Paper Type
Complete
Abstract
The rapid adoption of Generative Artificial Intelligence (GAI) is transforming Information Systems (IS) education, yet university curricula remain misaligned with industry expectations. This study investigates the industry-university AI competency gap, examining how Mode 2 knowledge production can facilitate GAI-integrated curriculum innovation. Conducted within a Master of Business Analytics course, this research employed a qualitative approach, using structured focus groups with industry professionals to identify essential GAI-related competencies, pedagogical approaches, and assessment models and co-create an assessment. Content analysis of focus group findings highlights the need for process-oriented assessments, GAI literacy training, and iterative industry collaboration to ensure graduates develop critical validation, ethical reasoning, and analytical oversight in GAI-assisted decision-making. Theoretically, this study extends Mode 2 knowledge production by demonstrating its relevance in curriculum co-creation for AI-integrated education. It offers practical recommendations for universities, educators, and policymakers, advocating for sustained university-industry engagement to embed problem-driven, industry-relevant AI competencies in IS education.
Paper Number
1522
Recommended Citation
Perera Muthupoltotage, Udayangi and Walsh, Martin A., "Bridging the Industry-University AI Competency Gap: A Mode 2 Approach to GAI-Integrated Education" (2025). AMCIS 2025 Proceedings. 20.
https://aisel.aisnet.org/amcis2025/is_education/is_education/20
Bridging the Industry-University AI Competency Gap: A Mode 2 Approach to GAI-Integrated Education
The rapid adoption of Generative Artificial Intelligence (GAI) is transforming Information Systems (IS) education, yet university curricula remain misaligned with industry expectations. This study investigates the industry-university AI competency gap, examining how Mode 2 knowledge production can facilitate GAI-integrated curriculum innovation. Conducted within a Master of Business Analytics course, this research employed a qualitative approach, using structured focus groups with industry professionals to identify essential GAI-related competencies, pedagogical approaches, and assessment models and co-create an assessment. Content analysis of focus group findings highlights the need for process-oriented assessments, GAI literacy training, and iterative industry collaboration to ensure graduates develop critical validation, ethical reasoning, and analytical oversight in GAI-assisted decision-making. Theoretically, this study extends Mode 2 knowledge production by demonstrating its relevance in curriculum co-creation for AI-integrated education. It offers practical recommendations for universities, educators, and policymakers, advocating for sustained university-industry engagement to embed problem-driven, industry-relevant AI competencies in IS education.
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