Type:

Year:

Editor:

Author:

Tsao, Yung Chiau; Loh, Leon

Series:

Institution:

Page(s):

DOI number:

ISBN:

ISSN:

Abstract:

STEM-based learning is widely recognized globally as an effective approach for fostering interdisciplinary skills and preparing students for complex, real-world challenges. Taiwan’s technology education system has increasingly incorporated STEM principles, especially within secondary technology courses, where it is viewed as a strategy that fosters meaningful learning outcomes. This approach actively engages students in engineering and design projects, encouraging the integration and application of interdisciplinary knowledge in practical contexts. STEM instruction not only strengthens students' theoretical foundations but also enhances their hands-on skills and creative thinking. By applying learned concepts to solve practical problems, students validate their knowledge, building skills and resilience that better prepare them for careers in science, technology, and engineering fields. However, STEM teaching in Taiwan relies heavily on technology teachers, who are responsible for preparing content that spans multiple disciplines, such as science, engineering, and mathematics. This cross-disciplinary workload places a significant burden on lesson planning and preparation, making it challenging for teachers to maintain high instructional quality and effectiveness over time. Such pressures may lead some educators to discontinue STEM teaching altogether or struggle to sustain its benefits. In recent years, advancements in generative artificial intelligence (GenAI) have encouraged teachers to explore this technology’s potential in educational settings. Many teachers have begun incorporating GenAI tools, especially for lesson planning and curriculum design, aiming to ease preparation demands and reduce the time required. Despite these advancements, most educators currently limit their use of GenAI to basic tasks such as course planning and ideation, rather than fully utilizing its capabilities for more advanced instructional needs, including assessing student performance. This study employs a quantitative research approach, using a survey to investigate how technology teachers across Taiwan utilize GenAI in STEM teaching and to identify the primary sources from which they learn about this technology. Survey responses were gathered from 67 active technology teachers from various regions across Taiwan, all of whom teach students aged 13 to 15. This study addresses two main questions: How do technology teachers integrate GenAI within STEM education, and what are the primary sources through which they gain knowledge of GenAI? Key findings reveal that (1) 44% of Taiwan’s technology teachers use GenAI primarily for lesson planning and creative content generation, while only 8% apply it to assess student performance; (2) Teachers primarily acquire knowledge of GenAI through Professional Development courses and workshops organized by universities, Educational Technology Conferences and Exhibitions, and Online Learning Platforms; and (3) Online Learning Platforms play an especially significant role in supporting teachers’ use of GenAI for evaluating student performance. Based on these findings, this study suggests that strengthening teachers' engagement with GenAI through online platforms could improve their ability to apply it effectively in student performance evaluation. These insights underscore the importance of providing targeted AI-driven tools to support a broad range of instructional tasks in STEM teaching, offering valuable contributions to advancing design education and engineering pedagogy.

Keywords: