EG2025

Permanent URI for this community

https://diglib.eg.org/handle/10.2312/3607134

Browse

Browsing EG2025 by Subject "Applied computing → Education"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Computer Graphics Instructors' Intentions for Using Generative AI for Teaching
    (The Eurographics Association, 2025) Magana, Alejandra J.; Felkel, Petr; Žára, Jiří; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
    Background: Generative AI has significant potential to support learning processes, such as generating personalized content matching individual student needs. It also has the potential to support teaching processes by assisting instructors in generating content, assessing students, or supporting practice. This study investigates how computer graphics instructors have used generative AI or are planning to use generative AI to support their teaching. We implemented an anonymous online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) methodology and distributed it among Eurographics members. The research questions were: (1) What are computer graphics instructors' ways of integrating generative AI for teaching and learning purposes? (2) What are the influencing factors computer graphics instructors have considered for integrating generative AI for teaching and learning purposes? Results: Between October 2024 and January 2025, we received 12 responses. Findings suggest that while some instructors have integrated generative AI into some aspects of their teaching, others have not and are hesitant to adopt them in the future, particularly as related to generating content for creating assignments such as lecture notes, summaries, teaching examples, etc., and supporting their assessment processes such as providing feedback, evaluating assignments, or grading exams. However, instructors were more open to using generative AI to support their teaching practices, particularly as related to pedagogy, such as providing students with interactive practice problems and supporting their creative content generation. Conclusion: Findings from the study identified the level of acceptance among computer graphics instructors, primarily full professors, and their experiences and intentions for using generative AI. To get a better understanding of the adoption of generative AI in the field of computer graphics education, we would like to invite the community to share their experiences and future intentions via the survey, which will remain open for additional input.
  • Thumbnail Image
    Item
    Tracing Brilliance: Analysing Student Performance in Ray Tracing and Problem-Solving Capabilities and Approaches
    (The Eurographics Association, 2025) Liu, Enyu; Wünsche, Burkhard C.; Luxton-Reilly, Andrew; Lange-Nawka, Dominik; Hooper, Steffan; Thompson, Samuel E. R.; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
    Learning computer graphics is considered challenging due to the diverse skills required, including programming, mathematics, physics, problem solving skills, and spatial reasoning skills. Ray tracing is an important rendering technique in computer graphics but many students find the topic difficult. In this paper, we investigate problems students encounter when solving ray tracing questions by analyzing student answers to assessment questions for a third-year introductory Computer Graphics module. Our findings suggest that the difficulty of ray tracing questions is related to the challenge of integrating conceptual knowledge, programming skills, and mathematical concepts into problem-solving strategies. Our results provide insights how this effects students' problem solving capability, i.e., many students seem unable to make appropriate mental models of problem statements and hence give answers which violate fundamental properties of the problem statement. We also observed that many students solved problems through trial and error instead of identifying the cause of an error. We suggest that students might benefit from visualisation tools which help students making appropriate mental models.