Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

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Engaging students in creating novel content, also referred to as learnersourcing, is increasingly recognised as an effective approach to promoting higher-order learning, deeply engaging students with course material and developing large repositories of content suitable for personalised learning. Despite these benefits, some common concerns and criticisms are associated with learnersourcing (e.g., the quality of resources created by students, challenges in incentivising engagement and lack of availability of reliable learnersourcing systems), which have limited its adoption. This paper presents a framework that considers the existing learnersourcing literature, the latest insights from the...

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

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Incorporating Generative Artificial Intelligence (GenAI), especially Large Language Models (LLMs), into educational settings presents valuable opportunities to boost the efficiency of educators and enrich the learning experiences of students. A significant portion of the current use of LLMs by educators has involved using conversational user interfaces (CUIs), such as chat windows, for functions like generating educational materials or offering feedback to learners. The ability to engage in real-time conversations with LLMs, which can enhance educators' domain knowledge across various subjects, has been of high value. However, it also presents challenges to LLMs' widespread, ethical, and...

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

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Generative AI, currently most visible in education through large language models (LLMs), is increasingly embedded in students’ everyday learning practices, supporting tasks such as writing, coding, reasoning, and analysis. Yet its educational value remains uncertain because systems designed to improve immediate task performance may also weaken the cognitive and metacognitive processes that support durable learning. This paper addresses this learning–performance paradox by asking how AI systems can be designed to support learning rather than merely produce better outputs. To address this, in this paper, we make three contributions. First, we synthesise current LLM-based study support with...

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