Author Information
Fahima Alam, Queen Mary University of London, United KingdomJonathan Jackson, Queen Mary University of London, United Kingdom
Abstract
The growing use of Generative Artificial Intelligence (GenAI) tools such as ChatGPT is reshaping how students approach learning and assessment in Higher Education (HE) as well as the viability of traditional assessment methods such as essays. While a significant amount of research has focused on student acceptance of GenAI through models such as the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), there is a pressing need for practical examples of how assessments in HE can remain valid and secure despite the prevalence of GenAI. This study explores the benefits and limitations of a collaborative, process-oriented assessment implemented with 23 first-year degree apprenticeship students at Queen Mary University of London. Student teams were required to engage with an iterative essay-writing task, producing multiple drafts and reflecting on how their work developed through collaboration with each other and optional use of GenAI tools to support their writing process. Drawing on Task-Technology Fit (TTF) and Institutional Theory, the study examines how GenAI aligns with academic writing practices and institutional expectations within an undergraduate assessment context. The findings suggest that GenAI can support the learning process through exploration of ideas and refinement of arguments over time, if appropriate assessment design scaffolding exists. The iterative and collaborative elements of the assessment appeared to support the development of students’ academic voice, identity, and autonomy. The study highlights the value of designing assessments that prioritise process, reflection, and collaboration, enabling more meaningful and engaging learning experiences while strengthening academic integrity.
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