Can GPT-4 Trace Rays

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dc.contributor.authorFeng, Tony Haoranen_US
dc.contributor.authorWünsche, Burkhard C.en_US
dc.contributor.authorDenny, Paulen_US
dc.contributor.authorLuxton-Reilly, Andrewen_US
dc.contributor.authorHooper, Steffanen_US
dc.contributor.editorSousa Santos, Beatrizen_US
dc.contributor.editorAnderson, Eikeen_US
dc.date.accessioned2024-04-30T08:22:54Z
dc.date.available2024-04-30T08:22:54Z
dc.date.issued2024
dc.description.abstractRay 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.en_US
dc.description.sectionheadersExtended Reality, Emerging Technologies and Tools in CG Education
dc.description.seriesinformationEurographics 2024 - Education Papers
dc.identifier.doi10.2312/eged.20241003
dc.identifier.isbn978-3-03868-238-7
dc.identifier.issn1017-4656
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/eged.20241003
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/eged20241003
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Ray tracing; Natural language generation; Applied computing → Education
dc.subjectComputing methodologies → Ray tracing
dc.subjectNatural language generation
dc.subjectApplied computing → Education
dc.titleCan GPT-4 Trace Raysen_US
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