Embedding Generative AI as a digital capability into a year-long skills program

Smith, David P., Sokoya, Dami, Moore, Skye, Okonkwo, Chinenye, Boyd, Charlotte, Lacey, Melissa M. and Francis, Nigel J. ORCID: https://orcid.org/0000-0002-4706-4795 2025. Embedding Generative AI as a digital capability into a year-long skills program. Journal of University Teaching and Learning Practice 22 (4) 10.53761/fh6q4v89

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Abstract

The arrival of Generative Artificial Intelligence (GenAI) into higher education has significantly transformed assessment practices and pedagogical approaches. Large Language Models (LLMs) powered by GenAI present unprecedented opportunities for personalised learning journeys. However, the emergence of GenAI in higher education raises concerns regarding academic integrity and the development of essential cognitive and creative skills among students. Critics worry about the potential decline in academic standards and the perpetuation of biases inherent in the training sets used for LLMs. Addressing these concerns requires clear frameworks and continual evaluation and updating of assessment practices to leverage GenAI's capabilities while preserving academic integrity. Here, we evaluated the integration of GenAI into a year-long MSc program to enhance student understanding and confidence in using GenAI. Approaching GenAI as a digital competency, its use was integrated into core skills modules across two semesters, focusing on ethical considerations, prompt engineering, and tool usage. The assessment tasks were redesigned to incorporate GenAI, which takes a process-based assessment approach. Students' perceptions were evaluated alongside skills audits, and they reported increased confidence in using GenAI. Thematic analysis of one-to-one interviews revealed a cyclical relationship between students' usage of GenAI, experience, ethical considerations, and learning adaptation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Biosciences
ISSN:	1449-9789
Date of First Compliant Deposit:	30 September 2025
Date of Acceptance:	10 June 2025
Last Modified:	01 Oct 2025 08:45
URI:	https://orca.cardiff.ac.uk/id/eprint/181407

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