Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

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Many engineering disciplines require problem-solving skills, which cannot be learned by memorization alone. Open-ended textual exercises allow students to acquire these skills. Students can learn from their mistakes when instructors provide individual feedback. However, grading these exercises is often a manual, repetitive, and time-consuming activity. The number of computer science students graduating per year has steadily increased over the last decade. This rise has led to large courses that cause a heavy workload for instructors, especially if they provide individual feedback to students. This article presents CoFee, a framework to generate and suggest computer-aided feedback for...

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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University courses with hundreds of students have become common, particularly during early years of university studies. The sheer scale of these courses limits traditional instruction, shifting it towards a one-to-many mode of delivery. This shift reduces student–instructor interaction and tailored instructor feedback which are crucial for student success. Automated feedback systems allow scaling feedback, but they often reduce instructor contributions to student learning. This paper investigates how emerging technologies can support, rather than replace, instructors in tailoring their teaching and feedback to identify and correct student knowledge gaps at scale. To address this challenge,...

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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Introduction: Generative AI is reshaping programming education, yet its effects on conceptual learning, intrinsic motivation, and cognitive load remain unclear. This study tests whether assistance deepens understanding or primarily boosts task completion, and how scaffolded versus answer-giving designs matter. Objectives: This study compares performance, learning, cognitive load, frustration, and motivation across three AI support types, and examines students’ perceptions. Methods: A three-arm randomized controlled trial was conducted in an introductory programming (CS1) course at TUM (N=275). Participants completed a 90-minute exercise on concurrency, implementing a parallel sum with...

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