Exploring the Role of AI-Generated Feedback Tangential to Learning Outcomes

Steven C. Sutherland, Tiago Machado, Shruti Mahajan, Omid Mohaddesi, Camillia Matuk, Gillian Smith, Casper Harteveld

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Students are often tasked in engaging with activities where they have to learn skills that are tangential to the learning outcomes of a course, such as learning a new software. The issue is that instructors may not have the time or the expertise to help students with such tangential learning. In this paper, we explore how AI-generated feedback can provide assistance. Specifically, we study this technology in the context of a constructionist curriculum where students learn about experimental research through the creation of a gamified experiment. The AI-generated feedback gives a formative assessment on the narrative design of student-designed gamified experiments, which is important to create an engaging experience. We find that students critically engaged with the feedback, but that responses varied among students. We discuss the implications for AI-generated feedback systems for tangential learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 2023 IEEE Conference on Games, CoG 2023
PublisherIEEE Computer Society
ISBN (Electronic)9798350322774
StatePublished - 2023
Event5th Annual IEEE Conference on Games, CoG 2023 - Boston, United States
Duration: Aug 21 2023Aug 24 2023

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289


Conference5th Annual IEEE Conference on Games, CoG 2023
Country/TerritoryUnited States


  • automated feedback
  • constructionist learning
  • creativity support
  • game design

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software


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