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

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