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.

Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

This paper presents a study that evaluates gamification as a strategy to improve learning in higher education. This research aims to determine the effects of gamification on the teaching-learning process by implementing a virtual educational escape room. This learning strategy was applied to four groups of students studying the Computational Modeling of Movement course in the first semester of the B.S. in Computer Science and Technology program at Tecnologico de Monterrey. To determine their perception of their experience in the activity of an escape room and the elements they find valuable in their learning, we performed a descriptive analysis based on an online questionnaire with items divided into three dimensions. The students' responses were measured with a Likert scale, and their reliability was confirmed using Cronbach's alpha coefficient. The results showed that the students positively valued the application of the escape room as a tool and a didactic proposal to improve the teaching-learning process. In addition, implementing the virtual educational escape room was perceived as rewarding by both students and teachers, motivating the latter to implement innovative teaching strategies that allowed them to strengthen their academic work.

The efficiency of epidemiological models is based on two characteristics: speed and accuracy. While Equation-Based Modeling (EBM) enables agile calculations, its approximations lack precision because they exclude population heterogeneity. On the other hand, Agent-Based Modeling (ABM) integrates individuality into the model, but it requires considerable processing time to obtain results. In this work, a sequential strategy is developed to generate ABM that preserves population heterogeneity while reducing processing time. This model is obtained through an optimization process that identifies the set of parameters whose approximation to the pandemic’s real data minimizes error. The process begins with Particle Swarm Optimization (PSO), whose cost function is implemented via EBM, enabling comparison of the population projections against the real data recorded by health institutions in three major Mexican cities. The resulting parameters are transferred to an ABM modeled in NetLogo and validated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The results show that calibrated parameters during the optimization stage, when applied in ABM simulations, generate epidemiological scenarios that reflect the pandemic’s actual behavior. These findings indicate that EBM-based calibration supports ABM experimentation while reducing the computational cost of agent-level optimization.