将人工智能融入工程教育:面向英国学生的全面回顾和学生知情模块设计

IF 2.1 2区 工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Yijia Hao;Yushi Liu;Bo Liu;George Amarantidis;Rami Ghannam
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引用次数: 0

摘要

贡献:人工智能(AI)在工程高等教育中的整合变得越来越重要。本文对工程高等教育中人工智能集成的当前研究进行了全面回顾,并介绍了一个旨在教授工程学生人工智能基础知识的试点人工智能入门模块,从而为集成奖学金做出了贡献。背景:随着人工智能的快速发展,将人工智能整合到工程课程中以帮助学生为劳动力做好准备至关重要。然而,对于人工智能与工程高等教育的整合策略,目前还缺乏全面的研究。研究问题(RQs):本文解决了以下RQs:工程高等教育中人工智能集成的现状是什么?将人工智能教育融入本科工程课程的关键考虑因素是什么?在为电子专业的本科生提供人工智能模块的过程中,有哪些挑战和经验教训?方法:进行了一项全面的审查,以确定将人工智能整合到工程课程中的教学方法的当前研究。在这一全面审查的基础上,还开发和实施了一个试点人工智能介绍模块。为了为英国学生定制模块设计,我们从英国29所大学的课程审查中收集了数据,以了解用于提供这些课程的平台。最后,使用调查来评估该模块的影响,并确定任何挑战和吸取的教训。研究结果:我们的综合综述显示,在工程高等教育中,人工智能集成的综合研究缺乏。项目审查结果显示,英国有29所大学在同一课程中提供人工智能和工程相关知识,其中伦敦大学引领了这一趋势。在审查之后,开发了人工智能模块,并将其交付给150名英国电子和电气工程一年级学生。该模块通过分别由114名和104名学生完成的入学和退学调查来评估。结果表明,试点人工智能模块有助于向工程本科学生教授人工智能基础知识,97%的学生同意该模块可以提高他们未来的工作能力。回顾和开发的模块可以为将人工智能引入现有的本科工程课程提供有价值的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating AI in Engineering Education: A Comprehensive Review and Student-Informed Module Design for U.K. Students
Contribution: The integration of artificial intelligence (AI) in engineering higher education is becoming increasingly important nowadays. This article contributes to the Scholarship of Integration by providing a comprehensive review of current research on AI integration in engineering higher education and presenting a pilot AI introductory module designed to teach engineering students AI fundamentals. Background: With the rapid development of AI, it is crucial to integrate AI into engineering curricula to prepare students for the workforce. However, there is a lack of comprehensive research on the strategies to integrate AI into engineering higher education. Research Questions (RQs): This article addresses the following RQs: What is the current state of AI integration in engineering higher education? What are the key considerations for integrating AI education into undergraduate engineering programs? What are the challenges and lessons learned when delivering an AI module to undergraduate students majoring in electronics? Methodology: A comprehensive review was conducted to identify current research on pedagogical methods for integrating AI in engineering curricula. A pilot AI introductory module was also developed and implemented based on this comprehensive review. To customize module design for U.K. students, data was collected from a program review of 29 universities in the U.K. to understand the platforms used to deliver these programs. Finally, surveys were used to evaluate the impact of this module and to identify any challenges and lessons learned. Findings: Our comprehensive review revealed a lack of comprehensive research on AI integration in engineering higher education. The program review results showed that 29 universities in the U.K. offer AI and engineering-related knowledge in the same curriculum, among which London leads the trend. Following the review, an AI module was developed and delivered to 150 U.K. first-year electronics and electrical engineering students. The module was evaluated via entry and exit surveys that were completed by 114 and 104 students, respectively. The results suggested that the pilot AI module aids in teaching AI fundamentals to undergraduate engineering students, with 97% of students agreeing that the module can increase their future job competencies. The review and developed module can serve as valuable references for introducing AI into existing engineering programs at the undergraduate level.
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来源期刊
IEEE Transactions on Education
IEEE Transactions on Education 工程技术-工程:电子与电气
CiteScore
5.80
自引率
7.70%
发文量
90
审稿时长
1 months
期刊介绍: The IEEE Transactions on Education (ToE) publishes significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE. Contributions must address discovery, integration, and/or application of knowledge in education in these fields. Articles must support contributions and assertions with compelling evidence and provide explicit, transparent descriptions of the processes through which the evidence is collected, analyzed, and interpreted. While characteristics of compelling evidence cannot be described to address every conceivable situation, generally assessment of the work being reported must go beyond student self-report and attitudinal data.
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