of higher education in Thailand.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".