The end of multiple choice tests: using AI to enhance assessment

TL;DR

This paper proposes replacing traditional multiple choice tests with AI-analyzed open-ended explanations to better understand student thinking and improve learning outcomes.

Contribution

It introduces a novel AI-based method to analyze student explanations, overcoming limitations of multiple choice assessments.

Findings

AI analysis provides rapid, detailed insights into student misconceptions

The approach offers actionable feedback to guide instruction

Preliminary results show improved understanding of student thinking

Abstract

Effective teaching relies on knowing what students know-or think they know. Revealing student thinking is challenging. Often used because of their ease of grading, even the best multiple choice (MC) tests, those using research based distractors (wrong answers) are intrinsically limited in the insights they provide due to two factors. When distractors do not reflect student beliefs they can be ignored, increasing the likelihood that the correct answer will be chosen by chance. Moreover, making the correct choice does not guarantee that the student understands why it is correct. To address these limitations, we recommend asking students to explain why they chose their answer, and why "wrong" choices are wrong. Using a discipline-trained artificial intelligence-based bot it is possible to analyze their explanations, identifying the concepts and scientific principles that maybe missing or misapplied. The bot also makes suggestions for how instructors can use these data to better guide student thinking. In a small "proof of concept" study, we tested this approach using questions from the Biology Concepts Instrument (BCI). The result was rapid, informative, and provided actionable feedback on student thinking. It appears that the use of AI addresses the weaknesses of conventional MC test. It seems likely that incorporating AI-analyzed formative assessments will lead to improved overall learning outcomes.