Transformar

Development of an interdisciplinary educational resource to foster active and motivated learning

Francisco Javier Sainz Muñoz, Salvador Baena Morales, Alberto Ferriz Valero — Tue, 24 Mar 2026 00:00:00 -0300

This article presents the design and implementation of Kahbom, a low-cost, game-based device intended to foster active and motivated learning. The study follows a descriptive qualitative design with 53 upper-secondary students (16–17 years old) from a public school in Spain. The device combines physical levers, light and sound feedback, and time-limited semantic challenges in which small groups must identify an impostor word to “defuse the bomb.” Data were collected through an open-ended questionnaire and analysed using ATLAS.ti. Five main categories emerged from students’ perceptions: positive acceptance, motivation, exploration, interest, and innovation. Results show high levels of enjoyment, cooperative engagement, and curiosity, as well as sustained attention during the activity. The discussion highlights the potential of hybrid ludic-technological resources for formative assessment, inclusive practice, and interdisciplinary learning, and outlines future lines of research to examine long-term motivational and learning effects.

Ethical opportunities and challenges of AI in mental health in Latin America

Juan P. López, Fernando Vera, Tobías A. Parodi — Tue, 24 Mar 2026 00:00:00 -0300

Artificial intelligence (AI) has emerged as a promising alternative to address the mental health crisis in Latin America and the Caribbean, characterized by a high prevalence of disorders, a shortage of professionals, and profound structural inequalities. This study aims to critically analyze the opportunities, applications, and ethical challenges of AI in mental health within the regional context. Using a critical and integrative narrative review, academic literature and normative documents published between 2020 and 2025 are examined, including empirical studies, reviews, and regulatory frameworks relevant to the region. The results reveal an emerging landscape in predictive diagnosis, emotional monitoring, and chatbots, yet limited by algorithmic biases, the lack of representative data, and the absence of specific regulations. It is concluded that the effective implementation of AI in mental health requires a responsible development approach that articulates ethical governance, strengthening of local capacities, and sociocultural adaptation, prioritizing equity and protection of vulnerable populations.

Inteligencia artificial como herramienta de apoyo terapéutico en salud mental: Perspectiva América Latina. Revisión sistemática

Juan P. López — Tue, 24 Mar 2026 00:00:00 -0300

La presente investigación realiza una revisión sistemática sobre el uso de la Inteligencia Artificial (IA) en el tratamiento de la salud mental, con énfasis en América Latina. Se analizan los métodos y modelos de IA más utilizados, como el procesamiento de lenguaje natural, el aprendizaje automático y las redes neuronales profundas, empleados en la detección, monitoreo y apoyo terapéutico de trastornos como ansiedad, depresión y esquizofrenia. Además, se examinan las principales arquitecturas tecnológicas, los desafíos éticos y la ausencia de marcos regulatorios regionales. Los resultados pretenden identificar oportunidades de desarrollo tecnológico aplicable desde la ingeniería de sistemas, orientadas a mejorar la accesibilidad, la eficacia y la equidad en la atención en salud mental. Se concluye la necesidad de impulsar investigaciones interdisciplinarias y marcos normativos que promuevan una integración responsable de la IA en este campo.

Precision education in the algorithmic age: Toward a conceptual framework for personalized learning in higher education

Fernando Vera — Tue, 24 Mar 2026 00:00:00 -0300

This article presents the design and implementation of Kahbom, a low-cost, game- Precision education in the algorithmic age has gained increasing relevance as higher education institutions seek to respond more effectively to student diversity, learning variability, and evolving demands for personalized teaching. Assisted by artificial intelligence, this emerging approach moves beyond traditional standardization by enabling more adaptive, data-informed, and responsive learning environments. This conceptual analysis examines precision education as a developing educational framework shaped by the integration of artificial intelligence, learning analytics, adaptive systems, and pedagogical decision-making. The article identifies its defining attributes, including personalization, predictive support, timely intervention, and continuous adjustment to learners’ needs, progress, and contexts. It also discusses key antecedents, such as technological infrastructure, ethical governance, data literacy, and faculty preparedness, as well as its potential outcomes for engagement, equity, and academic success. The paper argues that AI-assisted precision education should be understood as a human-centered and ethically guided framework that strengthens, rather than replaces, pedagogical judgment in higher education.