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Faculty Needs Analysis of AI-Based Team Collaboration Support Functions in University Project Courses: A Comparison between Humanities and Engineering

  • The Journal of Transdisciplinary Studies
  • Abbr : JTS
  • 2026, 10(2), pp.117~130
  • DOI : 10.22685/jts.2026.10.2.117
  • Publisher : The Society for Transdisciplinary Studies
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Received : July 28, 2026
  • Accepted : August 22, 2026
  • Published : August 31, 2026

Sanghyun Song ORD ID 1,  Yunja Hwang ORD ID 1

1단국대학교

Accredited

ABSTRACT

Objectives: This study aimed to investigate faculty perceptions of the importance of and need for AI-based team collaboration support functions in project-based courses and Capstone Design, compare these perceptions between the humanities and social sciences and science and engineering disciplines, and propose AI-based team collaboration strategies that reflect both common and discipline-specific needs. Methods: A survey was conducted with 65 faculty members from 16 universities who had experience teaching project-based courses or Capstone Design. The questionnaire measured perceptions of AI-based team collaboration support functions and the need for AI-based collaboration design strategies. Data were analyzed using descriptive statistics, Welch's t-tests, and Importance–Need Analysis to examine disciplinary differences. Results: The findings indicate that faculty members perceive generative AI not merely as a content generation tool but as an educational partner that supports collaboration throughout the project process. The Importance–Need Analysis identified information search and report writing, problem definition and idea generation, and performance management and learning analytics as priority development areas, whereas team discussion and conflict management was classified as a potential improvement area. Science and engineering faculty assigned significantly higher importance to AI-assisted code generation and debugging than did humanities and social sciences faculty. Faculty members from both disciplinary groups also expressed strong needs for AI-supported feedback, learning analytics and diagnostic support, and project coaching. Conclusions: This study identifies priorities for AI-based team collaboration support and provides preliminary design directions for integrating generative AI into collaborative instructional design in higher education. The findings suggest that common core AI support functions should be prioritized, with selected functions strengthened according to disciplinary characteristics.

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