<scp>AI</scp> In Higher Education: Risks and Opportunities From the Academician Perspective
Bibliographic record
Abstract
ABSTRACT This research investigates how artificial intelligence (AI) influences higher education, specifically exploring the perspectives of academicians regarding associated risks and opportunities. The study is aimed at the implementation of AI within university settings and its impact on both educators and students. Given the swift integration of AI, notably the widespread adoption of generative AI in higher education, the article emphasises AI's ability to collect detailed data, providing a deeper understanding of academicians' learning experiences. This, in turn, enables personalised support, allowing academicians to respond more effectively to students' needs and improve the overall educational process. Moreover, the research highlights AI's potential to proactively identify students at risk of failure, offering academicians a comprehensive view for more effective assessment. On the other hand, these advantages and the growing dependence on technology pose challenges, including reduced interaction between academicians and students, shifts in workforce dynamics, concerns about student privacy and disparities in technology access. Acknowledging these issues, the study underscores the importance of preparing academicians and students for the evolving landscape of higher education shaped by AI. It stresses the need for proactive measures to navigate these changes effectively, as they are inevitable. The findings of this study are significant for the field of higher education, as they provide a clear and critical assessment of AI's transformative potential and advocate for proactive measures to navigate the changes effectively.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".