Faculty engagement and artificial intelligence: Bibliometric analysis and recent trends
Bibliographic record
Abstract
This paper presents a bibliometric investigation of research linking faculty engagement and artificial intelligence (ai) over the period 2014–2025, using scopus as the primary data source. Analytical tools, including vosviewer, were employed to trace publication growth, citation trends, and collaborative networks, as well as to uncover intellectual foundations and thematic developments. The results indicate a sharp increase in scholarly attention after 2020, with the years 2023–2025 marking the highest levels of research activity. Countries like the united states, united kingdom, canada, india, and china emerged as leading contributors, while brazil demonstrated notable impact through highly cited publications despite fewer outputs. Key contributions from scholars including borges, braganza, and koo provided pivotal theoretical grounding for subsequent studies. Thematic clustering revealed that research in this area has expanded from early discussions of technological adoption to broader concerns involving pedagogy, institutional leadership, workload distribution, ethical considerations, and the gigification of academic roles. Although the study is limited by its reliance on a single database and the dynamic nature of citation metrics, it offers novel insights by systematically integrating two fields that have rarely been examined together. The originality of the study lies in mapping how ai is shaping faculty engagement in higher education and in identifying knowledge gaps that open pathways for future interdisciplinary and practice-oriented research.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.100 | 0.217 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".