Is AI changing learning and assessment as we know it? Evidence from a ChatGPT experiment and a conceptual framework

Abstract

ChatGPT, a state-of-the-art chatbot built upon Open AI's generative pre-trained transformer, has generated a major public interest and caused quite a stir in the higher education sector, where reactions have ranged from excitement to consternation. This paper therefore examines the potential impact of ChatGPT on learning and assessment, using the example of academic essays, being a major form of assessment with widespread applications of ChatGPT. This provides an opportunity to unpack broader insights on the challenge of generative AI's to the relevance, quality and credibility of higher education learning in a rapidly changing 21st century knowledge economy. We conducted a quasi-experiment in which we deployed ChatGPT to generate academic essays in response to a typical assessment brief, and then subjected the essays to plagiarism checks and independent grading. The results indicate that ChatGPT is able to generate highly original, and high quality, contents from distinct individual accounts in response to the same assessment brief. However, it is unable to generate multiple original contents from the same account, and it struggled with referencing. The discussion highlights the need for higher education providers to rethink their approach to assessment, in response to disruption precipitated by artificial intelligence. Thus, following the discussion of empirical data, we propose a new conceptual framework for AI-assisted assessment for lifelong learning, in which the parameters of assessment extend beyond knowledge (know what) testing, to competence (know how) assessment and performance (show how) evaluation.

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