Machine learning (ML) has become a vital skill across various disciplines, driving innovation and transforming industries. This growing demand emphasizes the need for effective teaching methods tailored to students with diverse academic and technical backgrounds. Teaching ML to non-majors presents significant challenges, as many students lack foundational knowledge in mathematics and programming. This study explores how university instructors addressing these challenges in bachelor’s and master’s level courses, based on semi-structured interviews. The analysis uncovered several key strategies used by instructors. Many emphasized connecting ML concepts to real-world applications, making the subject more relatable. Visualization tools were commonly employed to simplify abstract concepts and improve comprehension. Hands-on activities, including live coding and interactive assignments, were highlighted as effective methods to engage students and bridge theory with practice. However, instructors faced challenges as well such as accommodating diverse student backgrounds, correcting misconceptions, and designing assignments that balanced accessibility and depth. Although tracking student progress was not the focus of this study, some insights were provided. Some instructors mentioned using formative assessments, such as quizzes and project-based evaluations, to measure understanding. These findings highlight the importance of adaptable teaching methods and inclusive learning experiences in making ML education more accessible and effective for non-majors, while also providing actionable advice to improve the educational process and course design.

The use of chatbots in eHealth applications has grown significantly, providing an interactive platform for users to improve their health and lifestyle. To enhance these applications, human feedback has been incorporated, with a coach offering personalized guidance to help users achieve their goals. However, deciding whether to provide human feedback or rely solely on automated systems remains a challenge, especially given the cost constraints associated with human involvement. This study presents a reinforcement learning (RL) model designed to optimize this decision by analyzing users’ states and predicting the potential benefit of such interventions. The model was trained on data from a longitudinal study involving over 500 daily smokers and vapers who interacted with a virtual coach across multiple sessions. Our findings indicate that the RL model effectively assists in determining whether human feedback is valuable, increasing the likelihood of behavior change, enhancing users’ quitter identities, and further reducing smoking/vaping frequency. This research contributes to the development of more effective, resource-efficient eHealth interventions.