Feng, Tony HaoranWünsche, Burkhard C.Denny, PaulLuxton-Reilly, AndrewHooper, SteffanSousa Santos, BeatrizAnderson, Eike2024-04-302024-04-302024978-3-03868-238-71017-4656https://doi.org/10.2312/eged.20241003https://diglib.eg.org/handle/10.2312/eged20241003Ray Tracing is a fundamental concept often taught in introductory Computer Graphics courses, and Ray-Object Intersection questions are frequently used as practice for students, as they leverage various skills essential to learning Ray Tracing or Computer Graphics in general, such as geometry and spatial reasoning. Although these questions are useful in teaching practices, they may take some time and effort to produce, as the production procedure can be quite complex and requires careful verification and review. From the recent advancements in Artificial Intelligence, the possibility of automated or assisted exercise generation has emerged. Such applications are unexplored in Ray Tracing education, and if such applications are viable in this area, then it may significantly improve the productivity and efficiency of Computer Graphics instructors. Additionally, Ray Tracing is quite different to the mostly text-based tasks that LLMs have been observed to perform well on, hence it is unclear whether they can cope with these added complexities of Ray Tracing questions, such as visual processing and 3D geometry. Hence we ran some experiments to evaluate the usefulness of leveraging GPT-4 for assistance when creating exercises related to Ray Tracing, more specifically Ray-Object Intersection questions, and we found that an impressive 67% of its generated questions can be used in assessments verbatim, but only 42% of generated model solutions were correct.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Ray tracing; Natural language generation; Applied computing → EducationComputing methodologies → Ray tracingNatural language generationApplied computing → Education10.2312/eged.202410038 pages