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.