Systematic Review of the Impact of Artificial Intelligence in Higher Education
Bibliographic record
Abstract
Generative AI has undergone a radical transformation, becoming a revolutionary change as important as when the internet appeared. This systematic review explores the impact of AI in higher education, using the principles of Education 4.0 to guide the analysis as a framework. This research used the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA), based on a review of 243 articles published between 2017 and 2025, to address three main objectives: to systematically examine the existing literature, to explore the opportunities and challenges of AI integration, and to identify gaps for future research. Co-occurrence analysis and data-driven methods, including LDA, BERTTopic, and K-Means clustering, reveal that the interest of the scientific community has been growing, focusing on ethical governance, the enhancement of personalized learning, and the development of faculty AI competencies. These priorities are in line with more general worries about guaranteeing equity, openness, and inclusivity in the use of AI. The statistical analyses and administrative applications, on the other hand, have received less attention and are still ripe for investigation. The comprehension of AI's disruptive role in education is strengthened by this exploratory review, which also suggests ways to advance research and practice in higher education settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".