How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial intelligence

TL;DR

This paper introduces the MAGE framework to evaluate and improve assessment questions' vulnerability to generative AI, focusing on critical thinking skills and aiding educators in designing more robust digital assessments.

Contribution

It presents the novel MAGE framework for systematically critiquing assessment questions' susceptibility to AI, enhancing assessment design for critical thinking evaluation.

Findings

MAGE framework effectively identifies AI vulnerabilities in assessment questions.

Application to ChatGPT-4 demonstrates practical utility.

Framework supports tailored assessment design to mitigate AI misuse.

Abstract

Generative AI such as those with large language models have created opportunities for innovative assessment design practices. Due to recent technological developments, there is a need to know the limits and capabilities of generative AI in terms of simulating cognitive skills. Assessing student critical thinking skills has been a feature of assessment for time immemorial, but the demands of digital assessment create unique challenges for equity, academic integrity and assessment authorship. Educators need a framework for determining their assessments vulnerability to generative AI to inform assessment design practices. This paper presents a framework that explores the capabilities of the LLM ChatGPT4 application, which is the current industry benchmark. This paper presents the Mapping of questions, AI vulnerability testing, Grading, Evaluation (MAGE) framework to methodically critique their assessments within their own disciplinary contexts. This critique will provide specific and targeted indications of their questions vulnerabilities in terms of the critical thinking skills. This can go on to form the basis of assessment design for their tasks.