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.

Feedback is an essential component of the teaching–learning process; however, it can vary in quality due to different contexts and students’ and professors’ individual characteristics. This research explores the effect of generative artificial intelligence (GenAI) in strengthening personalized and timely feedback by initially defining an adaptable framework to integrate GenAI into feedback mechanisms defined in theoretical frameworks. We applied a between-subjects analysis in an experimental research design with 263 undergraduate students across multiple disciplines based on an approach consisting of a pretest–post-test process and control and focus groups to evaluate students’ perceptions of artificial intelligence-enhanced feedback versus traditional professor-led feedback. The results show that students who used GenAI declared statistically significantly higher satisfaction levels and a greater sense of ownership in the feedback process. Additionally, GenAI scaffolded continuous improvement and active student participation through a structured and accessible feedback environment, determining that 97% of students are willing to reuse the tool. These findings show that GenAI is a valuable tool to complement professors in the creation of an integrated feedback model. This study draws directions on future research on the combination of artificial intelligence and innovative strategies to produce a long-term impact on education.

The post-pandemic stage has accelerated the search for innovative ways that impact the teaching–learning process. Flipped learning and gamification have been used as active learning strategies to increase motivation and student learning gains. Both strategies have shown positive results when applied alone and when compared to traditional modalities. In this work, we present a quantitative study that was applied to 414 students throughout a complete course of basic programming, divided into four groups: (1) group that applied flipped learning using videos, (2) group that applied outside-class gamification, (3) group that applied both strategies, and (4) control group. A pretest–posttest process, with 96 true or false questions test, was applied to the groups to find out the normalized learning achievements of the students. A statistical analysis found that the students in groups 1 and 2 performed significantly better (+9%) than the students in the control group. In addition, the students of group 3 had a lower performance than the students of groups 1 and 2 (−10%). Our results confirm that active learning in a flipped classroom and the use of gamification can be useful and strategic tools for advancing the new way of educating in the post-pandemic period.

Active learning strategies are widely studied, but perspective on their effectiveness in complete undergraduate studies or about their contribution to closing the gender gap are still required. Challenge-based learning has been around for more than a decade. However, results have been collected in limited time and application environments, for example, one semester or one activity in a course. In this work, we present a quantitative study that was applied to results of the National Center for the Evaluation of Higher Education’s Engineering Bachelor’s Degree Standardized General Examination of 4226 students comparing those who received a traditional educational model and those who received a challenge-based learning educational model. A statistical analysis of communication and disciplinary competencies found that the traditional educational model induces a greater marginal significant result in the test. Additionally, we found that female students perform better in communication competencies while male students perform better in disciplinary competencies. Our results confirm that challenge-based learning is as effective as a traditional educational model when applied during complete undergraduate studies while developing competencies like critical thinking, long-term retention, leadership, multidisciplinary teamwork, and decision-making. Challenge based learning is a prolific learning strategy for evolving into a new way of teaching in undergraduate programs.

We introduce a distributed environment to learn by observing others and the consequences of their behavior. Students learn by competing with other students, comparing experiences, and then adopting a new behavior. The environment integrates a novel communication model, a virtual laboratory and an intelligent tutor. It was applied in a virtual robotics laboratory, where the students learn by doing and watching other people's work, in a robotics competition. The competition is related with actuators and control theory. The students design a control program, taking care of the mechanical and sensor aspects, which were explored previously in the learning environment. To participate in a contest, the students load their program, which is executed in the virtual lab. Up to 4 participants, as well as additional observers, watch the robots performance in the simulated environment. We conducted a user study with 20 students, and compared the learning gain of students that participated in competitions, against other that practiced individually. Preliminary experiments show that this environment motivates and improves student learning.