Developing evaluative judgement for a time of generative artificial intelligence

Abstract

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 judgement of generative AI processes; and (3) generative AI assessment of student evaluative judgements. We argue for developing students’ capabilities to identify and calibrate quality of work – uniquely human capabilities at a time of technological acceleration – through existing formative assessment strategies. These approaches circumvent and interrupt students’ uncritical usage of generative AI. The relationship between evaluative judgement and generative AI is more than just the application of human judgement to machine outputs. We have a collective responsibility, as educators and learners, to ensure that humans do not relinquish their roles as arbiters of quality.