Artificial Intelligence, Assessment Integrity, and the Future of Curriculum and Teaching: Governing Educational Innovation Without Compromising Academic Standards

Authors

Sixbert SANGWA
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Placide MUTABAZI
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Jean Bosco MUVUNYI
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Synopsis

Artificial intelligence (AI) changes the conditions under which universities generate knowledge, organise teaching, evaluate individual achievement and justify academic awards. Its significance is not exhausted by the efficiency of automated tools or the detection of misconduct: AI unsettles the relationship among intended competence, performed intellectual work, submitted evidence and accountable educational judgement. This chapter develops a Rwanda-anchored, critically evidence-based account of responsible AI integration across curriculum, pedagogy, assessment, institutional management and external review. A purposive documentary and conceptual synthesis combines foundational scholarship, recent experimental research syntheses on learning outcomes and assessment validity with UNESCO guidance, the National AI Policy, Rwanda's data-protection law, Higher Education Council instruments and international risk-management benchmarks. It differentiates generative, predictive, adaptive and increasingly agentic applications; examines disciplinary and role-specific AI literacy; and analyses authorship, academic integrity, accessibility, bias, privacy, procurement, academic labour and technological dependence. The chapter's distinctive contribution is an evidence-traceable governance model connecting educational purpose, legitimate authority, bounded AI use, independently interpretable assessment evidence, learner protection and reviewable decisions. Practical instruments include an institutional AI governance and academic assessment integrity framework, task-level disclosure template, assessment-redesign matrix, risk-based tool appraisal and worked hypothetical evaluation. These are original professional proposals, not regulator-approved or independently validated instruments. The chapter concludes that trustworthy AI-enabled higher education requires greater, rather than diminished, human responsibility for what is taught, learned, assessed and certified.

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Forthcoming

17 October 2026