Designing for the AI Era: A Framework for Integrating Generative AI into Assessment in Higher Education

Ransome, Emma

Designing for the AI Era: A Framework for Integrating Generative AI into Assessment in Higher Education

Ransome, Emma (2026) Designing for the AI Era: A Framework for Integrating Generative AI into Assessment in Higher Education. Practitioner Reseach in Higher Education. ISSN 1755-1382

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Abstract

The rapid emergence of generative artificial intelligence (GenAI) presents both significant challenges and opportunities for assessment in higher education. Many existing assessment designs, developed prior to widespread AI use, are increasingly misaligned with the skills, practices, and forms of judgement required in an AI-enabled world. At the same time, sector debates around academic integrity, equity, and responsible use highlight the need for clear pedagogic guidance for staff and students.

This paper introduces the GenAI-Integrated Assessment Design Framework, a conceptual model to support the design of robust, authentic, inclusive, and future-facing assessment. Drawing on assessment theory, emerging GenAI scholarship, and principles of equity and transparency, the framework articulates five interdependent dimensions: alignment of assessment purpose and learning outcomes; authenticity and professional relevance; transparent expectations for AI use; diversification of assessment modes; and attention to integrity, equity, and accessibility.

The paper outlines how the framework can be operationalised at module, programme, and institutional levels, supporting curriculum design, quality assurance, and policy development beyond risk-driven responses.

Item Type:	Article
Dates:	Date Event 1 July 2026 Accepted 22 July 2026 Published Online
Subjects:	CAH22 - education and teaching > CAH22-01 - education and teaching > CAH22-01-01 - education
Divisions:	Law and Social Sciences > Education
Depositing User:	Gemma Tonks
Date Deposited:	04 Aug 2026 11:40
Last Modified:	04 Aug 2026 11:40
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17142