{"title":"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","authors":"Yijia Hao;Yushi Liu;Bo Liu;George Amarantidis;Rami Ghannam","doi":"10.1109/TE.2025.3536105","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"173-185"},"PeriodicalIF":2.1000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating AI in Engineering Education: A Comprehensive Review and Student-Informed Module Design for U.K. Students\",\"authors\":\"Yijia Hao;Yushi Liu;Bo Liu;George Amarantidis;Rami Ghannam\",\"doi\":\"10.1109/TE.2025.3536105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":55011,\"journal\":{\"name\":\"IEEE Transactions on Education\",\"volume\":\"68 2\",\"pages\":\"173-185\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Education\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10924443/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION, SCIENTIFIC DISCIPLINES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Education","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10924443/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
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.
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.