Author Information
Ying Wu, University of Dundee, United KingdomAndrew Ross, University of Dundee, United Kingdom
Malcolm Stewart, University of Stirling, United Kingdom
Abstract
This paper presents the AI-Enabled Industry-Collaborative Assessment Model (AI-ICAM), developed to integrate artificial intelligence into higher education assessment through authentic, industry-aligned tasks. Drawing on three case studies, the research examines how AI can enhance assessment design, delivery, and outcomes in partnership with industry stakeholders. The first case, an undergraduate sustainability and digital marketing module, demonstrated how students leveraged AI for creative campaign assets, improving alignment with client sustainability goals. The second case, a multidisciplinary “Biz-a-thon” innovation sprint with a financial technology partner, showed how AI-supported rapid prototyping improved time efficiency, presentation quality, and professional polish under time constraints. The third case, a curated podcast series with industry leaders, revealed that even indirect AI exposure through expert discourse could stimulate student engagement with emerging tools and trends. Cross-case analysis identified common benefits of AI integration, including enhanced creativity, efficiency, and professionalism, alongside variations in impact depending on whether AI use was direct or indirect. The findings highlight the importance of strategic alignment between AI capabilities, assessment objectives, and industry needs, underpinned by a culture of trust, ethical practice, and openness to innovation. The paper concludes with practical recommendations for refining AI-ICAM and advancing university–industry collaboration in assessment.
Paper Information
Conference: ECE2025Stream: Assessment Theories & Methodologies
This paper is part of the ECE2025 Conference Proceedings (View)
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To cite this article:
Wu Y., Ross A., & Stewart M. (2025) AI-Enabled Industry-Collaborative Assessment (AI-ICAM): Advancing Pedagogy and University–Industry Collaboration ISSN: 2188-1162 The European Conference on Education 2025: Official Conference Proceedings (pp. 467-487) https://doi.org/10.22492/issn.2188-1162.2025.38
To link to this article: https://doi.org/10.22492/issn.2188-1162.2025.38
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