Designing an AI-Resilient Assessment Framework in Open and Distance Learning

Abstract

The emergence of generative artificial intelligence tools such as ChatGPT, Copilot, and Gemini has disrupted long-established ideas about teaching, learning, and assessment in higher education, especially within the Open and Distance Learning (ODL) context. Evidence from recent literature shows that traditional assessment formats, particularly take-home assignments and written tasks, are increasingly vulnerable to AI automation, which subsequently raises significant concerns about academic integrity. This conceptual paper proposes the AI-Resilient Assessment Framework (ARAF), a theoretically grounded framework that positions AI not as a threat but as a catalyst for higher-order learning. By synthesising evidence from GenAI assessment reviews, AI policy studies, authentic assessment strategy, and research on AI literacy, ARAF introduces three core principles, namely Transparency, Reflection, and Authenticity, supported by a set of Institutional Enablers. The framework shifts assessment design away from policing AI use and toward fostering ethical engagement, metacognitive reasoning, contextual judgement, and real-world performance. It offers both practical and theoretical guidance for ODL institutions that aim to implement sustainable, ethical, and future-ready assessment strategies within an AI-enhanced learning environment.