With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical educational programs, such as social sciences courses, is challenging. Here, we present an approach to address this challenge by using no-code AI in a course for university students with diverse educational backgrounds. This approach was tested in an empirical, case-based educational setting, in which students engaged in data collection and trained ML models using a no-code AI platform. In addition, a framework consisting of five principles of instruction (problem-centered learning, activation, demonstration, application, and integration) was applied. This paper contributes to the literature on IS education by providing information for instructors on how to incorporate no-code AI in their courses and insights into the benefits and challenges of using no-code AI tools to support the ML workflow in educational settings.