AI-Enabled Industry-Collaborative Assessment (AI-ICAM): Advancing Pedagogy and University–Industry Collaboration

TY - GEN

T1 - AI-Enabled Industry-Collaborative Assessment (AI-ICAM)

T2 - The European Conference on Education 2025

AU - Wu, Ying

AU - Ross, Andrew

AU - Stewart, Malcolm

N1 - Conference code: 13th

PY - 2025/9/24

Y1 - 2025/9/24

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - higher education

KW - assessment innovation

KW - industry collaboration

KW - sustainability education

U2 - 10.22492/issn.2188-1162.2025.38

DO - 10.22492/issn.2188-1162.2025.38

M3 - Conference contribution

T3 - The European Conference on Education 2025: Official Conference Proceedings

SP - 467

EP - 487

BT - The European Conference on Education 2025

A2 - Haldan, Joseph

PB - The International Academic Forum (IAFOR)

CY - Japan

Y2 - 10 July 2025 through 14 July 2025

ER -