Progress has been made in recent decades toward achieving gender equality, but today, the gender gap is still noticeable, especially in STEM fields. In support of Goal 5 of the 2030 Sustainable Development Agenda: achieve gender equality and empower all women and girls, we analyze the context of a private university in northeastern Mexico using a sample of 249 students (157 males and 92 females) enrolled in the first-year engineering course Physics I. The sample presents better academic performance in favor of women by the end of the course as reported through the final course score (F); thus, we explore potential gender differences in student profiles, such as their motivation and level of procrastination using Kruskal–Wallis correlation tests, and measuring the effect size with Cohen’s d. Our tests reported here reveal significant differences in extrinsic motivation (EMO) and intrinsic motivation (IMO), where females obtained higher means in IMO, while males reported higher levels of procrastination (PRO). Contrary to other cases in the literature, the sample presents better academic performance in favor of women. Our findings here aim to encourage programs and strategies that strengthen women’s intrinsic motivation to support women’s empowerment and keep reducing the gender gap.

This classroom-based case study examines how an AI-mediated Socratic dialogue, implemented through ChatGPT, can support students’ engagement and perceived learning in undergraduate thermodynamics. Conducted in a first-year engineering physics course at a private university in northern Mexico, the activity invited small student groups to interact with structured prompts designed to promote inquiry, collaboration, and reflective reasoning about the adiabatic process. Rather than functioning as a source of answers, ChatGPT was intentionally positioned as a mediating scaffold for Socratic questioning, prompting students to articulate, examine, and refine their reasoning. A mixed-methods approach was employed, combining a 10-item Likert-scale survey with construct-level statistical analysis of two focal dimensions: perception of learning and engagement, including an exploratory comparison by gender. Results indicated consistently high levels of perceived learning and engagement across the cohort, with average scores above 4.5 out of 5. At the construct level, no statistically significant gender differences were observed, although a single item revealed higher perceived learning among female students. Overall, the findings suggest that the educational value of ChatGPT in this context emerged from its integration within a Socratic, inquiry-oriented pedagogical design, rather than from the technology alone. These results contribute to ongoing discussions on the responsible and pedagogically grounded integration of generative AI in physics education and align with Sustainable Development Goal 4 (Quality Education).

This paper explores how the entry profiles of engineering students are related to their academic performance during the first year of university in a sample of 255 first-year engineering students (77 females and 178 males) at a university in Northeast Mexico. The predictors used were the high school grade point average (HSGPA), Scholastic Aptitude Test (SAT) results, the first admission test, and a Spanish adaptation of the Survey of Study Habits and Attitudes Test (SSHA) from Brown and Holtzman. The SSHA adaptation was tested for internal consistency reliabilities via Cronbach’s alpha globally (0.92) and for the following categories: delay avoidance (DA: 0.79), work methods (WM: 0.81), teacher approval (TA: 0.89), and educational acceptance (EA: 0.74). The results were compared with those of other studies to validate their consistency. To assess the different entry profiles between high- and low-achieving students, we performed a Kruskal–Wallis test and found significant differences (p < 0.001) between both profiles for all variables. We then measured the relationships between the variables and academic success by constructing a correlation table, where HSGPA, SAT, and DA showed the highest correlations: 0.61, 0.40, and 0.36, respectively. With these outcomes, a predictive model via a logistic regression ($R^2=0.52$) was built to forecast first year academic performance in the specific context.

In sustainable education, it is important to analyze student diversity in order to create strategies that allow for the implementation of inclusive education based on the differences observed among students. To achieve this, a sample of 321 first-year engineering students (107 females and 214 males) at a private university in northeast Mexico was analyzed during the 2020 academic year. Students were classified according to their gender, engineering program, and the development of their multiple intelligences according to Howard Gardner theory of multiple intelligences. To verify the effect of gender and program factors on the development of multiple intelligences, Kruskal–Wallis tests were performed with α = 0.05. The analysis of the effects of gender identified significant differences in four intelligences: linguistic and interpersonal (for which the female students obtained higher mean scores) and mathematical and visual (for which the male students obtained higher mean scores). The analysis of the effects of the engineering program identified significant differences in five intelligences: mathematical, visual, and musical (for which civil engineering students obtained a higher mean score than the students in the other programs); kinesthetic (for which computer science students obtained a lower mean score than students in the other programs); and naturalistic (for which sustainability engineering students obtained a higher mean score than students in the other programs). These differences allowed us to observe the characteristics of the students and to develop more inclusive courses in order to make the teaching and learning process more optimal and sustainable.

This study examines pseudoscientific beliefs among university students, focusing on gender and faculty differences at a private university in northeastern Mexico. Data were collected from 794 students (38% male, 62% female) across six academic disciplines using the Pseudoscience Endorsement Scale (PES). Statistical analyses included the Kruskal–Wallis test to assess group differences and the Wilcoxon rank sum test for pairwise comparisons. Results revealed significant gender differences (p < 0.001), with female students scoring higher. Scores also varied significantly across faculties (p < 0.001), with students in Business and Arts, Architecture, and Design exhibiting the highest levels of endorsement of pseudoscientific beliefs. In contrast, those in Engineering and Technology scored the lowest. These findings underscore the need for targeted educational interventions to mitigate pseudoscientific beliefs and enhance students’ understanding of STEM fields. The study aligns with the United Nations’ Sustainable Development Goal 4, which advocates for inclusive, equitable, and quality education that fosters critical thinking and lifelong learning. It emphasizes the importance of accounting for gender and academic background when addressing students’ belief systems.

Inspired by the principles of quantum mechanics, we constructed a model of students’ misconceptions about heat and temperature, conceptualized as a quantum system represented by a density matrix. Within this framework, the presence or absence of misconceptions is delineated as pure states, while the probability of mixed states is also considered, providing valuable insights into students’ cognition based on the mental models they employ when holding misconceptions. Using the analysis model previously employed by Lei Bao and Edward Redish, we represented these results in a density matrix. In our research, we utilized the Zeo and Zadnik Thermal Concept Evaluation among 282 students from a private university in Northeast Mexico. Our objective was to extract information from the analysis of multiple-choice questions designed to explore preconceptions, offering valuable educational insights beyond the typical Correct–Incorrect binary analysis of classical systems. Our findings reveal a probability of 0.72 for the appearance of misconceptions, 0.28 for their absence, and 0.43 for mixed states, while no significant disparities were observed based on gender or scholarship status, a notable difference was observed among programs (p < 0.05). These results are consistent with the previous literature, confirming a prevalence of misconceptions within the student population.