Student Ambassadors for Sustainable AI EdTech Knowledge Sharing in Higher Education

Authors

  • Hyein Cho Research Institute for Data Science and AI, Hallym University, Chuncheon, Republic of Korea
  • Min Soo Lee Center for Educational Innovation, Hallym University, Chuncheon, Republic of Korea
  • Young Woong Ko Department of Computer Engineering and Smart Computing Lab., Hallym University, Chuncheon, Republic of Korea
  • Yong Geun Kim Department of Business Administration, Hallym University, Chuncheon, Republic of Korea
  • Jion Kim Department of Forensic Information Science and Technology, Hallym University, Chuncheon, Republic of Korea
  • Minho Kim AI EdTech Center, Hallym University, Chuncheon, Republic of Korea
  • Tae Il Yoon Department of Advertising and Public Relations, Hallym University, Chuncheon, Republic of Korea

DOI:

https://doi.org/10.34190/ecel.25.1.5455

Keywords:

Representational quality, AI EdTech, sustainable knowledge sharing, student ambassadors, knowledge mobilisation

Abstract

This conceptual paper develops a framework for appraising institutional knowledge assets produced through AI EdTech dissemination. Knowledge mobilisation research emphasises dissemination, interpretation, usefulness, uptake and context, but does not typically isolate representational quality as a distinct asset-level appraisal dimension. This gap is especially consequential when verification asymmetry and promotional incentive co-occur: claims may exceed inspectable evidence, overstate attribution or omit material conditions and risks even when assets remain visible, accessible and reusable. The paper therefore conceptualises representational quality through four criteria: claim traceability, attribution restraint, condition disclosure and risk representation. It further specifies a student ambassador model in which case-linked direct-user knowledge is combined with evidence access, expert review and revision rights across a governed production-and-maintenance lifecycle. Production-stage verification constrains distortion before dissemination, while post-hoc fidelity audit supports correction after publication; engagement analytics substitute for neither. Five falsifiable propositions address construct distinctness, verification, direct-user informational advantage, conditional diagnostic value and maintenance after material change. The paper reports no empirical findings. Its contribution is to position representational quality as an explicit property of AI EdTech knowledge assets and to specify the conditions and governance mechanisms under which it should be appraised.

Author Biographies

Hyein Cho, Research Institute for Data Science and AI, Hallym University, Chuncheon, Republic of Korea

Hyein Cho is a research professor at the Center for Educational Innovation, Hallym University. Her research interests include artificial intelligence in education, AI EdTech, technology-enhanced learning, generative AI in education, learning analytics, digital competence, and the design and evaluation of AI-supported learning environments in higher education.

Min Soo Lee, Center for Educational Innovation, Hallym University, Chuncheon, Republic of Korea

Min Soo Lee is a research professor at the Center for Educational Innovation, Hallym University. She earned her PhD in Media and Communication from Temple University. Her research examines media, archives, representation, and cultural memory, with recent work on student-led digital content production and knowledge mobilisation in AI-enabled higher education.

Young Woong Ko, Department of Computer Engineering and Smart Computing Lab., Hallym University, Chuncheon, Republic of Korea

Young-Woong Ko is a professor in the School of Software and Dean of Academic Affairs at Hallym University. He earned his PhD in computer engineering from Korea University. His research interests include operating systems, artificial intelligence, AI-enabled teaching and learning, educational technology platforms, and higher education innovation.

Yong Geun Kim, Department of Business Administration, Hallym University, Chuncheon, Republic of Korea

Yong Geun Kim is an associate professor in the Department of Business Administration at Hallym University and a Stanford Sustainability Changemaker. He earned his PhD in Business Administration from Sungkyunkwan University. His research focuses on human resource management, leadership, sustainability, and entrepreneurship, informed by corporate consulting experience.

Jion Kim, Department of Forensic Information Science and Technology, Hallym University, Chuncheon, Republic of Korea

Jion Kim is an associate professor in the Department of Forensic Information Science and Technology at Hallym University. He earned his PhD in sociology from Yonsei University. His research focuses on criminal network analysis, data forensics, AI-based data analysis, and evidence-informed approaches to complex social and organizational phenomena.

Minho Kim, AI EdTech Center, Hallym University, Chuncheon, Republic of Korea

Minho Kim works at the AI EdTech Center at Hallym University. He earned his PhD in Business Administration from Sejong University in 2015. His main research interests include industry–academia collaboration, business administration, and institutional approaches to educational innovation and AI EdTech.

Tae Il Yoon, Department of Advertising and Public Relations, Hallym University, Chuncheon, Republic of Korea

Tae-Il Yoon is a professor in the Department of Advertising and Public Relations at Hallym University. He earned his doctoral degree in advertising from the University of Missouri. His academic interests include advertising, public relations, cultural communication, media content, and strategic communication for public dissemination.

Downloads

Published

2026-10-07

Issue

Vol. 25 No. 1 (2026): Proceedings of the 25th European Conference on e-Learning (ECEL 2026)

Section

Academic papers