Digitalised higher education: key developments, questions, and concerns
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
Higher education is already profoundly digitalised. Students, academics, and university administrators routinely use digital technologies, many of which rely on data, including artificial intelligence. Universities aim to operate as data-powered organisations to support institutional efficiency and the personalisation of learning and student experience. These developments are occurring against the backdrop of university digital infrastructure moving to the cloud and the increasing role of ‘Big Tech’ in the sector. However, there are many unknowns about the aggregate impact of digitalisation on the sector, and hence, questions about potential risks and harms remain unanswered. Our approach in this collective piece is to reflect on particularly relevant and impactful dynamics of higher education digitalisation. We first identify assetisation as an emergent mode of governance linked to the digitalisation of HE, which brings new temporal, relational, and lock-in challenges for universities and their constituents. Second, we examine the macro-level structural transformation of higher education with the increasing role of Big Tech and Big EdTech. We conclude by discussing the consequences of the identified macro power dynamics.
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 | 0.001 |
| 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