Generative artificial intelligence (GenAI) is emerging as a transformative technology in higher education, particularly in programming instruction. However, its impact on learning, motivation, and the educational environment must still be fully understood. This study aims to determine the capacity of GenAI to generate effective computer programming learning in STEM university students, comparing it with active learning methods based on video. An experiment was conducted with 40 computer engineering students divided into two groups: one using GenAI (Google Gemini 1.5) and another employing educational videos. Pre- and post-tests of knowledge and the Intrinsic Motivation Inventory (IMI) were applied to evaluate learning, intrinsic motivation, and the learning environment. No significant differences in learning were found between the groups. However, GenAI significantly increased perceived autonomy and reduced perceived effort and pressure, while video-based learning significantly improved perceived competence. These findings suggest that both methods seem to motivate in diverse ways and that they could complement each other in an integrated teaching approach, offering new perspectives for designing programming learning environments in higher education.

This study investigates the differential effects of gamification on learning outcomes, motivation, and usability perceptions in an introductory programming course, focusing on gender differences. While gamification has shown promise for increasing student engagement in educational settings, its impact may vary across genders. An experimental study was conducted with 88 university students randomly assigned to gamified and non-gamified groups. Learning gains were assessed through pre- and post-tests, motivational factors were measured via questionnaires, and usability perceptions were evaluated using the Technology Acceptance Model (TAM) questionnaire. Results revealed that women learned significantly more than men in the non-gamified condition, while men outperformed women in the gamified condition. Furthermore, men reported higher enjoyment, usefulness, and comfort with the gamified tool than women. Interestingly, both genders indicated greater satisfaction with the non-gamified version. These findings contribute nuanced insights into how gamification impacts genders differently in programming education, suggesting that gamification may hinder women’s learning while modestly benefiting men. The study highlights the importance for practitioners to carefully consider gender dynamics when implementing gamified approaches, potentially offering customization options or blended techniques to optimize learning outcomes for all students in programming education.

Learning material and adaptive learning are the most important topics when we talk about the learning process of every student. Currently, the biggest problem is that it is not possible for university teachers to help each and every student with every minor problem they have or aspect of the teaching material they do not understand. The aim of this work is propose an adaptive e-learning platform that has three different teaching proposals. The participants are engineering students who are studying subjects of object-oriented programming in Java language and are differentiated with a profile test based on the works of Neil D. Fleming. The subject of inheritance has already been tested in four opportunities showing positive results in chi-square and hypothesis test, where students sustain that their learning process was improved.

This paper presents the design of an affective algorithm for implementing autonomous decisionmaking systems that incorporate an emotional stabilizer mechanism for the use in the stock market domain. Emotions have a direct influence on human decision-making processes. Non-deterministic behavior in humans can be partially explained by emotions. In this sense, an artificial emotion can be implemented as a synthetic abstraction derived from the observation of human emotions. This paper presents studies related to emotional stability and emotional regulation. However, to the best of our knowledge, it is not possible to identify studies that define a relationship between the regulation of artificial emotions and the decision effectiveness of autonomous decision-making systems, specifically for the stock market domain. With the aim to improve investment results in the stock market domain, a mechanism based on artificial emotions is presented that was designed as a single layer of decision criteria defined by both rational and emotional perspectives. Along with the proposal of an emotional stabilizer mechanism, different values of emotional bandwidths and emotional update rates were tested, aiming to explore the degree of influence of these parameters on the effectiveness of investment decisions made by artificial investors. Our proposal considers the definition of an experimental scenario based on official data from the New York Stock Exchange. The results are promising and include a linear regression analysis. The test results suggest that the use of autonomous affective decision-making systems with emotional stabilization can improve the effectiveness of the decision made.