Examining the Impact of Artificial Intelligence Implementation on Enhancing Research Productivity in Higher Education
Bibliographic record
Abstract
This study examines the impact of Artificial Intelligence (AI) adoption on research productivity in higher education, focusing on the roles of Information Quality, System Quality, Service Quality, System Usage, and User Satisfaction. A quantitative approach was employed, with data collected from 120 respondents via a structured Google Forms survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS. The findings show that User Satisfaction is the strongest predictor of research productivity, highlighting the importance of user-centered design and functionality in AI tools. Other constructs, such as Information Quality and System Usage, had weaker direct effects, indicating their influence may be mediated by satisfaction. The study contributes a validated framework for measuring AI effectiveness in academic settings and offers both theoretical insights and practical guidance for higher education institutions and AI developers. Future research should include longitudinal designs, cross-disciplinary comparisons, and the integration of emerging AI technologies to broaden the framework's relevance. Overall, the study underscores AI's transformative potential in academia while identifying key factors necessary for optimizing its adoption to enhance research excellence.
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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 | 0.043 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".