Cheat sites and artificial intelligence usage in online introductory physics courses: what is the extent and what effect does it have on assessments?

Abstract

As a result of the pandemic, many physics courses moved online. Alongside, the popularity of internet-based problem-solving sites and forums rose. With the emergence of Large Language Models, another shift occurred. One year into the public availability of these models, how has online help-seeking behavior among introductory physics students changed, and what is the effect of different patterns of online-resource usage? In a mixed-method approach, we investigate student choices and their impact on assessment components of an online introductory physics course for scientists and engineers. We find that students still mostly rely on traditional internet resources, and that their usage strongly influences the outcome of low-stake unsupervised quizzes. However, we also find that the impact of different help-seeking patterns on the supervised assessment components of the course is non-significant.