In June 2025 the University of Cape Town's Senate endorsed an AI in Education Framework, and from 1 October 2025 UCT discontinued Turnitin's AI Score, citing unreliable false positives and negatives, in favour of ethical AI literacy and redesigned assessment.
53 cases
AI-related disciplinary cases at University of Pretoria (2024-2025)
In June 2025, UCT's Senate Teaching and Learning Committee endorsed a new AI in Education Framework; from 1 October 2025 the university stopped using AI-detection tools, including Turnitin's AI Score, in student assessment, citing unreliable results (both false positives and false negatives) that risked wrongful misconduct accusations, particularly against multilingual and non-native-English writers.
Objectives
Replace automated AI-detection surveillance with an approach built on ethical AI literacy, transparency about permitted AI use, and assessment redesign that is harder to complete dishonestly with AI alone.
Activities
UCT withdrew Turnitin's AI Score and other detectors from assessment processes and rolled out its AI in Education Framework university-wide, against a national backdrop where peer universities take different approaches: the University of Pretoria recorded roughly 53 AI-related disciplinary cases in 2024-2025 and triangulates evidence (declarations, version history, oral clarification) rather than relying on a single detector score, the University of Johannesburg treats undisclosed AI use as fraudulent authorship, and Unisa combines AI-enabled proctoring with tiered disciplinary responses.
Results
The decision was widely covered in South African media as a landmark break from a widely used but increasingly distrusted detection product; no measured learning-outcome data has been published.
Conclusions
UCT's move illustrates South African universities independently converging on the conclusion reached by regulators elsewhere - that detection software alone cannot reliably police AI use in assessment.
Implementation
Indicative cost
Medium (€50k–€500k)
Time to results
Short (< 1 year)
Staffing & skills
Senate Teaching and Learning Committee, Faculty teaching staff redesigning assessments
Conditions for success
Institutional buy-in from Senate-level governance
Investment in staff AI-literacy training
Willingness to redesign assessment tasks rather than rely on detection tooling
Common failure modes
Uneven adoption if individual faculties keep relying on detector scores informally
New assessment designs could still be circumvented without ongoing review
No independent evaluation yet of whether false-positive/negative harms are actually reduced
Where it fits
Governance type
single public university (Senate-level decision)
Scale
institution-wide
Income level
upper-middle-income (South Africa)
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Data sources
Where this practice's information was retrieved from, and when.
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