AI Policies in Higher Education: Risk Management, Academic Integrity, and EU AI Act Alignment for Partner Institutions
DOI:
https://doi.org/10.5281/zenodo.18525373Keywords:
higher education, AI policy, academic integrity, risk management, compliance, governance, EU AI ActAbstract
Artificial intelligence (AI) is being adopted across higher education for advising, learning analytics, admissions triage, assessment support, and administrative efficiency. This diffusion creates measurable benefits but also introduces integrity risks (unauthorised assistance and undetected contract cheating), rights and privacy risks (profiling, surveillance, and automated decision-making), and safety/security risks (prompt injection, data exfiltration, and supply-chain vulnerabilities). This paper develops an implementable policy-and-compliance framework for partner institutions that integrates (i) (i) institutional governance and role assignment, (ii) a use-case classification and risk-scoring rubric aligned with the EU AI Act’s risk-based logic, (iii) academic integrity controls embedded in assessment design and disclosure rules, and (iv) monitoring, documentation, and auditability requirements grounded in recognised risk-management standards. An illustrative dataset scores six common university AI use cases on overall risk and academic integrity impact, demonstrating that proctoring and automated grading concentrate the highest risk, while advisory chatbots and research summarisation are lower-risk but still require privacy and transparency controls. The paper concludes with a practical checklist and forward-looking scenarios (2027–2030) showing how governance maturity can reduce measured risk while compliance obligations and threat environments evolve.
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