Teaching Artificial Intelligence and Machine Learning to Non-Majors: A Scoping Review

Abstract

Equipping university students with artificial intelligence (AI)/machine learning (ML) skills is essential for future career integration. Considering the crucial role of AI/ML and the identified shortage of instructional guidelines and resources for students, our objective was to investigate AI/ML instruction for non-majors. In this study, we provide a scoping review of AI/ML instruction at the tertiary level, identifying 24 journal articles, nine conference papers, and three book chapters published between 2010 and 2025, collected from seven databases. By analyzing the specific focus areas of AI/ML learning and teaching, we detected major trends in practice and areas to be improved in AI/ML educational research: 1) studies predominantly focus on AI/ML literacy and models that are accessible to non-majors; 2) a diverse set of technological tools and platforms are used in AI/ML instruction; 3) student-centered pedagogical approaches are claimed to be adopted in the courses, but most of the assessment relies solely on student surveys; and 4) studies often lack an assessment of learning outcomes and fail to provide a robust research design. Future research should prioritize providing evidence of instructional effectiveness in AI/ML. The findings of the study offer several insights to university instructors and researchers on AI/ML research trends, aiming to enhance the overall quality of this emerging and important field.