Description
The rapid adoption of Generative Artificial Intelligence (GenAI) in higher education has fundamentally disrupted established approaches to assessment and academic integrity. Traditional integrity frameworks, largely dependent on detection technologies and punitive responses, are increasingly inadequate in an AI-enabled learning environment and risk exacerbating existing inequities for minoritised, international and disabled students. This paper addresses the growing challenge of designing assessments that remain credible, inclusive and pedagogically meaningful in the context of widespread GenAI use.The study adopts a conceptual synthesis approach, drawing on Universal Design for Learning (UDL), Equity, Diversity and Inclusion (EDI), students as partners, active blended learning, and emerging principles of AI pedagogy. Through critical analysis of current assessment practices and integrity discourses, the paper integrates these frameworks to reconceptualise assessment design as the primary mechanism for safeguarding integrity and supporting authentic learning.
The paper proposes the AI-Resilient Assessment Design (AI-RAD) model, a design-led framework that embeds academic integrity through authenticity, inclusivity, transparency and ethical AI integration. The model foregrounds assessment task design, student partnership, inclusive practice, responsible AI use, and the assessment lifecycle, supported by staff capacity and institutional culture. Rather than positioning AI as a threat, AI-RAD reframes it as a legitimate learning tool within clearly articulated ethical and pedagogical boundaries.
The originality of this work lies in shifting the academic integrity debate away from detection and surveillance towards pedagogical design and equity. The AI-RAD model offers educators and institutions a practical and ethically grounded approach to assessment redesign that supports authentic learning, student agency and inclusive practice in an AI-enabled higher education landscape.
| Period | 26 Mar 2026 → 27 Mar 2026 |
|---|---|
| Held at | Academic Integrity & assessments in an AI world Conference, South Africa |
| Degree of Recognition | International |