Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

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Generative artificial intelligence (GenAI) has created significant assessment challenges in higher education. Universities and teachers have responded to these in various ways, including by way of new policies, revised assessment formats, and technological safeguards. These responses are typically predicated on an assumption that AI in assessment is a problem that can be solved if only the right approach can be found. However, in this paper we argue that GenAI may not be that kind of problem at all. Drawing on interviews with 20 teachers responsible for assessment design within a large Australian University, this paper applies Rittel and Webber framework of wicked problems to analyse educators...

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

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Generative artificial intelligence (AI) has rapidly increased capacity for producing textual, visual and auditory outputs, yet there are ongoing concerns regarding the quality of those outputs. There is an urgent need to develop students’ evaluative judgement – the capability to judge the quality of work of self and others – in recognition of this new reality. In this conceptual paper, we describe the intersection between evaluative judgement and generative AI with a view to articulating how assessment practices can help students learn to work productively with generative AI. We propose three foci: (1) developing evaluative judgement of generative AI outputs; (2) developing evaluative...

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

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The rapid integration of Generative Artificial Intelligence (GenAI) into educational contexts has presented both opportunities and challenges for students seeking and using feedback. While AI-generated feedback can offer increased access, timely responses and personalised insights, concerns about the quality of AI-generated feedback still persist, including issues of bias, factual inaccuracies, and homogenisation. This study investigates how students use, value and trust AI-generated feedback compared to feedback from educators. This paper draws on a large-scale cross-sectional survey administered across four major Australian universities. A quantitative analysis of ...

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