Scoping the Landscape : Opportunities, Challenges, and Strategies for Generative AI in Higher Education
Bibliographic record
Abstract
The present scoping review synthesizes literature from 2020 to 2025 to explore the integration of generative Artificial Intelligence (GenAI) in higher education, identifying its transformative opportunities, inherent challenges, and strategies for ethical implementation. Drawing on over 30 diverse sources, including peer-reviewed articles, institutional reports, and case studies, the review highlights GenAI’s potential to revolutionize education through personalized learning, task automation, innovative course development, and 24/7 academic support. Technologies like adaptive quizzes and virtual tutors, implemented by institutions such as Arizona State University and the University of Toronto, enhance learning experiences and expand access for students while aligning education with job market demands. However, challenges such as AI "hallucinations" causing misinformation, privacy risks, ethical concerns around cognitive autonomy, and disparities in accessibility for disabled and rural learners hinder equitable adoption. Governance strategies, including adaptive policies, human oversight, and AI literacy programs, are crucial for ensuring responsible implementation, with models from Stanford and MIT offering effective frameworks. Despite compelling evidence, gaps remain in addressing equitable access, long-term workforce implications, and consistent governance. This review provides a roadmap for stakeholders to harness the potential of GenAI while mitigating risks, with inclusive policies and AI literacy ensuring ethical and fair integration. Through the interconnection of these themes, this research lays a groundwork for further studies to bridge gaps and promote sustainable, innovative learning spaces in universities.
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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 | 0.056 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".