Navigating Online Learning and Artificial Intelligence: Identifying and Managing Assessment Risks in Private Higher Education in South Africa
Bibliographic record
Abstract
This paper investigates assessment integrity risks in online distance learning programmes at a private higher education institution in South Africa and the strategies used to manage them. Guided by an interpretivist lens, the research applies the Committee of Sponsoring Organisations of the Treadway Commission (COSO) Enterprise Risk Management (ERM) framework and sociotechnical systems theory to explore how technological, behavioural, and institutional factors intersect to influence academic integrity. Data from staff questionnaires reveal three interrelated dimensions of risk: emergent (artificial intelligence-driven misconduct and integrity threats), behavioural (unethical and dishonest practices), and structural (technological barriers and infrastructural limitations). Synthesised through the Emergent, Behavioural, and Structural risks addressed through Tools, Practices, and Training (EBS-TPT) model, the research highlights the importance of integrating digital tools and ethical training within proactive, design-oriented assessment strategies. Rather than relying solely on detection mechanisms, institutions should foster artificial intelligence (AI) literacy, ethical awareness, and authentic assessment design to sustain credibility in digital learning environments. The paper contributes a conceptual framework and practical insights for higher education institutions seeking to balance technological innovation with academic integrity in the evolving AI era.Keywords: online distance learning, academic integrity, assessment dishonesty, risk management protocols, private higher education. French Cet article examine les risques liés à l’intégrité des évaluations dans les programmes d’apprentissage à distance en ligne d’un établissement privé d’enseignement supérieur en Afrique du Sud, ainsi que les stratégies mises en œuvre pour les gérer. S’appuyant sur une approche interprétativiste, l’étude mobilise le cadre de gestion des risques d’entreprise COSO (ERM) et la théorie des systèmes sociotechniques afin d’explorer comment les facteurs technologiques, comportementaux et institutionnels interagissent et influencent l’intégrité académique. Les données recueillies auprès du personnel révèlent trois dimensions interdépendantes du risque : émergent (manquements à l’intégrité favorisés par l’IA), comportemental (pratiques malhonnêtes et non éthiques) et structurel (barrières technologiques et limitations infrastructurelles). Synthétisées dans le modèle EBS-TPT (Emergent, Behavioural and Structural risks addressed through Tools, Practices and Training) ces dimensions soulignent l’importance d’intégrer les outils numériques et la formation éthique dans des stratégies d’évaluation proactives et centrées sur la conception. Plutôt que de se reposer uniquement sur les mécanismes de détection, les établissements devraient promouvoir la littératie en matière d’IA, la conscience éthique et la conception d’évaluations authentiques afin de préserver la crédibilité des apprentissages numériques. L’article propose ainsi un cadre conceptuel et des recommandations pratiques pour aider les établissements d’enseignement supérieur à concilier innovation technologique et intégrité académique à l’ère de l’IA. Mots-clés : Enseignement à distance, intégrité académique, fraude à l'évaluation, protocoles de gestion des risques, enseignement supérieur privé.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".