Purpose This study explores first-year Electrical Engineering and Computer Science students’ use and perception of artificial intelligence (AI) tools in a programming course, and their preferences for future development. Design/methodology/approach We conducted an anonymous exploratory survey, consisting of items with predefined response options and open-ended items. Responses to the former and open-ended items were analyzed using descriptive statistics and inductive thematic analysis, respectively. Additionally, we clustered students using the Affinity Propagation algorithm based on their expressed preferences for possible improvements of AI tools Findings The findings show that AI tools are not universally effective, with seven student clusters identified based on differing needs and expectations. Students expressed a need for AI tools that offer more detailed error explanations and guidance rather than just delivering correct solutions. The most common concern among students is the provision of correct solutions without adequate explanations of the underlying mistakes, leading to a lack of deeper understanding Originality/value This study takes an exploratory approach by examining students’ perceptions and preferences for the design and capabilities of AI tools in helping them learn programming. Clustering students by preferences reveals distinct approaches that may be needed for different groups of learners. Given the limited research on such desires or on applying clustering to them, our analysis offers valuable insights into distinct viewpoints that can guide the design of future personalized educational AI tools
Kamberovic et al. (Fri,) studied this question.