Faculty and Staff Program on Generative AI and the Construction of an AI ‐Assisted Knowledge‐Sharing Website

This study examines a faculty and staff program on generative AI at the Hokkaido University of Science and proposes a reusable AI‐assisted workflow for developing a knowledge‐sharing website. The program addressed fundamental concepts, applications, and ethical considerations for participants from diverse backgrounds. A total of 120 individuals attended, and 83 valid responses were obtained. Quantitative findings demonstrated a good understanding and perceived usefulness, while qualitative feedback revealed increased motivation, broader perspectives, and heightened ethical awareness. Negative feedback highlighted concerns regarding security, time constraints, technical challenges, and mismatches with prior knowledge of the subject. To facilitate ongoing learning, a knowledge‐sharing website was created by transforming handwritten discussion notes into structured digital content using AI. Usage logs indicated relatively strong initial access; however, short engagement times suggest the need for enhanced usability and interactivity. Overall, within the scope of this single‐institution, short‐term case study, the program facilitated introductory AI learning, and the workflow presents a reusable model for institutional knowledge sharing that requires further validation in other contexts. Future work will focus on improving program design, website functionality, and longitudinal assessment of AI adoption. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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Publication Details

Journal
IEEJ Transactions on Electrical and Electronic Engineering
Published
2026-09-22
DOI
https://doi.org/10.1002/tee.70439
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
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article

Faculty and Staff Program on Generative AI and the Construction of an AI ‐Assisted Knowledge‐Sharing Website

Nobuyuki Sugio, Akihiro Suzuki, Naofumi Wada
IEEJ Transactions on Electrical and Electronic Engineering
AI in Service Interactions
article

Faculty and Staff Program on Generative AI and the Construction of an AI ‐Assisted Knowledge‐Sharing Website

Nobuyuki Sugio, Akihiro Suzuki, Naofumi Wada
article en

Abstract

This study examines a faculty and staff program on generative AI at the Hokkaido University of Science and proposes a reusable AI‐assisted workflow for developing a knowledge‐sharing website. The program addressed fundamental concepts, applications, and ethical considerations for participants from diverse backgrounds. A total of 120 individuals attended, and 83 valid responses were obtained. Quantitative findings demonstrated a good understanding and perceived usefulness, while qualitative feedback revealed increased motivation, broader perspectives, and heightened ethical awareness. Negative feedback highlighted concerns regarding security, time constraints, technical challenges, and mismatches with prior knowledge of the subject. To facilitate ongoing learning, a knowledge‐sharing website was created by transforming handwritten discussion notes into structured digital content using AI. Usage logs indicated relatively strong initial access; however, short engagement times suggest the need for enhanced usability and interactivity. Overall, within the scope of this single‐institution, short‐term case study, the program facilitated introductory AI learning, and the workflow presents a reusable model for institutional knowledge sharing that requires further validation in other contexts. Future work will focus on improving program design, website functionality, and longitudinal assessment of AI adoption. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

IEEJ Transactions on Electrical and Electronic Engineering
Hokkaido University of Science (JP), Hokkaido University (JP)
Openalex Percentile: Top 8%
AI in Service Interactions
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Faculty and Staff Program on Generative AI and the Construction of an AI ‐Assisted Knowledge‐Sharing Website — Nobuyuki Sugio, Akihiro Suzuki, et al. · IEEJ Transactions on Electrical and Electronic Engineering (2026) | TGRS Research Map | TGRS