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📅 24 de noviembre de 2025 — 17:00 (CET) 🎙️ Ponente: Linda Castañeda (Profesora e investigadora de Tecnología Educativa en la Universidad de Murcia) En esta Open Class, Linda Castañeda explora los pri...

lindacqCastañeda, Linda; Postigo-Fuentes, Ana Yara & Arroyo-Sagasta, Amaia (2025). Beyond Tools, Toward Power Structures: A Critical Review of AI in Primary Education. Revista Española de Educación Comparada (48), 73–95. https://doi.org/10.5944/reec.48.2025.45126 #videopaper #openaccess @aytranslator @amaia_arroyo

Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations between academic self-efficacy, academic stress, performance expectations, and AI dependency. It also identifies the negative consequences of AI dependency. Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations. The top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The findings provide explanations and solutions to mitigate the negative effects of AI dependency.

Advances in generative artificial intelligence show great potential for improving education. Yet little is known about how this new technology should be used and how effective it can be compared to current best practices. Here we report a randomized, controlled trial measuring college students’ learning and their perceptions when content is presented through an AI-powered tutor compared with an active learning class. The novel design of the custom AI tutor is informed by the same pedagogical best practices as employed in the in-class lessons. We find that students learn significantly more in less time when using the AI tutor, compared with the in-class active learning. They also feel more engaged and more motivated. These findings offer empirical evidence for the efficacy of a widely accessible AI-powered pedagogy in significantly enhancing learning outcomes, presenting a compelling case for its broad adoption in learning environments.