本研究針對1971-2025年間有關科技輔助技職教育的文獻進行系統性回顧與視覺化分析，以探討研究發展趨勢、熱門應用領域與研究議題。結果顯示，科技輔助技職教育的相關研究自2019年後快速增長，並於2025年達到高峰，主要發表國家集中於臺灣、中國大陸與美國。研究中最常應用的科技工具為虛擬實境（Virtual Reality, VR）、電子學習（E-learning）與數位學習（digital learning），新興技術，例如生成式人工智慧（Generative AI）、機器人及智慧導師系統，亦逐漸受到關注。在學科領域方面，工程與技術科目占比最高，自然科學與醫護教育次之，而語言、數學與商業領域的研究相對不足。研究對象以技職及工程學生為主、醫護學生居次，病人與家屬相關的應用研究有限。議題探討則多聚焦於情意面（動機、態度、滿意度）與認知面（知識獲取、批判思考），技能與行為改變的研究較少。研究方法以實驗設計為主流，輔以問卷調查與少量質性研究。本研究結果指出，科技輔助學習在技職教育中已展現高度多元性與創新性，未來應進一步拓展跨學科應用，強化技能與行為面的實證研究，並深化生成式AI等新興科技於教育場域中的應用效益評估。研究方法則可採用更多混合設計，以更全面理解學習歷程與教育成效，以作為科技輔助技職教育、人才培育的應用參考。

This study conducted a systematic review and visualization analysis of literature on technology-assisted vocational education and training (TVET) published between 1971 and 2025, with the aim of exploring research development trends, major application domains, and emerging topics. The results indicate that research on technology-assisted vocational education has grown rapidly since 2019, reaching its peak in 2025, with the majority of publications originating from Taiwan, mainland China, and the United States. The most frequently applied technologies include virtual reality (VR), E-learning, and digital learning, while emerging tools such as generative artificial intelligence (Generative AI), robotics, and intelligent tutoring systems have also begun to attract increasing attention. In terms of disciplinary focus, engineering and technology-related subjects dominate, followed by natural sciences and healthcare education, whereas research in language, mathematics, and business remains relatively limited. The primary study populations are vocational and engineering students, followed by healthcare students, with relatively few studies addressing applications for patients or family members. Thematic discussions predominantly center on affective factors (e.g., motivation, attitudes, satisfaction) and cognitive outcomes (e.g., knowledge acquisition, critical thinking), while investigations into skills and behavioral change are comparatively scarce. Experimental research designs are the prevailing methodology, supplemented by survey studies and a smaller number of qualitative inquiries. The findings suggest that technology-assisted learning in vocational education demonstrates considerable diversity and innovation. Future research should further expand cross-disciplinary applications, strengthen empirical investigations into skills and behavioral outcomes, and deepen the evaluation of emerging technologies such as generative AI in educational contexts. Methodologically, the adoption of more mixed-design approaches is recommended to achieve a more comprehensive understanding of learning processes and educational effectiveness, thereby offering valuable insights for the application of technology-assisted vocational education in talent cultivation.