Undoubtedly, artificial intelligence (AI) tools are becoming increasingly common in people’s lives. The educational field is one of the most reflective on the importance of its adoption. Universities have made great efforts to integrate these new technologies into their classrooms, considering that every future professional will need AI skills and competencies. This article examines the importance of student perception and acceptance in adopting AI tools in higher education effectively. It highlights how students’ positive perceptions can significantly influence their motivation and commitment to learning. This research emphasizes that to integrate AI into university curricula successfully, it is essential to include its technologies in all areas of study and foster positivity among students regarding their use and training. This study’s methodology applied the validated instrument “Perception of Adoption and Training in the Use of Artificial Intelligence Tools in the Profession” to a sample of Mexican students. This exploratory analysis highlights the need for educational institutions to understand and address student perceptions of AI to design educational strategies that incorporate technological advances, are pedagogically relevant, and align with the students’ aspirations and needs.

The objective of this article has been an approximation to the way in which the representation of characters from the LGBTI + collective has occurred in the last decades in the audiovisual media. The intention of this analysis is to point out the impact that a biased or stereotyped representation can have on the constitution of incorrect or limited imaginaries that trigger situations of discrimination towards this population group. The reflection focuses on the need to pay attention to the responsibility that means for content generators, to build and present characters as authentic and attached to reality as possible, considering that this is part of the social responsibility they have towards their consumers.

This study examines complex thinking as a higher-order cognitive competence in international graduate education. Drawing on Edgar Morin’s theoretical perspective, it analyzes how master’s students perceive this competence through four interrelated dimensions: systemic, scientific, critical, and innovative thinking. A total of 491 international students from a graduate university in Japan participated in the study. Using a quantitative, cross-sectional design, data were collected with the validated eComplexity instrument and analyzed through PERMANOVA with 999 permutations. The analysis examined differences in self-perceived complex thinking by sex, academic field, nationality, and academic semester. Results showed moderately high levels of self-perceived complex thinking across the sample, with systemic and critical thinking emerging as the strongest dimensions. Significant differences were found by nationality and academic semester, while no significant differences were observed by sex or academic field. These findings suggest that students’ perceptions of complex thinking are associated with cultural and academic trajectories, although the cross-sectional and self-report design requires cautious interpretation. The study contributes to competence-based graduate education by showing that complex thinking can be examined as a multidimensional and context-sensitive form of perceived cognitive development. Educational implications are discussed in relation to curriculum design, intercultural learning, global citizenship, and inclusion in international master’s programs.

Complex thinking is increasingly relevant in international postgraduate education, yet limited evidence exists on how its components are internally articulated within multicultural graduate cohorts. This study examined the relational organization of complex thinking among 491 international master’s students enrolled at a graduate institution in Japan. A quantitative, cross-sectional design was implemented using the 25-item eComplexity instrument. An item-level regularized psychometric network was estimated to identify central and bridge nodes, node predictability, empirical communities, and exploratory structural differences across groups with sufficient sample sizes. The resulting network was predominantly positive, with prominent associations involving systemic and scientific items. Bootstrap difference tests did not support a clear ranking among the nodes with the highest strength values. Case-dropping bootstrap showed greater stability for bridge strength (CS = 0.50) than for node strength (CS = 0.30), although the relative ranking of individual nodes should be interpreted cautiously. Innovative thinking was distributed across the network rather than functioning as an autonomous component, suggesting that it was relationally linked to systemic, scientific, and critical processes. Exploratory comparisons showed no significant network differences by sex. For China and Nepal, statistical significance depended on the structural difference statistic, and no individual edge difference remained significant after correction for multiple comparisons. Differential item functioning and limited subgroup stability further restricted the interpretation of this comparison. Overall, the study extends previous factorial validation and score-based analyses by showing how psychometric network analysis can identify the components that structure and connect a multidimensional competency. These findings provide complementary evidence for competency assessment, curriculum design, and postgraduate research training in multicultural higher education contexts.