ISEDJ

Abstract: This paper presents the systematic design and development of an undergraduate artificial intelligence and machine learning course intended to serve both technical and non-technical students in higher education. The work addresses growing industry demand for AI and ML competencies by proposing a comprehensive course framework that accommodates students from diverse academic backgrounds while maintaining academic rigor. Building upon established experiential and active learning theories, the proposed course design emphasizes hands-on learning through progressive skill development. The curriculum incorporates fundamental concepts including supervised and unsupervised learning, neural networks, natural language processing, and computer vision, while integrating ethical considerations throughout. The pedagogical framework utilizes cloud-based laboratory environments and industry-standard tools to provide accessible yet rigorous learning experiences that bridge theoretical understanding with practical implementation skills. This pedagogical design case synthesizes current best practices in AI and ML education, drawing from successful data analytics program implementations to develop a comprehensive framework for course design.

Download this article: ISEDJ - V25 N1 Page 66.pdf

Recommended Citation: Turner, S., Mew, L., Turner, T.G., (2027). Developing an Artificial Intelligence Course for a Small Undergraduate Program. Information Systems Education Journal 25(1) pp 66-79. https://doi.org/10.62273/HWXF5405