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The advent of large language models is reshaping computing education. Recentresearch has demonstrated that these models can produce better explanationsthan students, answer multiple-choice questions at or above the class average,and generate code that can pass automated tests in introductory courses. Thesecapabilities have prompted instructors to rapidly adapt their courses andassessment methods to accommodate changes in learning objectives and thepotential for academic integrity violations. While some scholars have advocatedfor the integration of visual problems as a safeguard against the capabilitiesof language models, new multimodal language models now have vision and languagecapabilities that may allow them to analyze and solve visual problems. In thispaper, we evaluate the performance of two large multimodal models on visualassignments, with a specific focus on Parsons problems presented across diversevisual representations. Our results show that GPT-4V solved 96.7\% of thesevisual problems, struggling minimally with a single Parsons problem.Conversely, Bard performed poorly by only solving 69.2\% of problems,struggling with common issues like hallucinations and refusals. These findingssuggest that merely transitioning to visual programming problems might not be apanacea to issues of academic integrity in the generative AI era.

More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems