Liliana Pedraja-Rejas

Teacher education programs should transcend the traditional emphasis on disciplinary knowledge to incorporate the development of transversal competencies, particularly critical thinking. This study explores the relationship between critical thinking and student satisfaction, while also exploring potential differences in perception based on student gender. A questionnaire was administered to 134 fourth-year preservice elementary education students across nine Chilean universities. Findings indicate that students’ self-perceived critical thinking abilities are slightly higher than their reported academic satisfaction, with no statistically significant differences between male and female participants. A moderate yet statistically significant positive correlation was identified between critical thinking and student satisfaction. Additionally, regression analysis revealed that critical thinking accounts for 17.18% of the variance in student satisfaction. Taken together, these results contribute empirical evidence to ongoing debates on how transversal competencies shape students’ academic experiences in teacher education. They also suggest that approaches aimed at strengthening critical thinking—commonly associated with active and student-centered pedagogies—may have broader relevance for enhancing students’ engagement with and evaluation of their training programs. In this sense, the study offers insights that may inform curricular design, pedagogical practices, and institutional strategies seeking to improve the quality and perceived value of teacher education, while pointing to the need for further research on the contextual and pedagogical conditions under which critical thinking most effectively supports students’ academic experiences and outcomes.

This article analyzes the development of intercultural communicative competence (ICC) in initial teacher education (ITE) at three regional Chilean universities located in border contexts. Based on 46 semi-structured interviews with students, teachers, and academic authorities, seven categories were identified that reveal a marginal and fragmented incorporation of interculturality into the curriculum, as well as a persistent gap between institutional discourse and actual formative experiences. The study is based on Munezane’s (2025) tree model of ICC, which organizes competence into interrelated ethical, formative, and practical dimensions, represented as the roots, trunk, and leaves of a dynamic, situated process of intercultural development. It employs an intersectional approach that makes inequalities associated with gender, ethnicity, and territory. The results show that the development of ICC is conditioned by institutional, epistemic, and relational factors, and that its strengthening requires comprehensive curricular policies and critical, context-sensitive teacher education. The study concludes that promoting a situated intercultural education that recognizes and addresses these inequalities represents both a challenge and an opportunity to strengthen the ethical and political commitment of teaching, fostering more just and democratic educational practices in contexts of diversity and structural exclusion.

Intelligence tests predict academic achievement, but their use in educational decision-making remains contested. We develop a decision-analytic framework, centered on a staged decision architecture, to determine when, for whom, and for which educational decisions intelligence testing adds value beyond grades, achievement measures, and contextual evidence. Drawing on psychometric scholarship, a generative account of achievement, and illustrative decision scenarios, we distinguish incremental validity from decision utility. Incremental validity refers to the predictive gain obtained by adding cognitive measures, whereas decision utility refers to the net benefit of using those measures once base rates, capacity constraints, error costs, fairness, and legitimacy are considered. We use the framework to identify conditions in which intelligence testing is expected to be most informative, especially educational transitions, contexts with uneven opportunity, and discrepancy-focused decisions such as underachievement or twice-exceptionality. We also specify minimum conditions for responsible use, including intended use, construct representation, reliability or precision, measurement comparability, predictive bias checks, and monitoring of distributional impact. We conclude that intelligence testing should be used conditionally and sequentially, with achievement and contextual indicators used first and cognitive assessment added only when it is likely to change the decision.

Dropout in higher education remains a persistent challenge with academic, institutional, and social consequences. Identifying students’ dropout intention before formal withdrawal occurs may provide useful evidence for preventive support strategies. This study examines dropout intention among undergraduate engineering students at a Chilean university. Using survey data from 189 students, five variables were operationalized: personal self-regulation, mental health, institutional perception, socio-economic conditions, and academic motivation. Two interpretable models were estimated: multivariable logistic regression and a shallow decision tree. The logistic regression model showed satisfactory explanatory capacity and identified socio-economic conditions and personal self-regulation as significant protective predictors of dropout intention, while mental health, institutional perception, and academic motivation were not significant after adjustment. The decision tree provided a complementary rule-based segmentation and confirmed the prominence of socio-economic conditions and personal self-regulation, while also indicating a secondary contribution of mental health. These findings suggest that dropout intention in engineering education is shaped primarily by structural conditions and self-regulatory resources rather than by institutional perception or academic motivation alone. The study provides empirically grounded evidence to support early identification and student support strategies in higher education.

