Tipo: artículo

Metodología: revisión bibliográfica

Temas: inteligencia artificial

Resumen:

Texto completo

Artificial intelligence (AI) is rapidly being integrated into educational contexts, promising personalized support and increased efficiency. However, growing evidence suggests that the uncritical adoption of AI may produce unintended harms that extend beyond individual learning outcomes to affect broader societal goals. This paper examines the societal implications of AI in education through an integrative framework with four interrelated dimensions: cognition, agency, emotional well-being, and ethics. Drawing on research from education, cognitive science, psychology, and ethics, we synthesize existing evidence to show how AI-driven cognitive offloading, diminished learner agency, emotional disengagement, and surveillance-...

Ver ficha completa

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

In recent decades, we have witnessed the democratization of AI-powered Educational Technology (AI-EdTech). However, despite the increased accessibility and evolving technological capabilities, its adoption is accompanied by significant challenges, predominantly rooted in social and psychological aspects. At the same time, limited research has been conducted on human factors, especially trust, influencing students' readiness and willingness to adopt AI-EdTech. This study aims to bridge this gap by addressing the multidimensional nature of trust and developing a new instrument for measuring students' perceptions of adopting AI-EdTech. With 665 student responses, we employ Exploratory and...

Ver ficha completa

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

While feedback is essential for guiding student learning, providing timely and personalized guidance in large-scale educational settings remains a significant challenge. Generative AI offers a scalable solution, yet little is known about students’ perceptions of AI-generated feedback. In this paper, we aim to investigate how the identity of the feedback provider (human vs. AI) affects students’ ability to assess feedback quality and whether their judgments are biased. We propose a comprehensive rubric for assessing the pedagogical quality of formative feedback. We use it to compare the objective quality of AI-generated and human-crafted feedback (N = 979). Next, using data collected from...

Ver ficha completa