As AI coding tools become more prevalent in programming, it is essential to understand how they influence programming education. This study, conducted in a first-semester Introduction to Programming course, aimed to determine the positive and negative effects of these tools on students’ learning experiences and their ability to develop essential programming skills. Using a mixed-methods approach, we collected data from 73 teams of engineering students over a 12-week period. Students completed surveys and reported on their AI tool usage. We analyzed this data quantitatively to identify trends in tool familiarity, usage, and student satisfaction. Additionally, qualitative analysis of student reports provided insights into the specific ways AI tools were used and their perceived benefits and drawbacks. The findings revealed a significant increase in AI tool familiarity (from 28% to 100%) and usage among students. Students’ satisfaction with AI tools improved over time. The most prevalent tasks for which novice programmers used AI tools included creating comments (91.7%), identifying and correcting bugs (80.2%), and seeking information (68.5%), while other tasks were less common. While these tools offered benefits like assisting in learning and enhancing real-world relevance, they also raised concerns about cheating, over-reliance on AI tools, and a limited understanding of core programming concepts.

This study examines the effects of integrating an inquiry-based final project into an early childhood robotics program, focusing on its influence on children’s problem-solving self-efficacy, attitudes toward collaboration, confidence in applying robotics to real-world challenges, and future interest in STEM. A total of 176 first-grade students (aged 6–7) were randomly assigned to either a research group that completed a culminating inquiry-based robotics project or a control group that followed a traditional structured curriculum. A quasi-experimental post-test-only comparison group design was used, and baseline equivalence was confirmed across groups. Results revealed that children who participated in the inquiry-based final project group demonstrated significantly higher problem-solving self-efficacy and more positive attitudes toward peer collaboration, while also being more likely to see the relevance of robotics to real-world problems and to align with inquiry-based learning approaches. Gender analysis showed that these gains were especially pronounced among girls, who exhibited more statistically significant improvements in problem-solving confidence and self-efficacy in inquiry-based problem-solving. The study’s findings highlight the benefits of incorporating inquiry-based final projects into early robotics curricula, addressing a critical gap in early childhood STEM education by providing evidence-based insights into how to enhance foundational STEM dispositions and engagement through inquiry-based, technology-integrated instruction.

The growing integration of generative artificial intelligence (GenAI) tools in higher education has potential to transform learning experiences. However, empirical research comparing GenAI-supported learning with traditional instruction lags behind these developments. This study addresses this gap through a controlled experiment involving 96 undergraduate computer science students in a Database Management course. Participants experienced either GenAI-supported or traditional instructions while learning the same concept. Data were collected through questionnaires, quizzes, and interviews. Analyses were grounded in self-determination theory (SDT), which posits that effective learning environments support autonomy, competence, and relatedness. Quantitative findings revealed significantly more positive learning experiences with GenAI tools, particularly enhancing autonomy through personalized pacing and increased accessibility. Competence was supported, reflected in shorter study times with no significant achievement differences between approaches. Students performed better on moderately difficult questions using GenAI, indicating that GenAI may bolster conceptual understanding. However, interviews with 11 participants revealed limitations in supporting relatedness. While students appreciated GenAI’s efficiency and availability, they preferred instructor-led sessions for emotional engagement and support with complex problems. This study contributes to the theoretical extension of SDT in technology-mediated learning contexts and offers practical guidance for optimal GenAI integration.