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Cited by 12Artificial Intelligence in EducationEducational Games and GamificationStudent Assessment and Feedback

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Enhancing Assessment and Feedback in Game Design Programs: Leveraging Generative AI for Efficient and Meaningful Evaluation

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Hutson, James, Fulcher, Ben, Ratican, Jeremiah·2024·IJERI: International Journal of Educational Research and Innovation
DOI 10.46661/ijeri.11038
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Abstract

The integration of generative AI tools in game design education offers promising ways to streamline the grading, assessment, and feedback processes that are typically labor-intensive. In game design programs, faculty often deal with varied file formats, including 3D models, executable prototypes, videos, and complex game design documents. Traditional methods of assessment and feedback, primarily text-based, struggle to provide timely and actionable insights for students. Furthermore, only a small percentage of top students consistently review and apply feedback, leading to inefficiencies. This article explores how generative AI tools can augment these processes by automating aspects of grading, generating more personalized and meaningful feedback, and addressing the time-intensive nature of reviewing diverse file formats. Key strategies are discussed, including the use of rubrics tailored for AI-based assessment, automated prompts for narrative-driven assignments, and the application of AI in reviewing complex project builds. The objective is to create more time for faculty to engage in live mentoring and hands-on learning activities, which research shows to be more effective. Practical examples of various game design assignments, including build reviews and document evaluations, are provided to illustrate these new approaches. This shift promises to enhance student engagement and improve learning outcomes.

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  • Many developers worry about the legal and ethical implications of AI models that are trained on existing artwork without explicit permission from original creators.

    “Many developers worry about the legal and ethical implications of AI models that are trained on existing artwork without explicit permission from original creators (Boucher et al. 2023; Hutson et al. 2024; Lee et al. 2023).”

    From Generative AI in Game Design: Enhancing Creativity or Constraining Innovation? (2025) · cites this paper as Hutson et al. 2024

    The Literature Review discusses ethical concerns about bias and fairness in AI-assisted assessment, and the Discussion addresses AI’s difficulty evaluating creative work (pages not provided). Neither addresses developers’ concerns about models trained on existing artwork without creators’ permission; searches for artwork, consent, copyright, and training-data concerns found no supporting passage. (LLM, conf=0.73)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 43

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Metadata

Authors
Hutson, J., Fulcher, B., & Ratican, J.
Affiliations
Lindenwood University (US)
ORCID
Venue
IJERI: International Journal of Educational Research and Innovation
Publisher
Universidad Pablo de Olavide
Published
2024-12-03
Issue
22
Pages
1-20
ISSN
2386-4303 (ISSN-L)
DOI
10.46661/ijeri.11038
OpenAlex
W4405012623
Cited by
12
References
5
Citations by year
2026: 7 · 2025: 5
FWCI
3.17
Type
journal article
Language
en
Open access
Licence
Locations
  • IJERI International Journal of Educational Research and Innovation (publishedVersion, cc-by-nc-nd)
  • RIO (Universidad Pablo de Olavide) (submittedVersion, cc-by-nc-nd)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
generative AI, game design education, automated grading, student feedback, assessment, rubrics, game design document, faculty mentoring, student engagement

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