ANCORing Generative AI within the Computing Curriculum

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Zarb, Mark and Goodfellow, Martin (2026) ANCORing Generative AI within the Computing Curriculum. Journal of Perspectives in Applied Academic Practice, 14 (1). pp. 39-51. ISSN 2051-9788 (https://doi.org/10.56433/vmnhhk83)

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Abstract

Generative AI and Large Language Models have become ubiquitous across education and within higher education institutions. Emerging challenges include potential over-reliance on Generative AI, risks to academic integrity, and inequitable access: there is an urgent need for students to develop ethical, self-regulated and grounded learning practices in its use. This paper presents insights distilled from a survey of 14 computer science educators in the UK, and identifies the overarching importance of teaching responsibility and ethical implications of the use of AI to students. The ANCOR framework is presented as a method for teaching responsible Generative AI use, integrating ethical reasoning, real-world examples, and curriculum-wide approaches. It offers a novel contribution by providing both actionable teaching techniques and a conceptual approach for embedding ethical and responsible use of Generative AI tools across computing curricula, including guidance on ethics integration, contextualising relevant policies, developing ethical decision-making skills, addressing anthropomorphism, and using illustrative real-world cases.

ORCID iDs

Zarb, Mark and Goodfellow, Martin ORCID logoORCID: https://orcid.org/0000-0003-2151-8442;
CORE (COnnecting REpositories)