The expansion of artificial intelligence (AI) in education has generated new training demands for teachers that go beyond technical skills, requiring critical competencies capable of addressing the ethical, pedagogical, and social challenges posed by this technology. Understanding how teachers develop the dispositions and skills needed to integrate AI reflectively has become a priority for educational systems. This study analyzes the relationship between general self-efficacy, critical thinking awareness, and AI literacy among pre-service and in-service teachers in Chile. A non-experimental quantitative design with a correlational-comparative approach was applied to an intentional sample of 1,169 participants. Validated scales and multivariate analyses were used to examine group differences and predictive relationships. The results indicate significant differences between pre-service and in-service teachers across all studied variables. Critical thinking awareness emerged as the strongest predictor of teaching self-efficacy in both profiles, underscoring its central role in fostering autonomous and informed pedagogical decision-making. AI literacy showed differential effects: positive among in-service teachers and ambivalent among pre-service teachers, suggesting that professional experience conditions how technological knowledge contributes to perceived competence. Gender gaps were also identified. The findings highlight that strengthening critical thinking awareness is a strategic pathway for enhancing teaching self-efficacy and promoting more thoughtful, ethical, and context-sensitive integration of AI in education, especially in scenarios of accelerated digitalization.

Professional education increasingly requires graduates to make decisions in complex systems marked by multiple stakeholders, feedback, delays, uncertainty, and unintended consequences, yet systems thinking is still often taught as a set of disconnected tools rather than as an integrated professional practice. This conceptual paper adopts an integrative theory-building approach to develop a unified architecture for systems thinking in professional education, drawing purposively on systems traditions, practice-based learning, assessment scholarship, and emerging work on generative artificial intelligence (GenAI). The paper proposes four iterative practices (sensemaking and boundary setting, co-modelling and causal representation, intervention reasoning, and meta-learning) as the core architecture for learning systems thinking in professional contexts. It further translates this architecture into indicative implications for curriculum sequencing, authentic tasks, and assessment, while positioning GenAI as a cross-cutting support/risk layer that can assist iteration and critique but also introduce predictable risks such as fabricated causal links, overreliance, and false mastery. To address these risks, the paper outlines governance conditions based on traceability, uncertainty checks, stakeholder validation, and process-based assessment. Overall, the framework provides a design-oriented basis for teaching, assessing, and governing systems thinking in contemporary professional education and a foundation for future empirical testing.

Artificial intelligence (AI) is rapidly transforming higher education, yet limited empirical evidence exists on how students experience the emotional, cognitive, and ethical tensions associated with AI, particularly in Latin American contexts. This study addresses this gap by examining patterns of adoption, perceived usefulness, and the ambivalent experiences that arise when engaging with AI tools for academic learning. A questionnaire combining closed and open-ended questions was administered to 170 engineering students from a Chilean public university. A mixed-methods design was used to analyse the data: quantitative analyses identified adoption patterns and perceived usefulness, while qualitative thematic analysis captured emerging emotional, ethical, and motivational tensions. The results showed high adoption (73.5%), driven by the pragmatic usefulness of saving time, understanding concepts, and improving work. Although the overall perception was positive, a deep ambivalence was identified, with enthusiasm and confidence coexisting with ethical (plagiarism), cognitive (dependence), and technical (reliability) concerns. It is concluded that the effective integration of AI transcends technological access and requires an institutional strategy that promotes critical digital literacy, clear policies, and support programmes that address competency and gender gaps, ensuring ethical and equitable adoption that enhances learning without compromising the development of critical thinking.

Learning has established itself as a fundamental pillar for the adaptation and continuous growth of organisations. This article analyses the impact of organisational learning on innovation and institutional performance in universities, focusing on five interdependent dimensions: organisational culture, knowledge management, organisational memory, continuous feedback, and dynamic capabilities. Through a narrative review of the specialised literature, a systemic framework is proposed that conceives organisational learning as an integral and strategic process, where each dimension contributes in key ways to institutional strengthening. Organisational culture fosters shared values and readiness for change; knowledge management enables the generation and application of relevant knowledge; organisational memory guarantees the continuity and transfer of learning; constant feedback facilitates adaptation; and dynamic capabilities prepare the university to face complex and changing contexts. As a practical contribution, an operational agenda is designed that links each dimension with a strategic action, a follow-up indicator, a suggested institutional tool, and theoretical references. This proposal seeks to offer an adaptable roadmap for management teams, quality assurance units, and university management training spaces.

This study investigates the influence of chirality on the dynamic susceptibility of concentric nanotori via micromagnetic simulations. The aim is to analyze the ferromagnetic resonance characteristics of coupled nanotori structures and compare them across various ring separation distances, thus providing an insight into how vortex configurations with identical or differing chiralities affect their dynamic properties. We analyze the energetic differences between the two vortex configurations and find them to be negligible; however, these minor differences suffice to explain the significant discrepancies in the demagnetization field observed between the nanotori. We examine the dynamic susceptibility spectrum and the spatial localization of the ferromagnetic resonance modes for different nanotori separations. Our findings demonstrate that the resonant oscillation frequencies are significantly influenced by the magnetostatic interactions between the nanotori, which can be effectively modulated by varying the distance between them. Furthermore, for smaller separations, the frequency peaks in the dynamic susceptibility markedly diverge between the two vortex configurations, demonstrating that the observed differences in the demagnetization field between the rings strongly influence the frequency response. In summary, our results indicate that both the inter-ring distance and the vortex configuration play a crucial role in determining the frequency response of the system.