AI Differences in Vocational and Undergraduate Differential Applications of Artificial Intelligence in Undergraduate and Vocational Higher Education: A Systematic Review
Bibliographic record
Abstract
This study systematically reviews and compares the integration of Artificial Intelligence (AI) in vocational and undergraduate education, drawing on 50 peer‑reviewed studies published between 2018 and 2025. Findings reveal that undergraduate institutions primarily leverage AI to enhance theoretical exploration, research capacity, and higher‑order cognitive skills, while vocational institutions deploy AI for competency‑based, practice‑oriented training aligned with immediate industry needs. Across both sectors, AI transforms educator roles from knowledge transmitters to facilitators—emphasizing technical integration in vocational settings and ethical stewardship in universities. Common benefits include personalized learning, efficiency gains, and improved student engagement, whereas challenges encompass resource disparities, curriculum misalignment, ethical risks, and the potential for student over‑reliance. Vocational institutions face particular vulnerability to inequities due to infrastructure gaps and diverse learner readiness, amplifying the digital divide. The review identifies significant gaps in longitudinal evidence, equity‑focused empirical studies, and sustainable implementation models. Policy and practice implications call for sector‑specific funding, professional development, and ethical AI design. This study underscores the need for tailored strategies to ensure AI fosters equitable, effective, and future‑ready post‑secondary learning environments.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".