Integrating generative artificial intelligence into teaching and assessment: A case study from a University in the UK: A case study from a University in the UK

Tadhg Blommerde*, Elenia Charalambous, William Bright, Ellie Musgrave, Lilian Ueno, Teagan Magee

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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    Abstract

    This chapter explores the pioneering integration of generative artificial intelligence (GenAI) into teaching and assessment within a module at a UK university. This initiative represents a significant step in advancing AI literacy in higher education, aiming to equip students with essential employability skills. The innovative approach empowered students to use GenAI tools effectively, critically, ethically, and responsibly. The collaborative effort with students provided valuable insights into GenAI's potential to enhance student learning and future career prospects. Key recommendations for educators include dispelling the notion that GenAI use equates to cheating and that its ethical and critical application should be promoted. Encouraging transparency in GenAI usage can mitigate student engagement issues, while continuous feedback from students ensures the module remains responsive to their needs and experiences. By making GenAI use explicit and teaching effective prompt engineering, the module fostered a transition from covert use to responsible application. Hands-on experiential learning sessions were pivotal in developing students' GenAI proficiency, enhancing their engagement and skill development.

    Original languageEnglish
    Title of host publicationEffective Practices in AI Literacy Education
    Subtitle of host publicationCase Studies and Reflections
    EditorsXianghan O’Dea, Davy Tsz Kit Ng
    Place of PublicationLeeds
    PublisherEmerald
    Pages119-126
    Number of pages8
    Edition1st
    ISBN (Electronic)9781836088523
    ISBN (Print)9781836088530
    DOIs
    Publication statusPublished - 2 Dec 2024

    Keywords

    • Ethical AI literacy
    • Experiential learning
    • Generative artificial intelligence
    • Higher education
    • Student engagement

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