Perspectives from South Africa on GenAI in Higher Education: A Postdigital Dialogue with the Global Context
Bibliographic record
Abstract
Abstract Drawn from an interdisciplinary gathering of 51 colleagues at the University of the Witwatersrand (Wits) in Johannesburg in March 2025, this collective article shares multiple perspectives from South Africa on our interactions with Generative AI (GenAI) across higher education (HE). Contributors have re-created our dialogue here, in the spirit of Ubuntu and via a postdigital lens, bringing together vital local knowledge and literature to demonstrate why context always matters deeply. Over decades now, HE policy language has inferred that we all experience digital technologies in the same way. With GenAI, this technocratic determinism is accompanied also, by a depressing dystopian fatalism. Rather than confine our diverse positionalities within either of these viewpoints, we favoured a relational approach of reciprocal listening and pedagogical responsiveness to explore the complex interplay between GenAI, learning design, assessment, and social justice. Amid the pressure to integrate GenAI, a deliberate pause is needed, to notice and respond to, the flaws it exposes in our traditional systems. It is therefore timely to also review the social contract that underpins equitable and ethical opportunities in HE. Under four themes, authors provide recommendations towards a new critical, relational GenAI governance, based on diverse lived experiences in this messy postdigital space. From this particular context in South Africa, we now warmly invite continued discussion across the wider global community.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.036 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".