Jiunn‐Shiou Fang
Papers
1
Total Citations
4
H-Index
1
About
Dr. Jiunn-Shiou Fang is an emerging scholar in the field of technical and engineering education, with a focused research interest in the integration of artificial intelligence and deep learning into pedagogical frameworks. His most notable contribution is the development of a thematic deep-learning teaching module, which he established and rigorously evaluated in his 2025 study. This work addresses the critical need for higher-quality technical human resources by creating a model that uses double-triangular fuzzy numbers and grey relational analysis to assess and enhance learning outcomes. Although early in his citation impact, with his primary work already garnering 4 citations shortly after publication, Dr. Fang’s research is positioned at the vital intersection of AI technology and curriculum design. His achievement lies in providing a systematic, evaluative approach to embedding complex AI concepts into technical education, thereby bridging the gap between industrial AI maturity and academic training. This foundational work promises to influence how future technical curricula are structured to meet the demands of an AI-driven industry.
Research Focus
Key Achievements
Top Papers
- 1