Abstract:

Artificial Intelligence (AI) education is rapidly expanding across disciplines, yet many undergraduate courses involving AI remain either technically narrow or conceptually abstract. This presentation introduces an interdisciplinary co-teaching model implemented in a Business School course jointly designed by faculty from two U.S. universities. The course integrates foundational AI concepts—including Machine Learning, Deep Learning, and Generative AI—with ethical analysis, organizational transformation, and societal impact.

A distinguishing feature of the course is its conference-driven structure. Students work in collaborative research teams to design AI-based projects that culminate in the production of a professional conference-style abstract, extended paper, and formal presentation submitted to an external academic or professional venue. Rather than treating AI as solely a technical skillset, the course emphasizes responsible/ethical AI, governance, bias mitigation, and data-driven decision-making within organizational and societal contexts.

Instruction incorporates hands-on RStudio-based analytical workflows without requiring prior coding experience, progressively introducing supervised and unsupervised learning techniques such as clustering, Random Forest, and Artificial Neural Networks, alongside generative AI applications and structured ethical analysis of AI-driven decision-making in business, governmental, and non-governmental organizations. Assessment is structured around iterative draft development, peer feedback, ethical analysis, and professional dissemination.

This presentation discusses pedagogical design, interdisciplinary co-teaching strategies, research-oriented assessment methods, and lessons learned in guiding students from conceptual AI understanding to conference-level scholarly communication. The conference-driven structure builds upon more than a decade of experience implementing research-integrated pedagogy across multiple courses. The model offers a reproducible and expandable framework for integrating technical AI literacy, ethical responsibility, and experiential learning within undergraduate business education.

Keywords:

Artificial Intelligence Education, Business Education, Experiential Learning, Responsible AI, Technology-Enhanced Pedagogy.