In this study, we evaluate how ChatGPT complements and enriches the traditional flipped learning strategy in higher education, particularly in engineering courses. Using an experimental design involving 356 students from basic programming courses in undergraduate engineering programs, we compared the normalized learning gain between groups that used the ChatGPT-assisted flipped learning strategy (focus groups) and those that followed a traditional video-based flipped learning methodology (control groups). The intervention lasted ten weeks, with two sessions of two hours each week. A pre-test–post-test analysis revealed that the focus groups showed significant improvement in normalized learning gain values compared to the control groups. These results confirm that incorporating ChatGPT into the flipped learning strategy can significantly enhance student performance by providing a more active, interactive, and personalized approach during the teaching–learning process. We conclude that the flipped learning strategy, upgraded with the assistance of ChatGPT, provides an effective means to improve understanding and application of complex concepts in programming courses, with potential to be extended to other areas of study in higher education. This study opens routes for future research on the integration of artificial intelligence into innovative pedagogical strategies with the goal of scaffolding the learning experience and improving educational outcomes.

The integration of Generative Artificial Intelligence (GenAI) into programming education has introduced new opportunities and challenges for the teaching–learning process in higher education. While GenAI tools can support personalized learning and immediate feedback, their effectiveness depends on how they are pedagogically integrated into instructional environments. This study analyzes the relationship between GenAI-supported instructional strategies, academic context, affective factors, and demographic variables on normalized learning gain in undergraduate introductory programming courses. A multi-group quasi-experimental pre-test–post-test design was conducted involving 648 engineering students distributed across 53 course groups taught by 32 professors from 10 campuses. Four GenAI-supported instructional strategies were implemented and compared with traditional instructional conditions. Learning gains were analyzed through a linear mixed-effects model considering instructional strategy, academic program, student frustration, future intention to use GenAI, student gender, and professor gender as fixed effects, while group/professor was modeled as a random effect. Results showed that instructional strategy was the strongest predictor of learning gain, followed by academic program and student frustration. In contrast, demographic variables and future intention to use GenAI did not show significant associations with learning outcomes. The findings suggest that learning gains in programming education depend primarily on instructional design and pedagogical mediation rather than on students’ demographic characteristics or technological predisposition.

The rapid integration of Generative Artificial Intelligence (GenAI) into programming education has raised important questions regarding its impact on learning processes, conceptual understanding, and technological dependency. This study analyzed the effects of four GenAI-supported instructional strategies in an introductory programming course for undergraduate engineering students. A multi-group quasi-experimental pre-test–post-test design was implemented involving 686 students distributed across 53 class groups, from 10 campuses, taught by 32 professors. The instructional conditions included Quizzes for Self-Regulation, Github-Copilot-assisted learning, Prompt Problems with Iterative Refinement, and Flipped Learning enhanced with GenAI, which were compared against a traditional teaching approach. Learning outcomes were measured using normalized learning gain, while statistical analyses were conducted using non-parametric methods due to deviations from normality and heteroscedasticity. Results indicate that GenAI integration did not produce statistically significant overall differences in learning gain when all GenAI-supported strategies were analyzed as a single cluster compared to traditional instruction. However, differences emerged between specific strategies, with Quizzes and Copilot-based approaches having higher median learning gains than Prompt Problems and Flipped Learning strategies. No statistically significant differences associated with gender were identified. These findings suggest that the effectiveness of GenAI in programming education depends less on the mere presence of the technology and more on the pedagogical conditions under which it is integrated into the teaching–learning process.