The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent months, Artificial Intelligence (AI) has had, and will continue to have, a dramatic impact on Higher Education (HE). A study conducted by researchers at a leading university in Australia surveyed 30 of their teaching staff, drawn predominantly from their teaching academy, and interviewed eight of them regarding the impact of AI on HE. Data were analyzed using the procedures of Inductive Thematic Analysis and revealed a lack of any homogenous sentiment around AI in HE and much ambiguity regarding best practice regarding recent technological developments. The results indicate concerns exist around concepts relating to academic integrity, however, these concerns may be exaggerated. Almost half of the participants indicated they were using AI within their teaching roles with the most common design change being modifications to assessments. Less than a quarter of staff agreed the university has adequately equipped them for AI, and more than three quarters indicated they would like support. They unanimously assumed the technology will improve. Keeping in mind universities’ obligation to serve students by preparing them for industry, it is vitally important that the HE sector stays informed of developments in AI and commit to ongoing research and discussions regarding best practice in response to AI. However, anything regarding AI and future developments will be extremely difficult to predict.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it