Adopting artificial intelligence in higher education requires more than selecting a model. The institution must first define the problem, the lawful basis for data use, the level of risk and the outcome it intends to improve.
A responsible pilot defines metrics before it begins: time saved, accuracy, satisfaction, errors, cost and security events. It also establishes which decisions remain under human supervision.
Once the pilot proves value, the institution needs governance: accountable owners, an AI system inventory, impact assessment, logs, acceptable-use criteria, monitoring and complaint mechanisms.
Sustainable innovation emerges when technology, regulation and academic experience move forward together.
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Artificial intelligence
Artificial intelligence in higher education: from pilot to governance
How to move from isolated experiments to measurable, secure use cases aligned with the institution.