A Comprehensive Review of Applications of AI Technologies in Higher Engineering Education
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| Title: | A Comprehensive Review of Applications of AI Technologies in Higher Engineering Education |
|---|---|
| Language: | English |
| Authors: | Chao Liu |
| Source: | Discover Education. 2025 4. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 25 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Information Analyses |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Higher Education, Engineering Education, Individualized Instruction, Academic Achievement, Predictor Variables, Intelligent Tutoring Systems, Grades (Scholastic), Accuracy, Algorithms, Bias, Integrity, Automation, Constructivism (Learning), Ethics, Technology Integration, Curriculum Design, Faculty Development |
| DOI: | 10.1007/s44217-025-00954-0 |
| ISSN: | 2731-5525 |
| Abstract: | This paper presents a comprehensive narrative review of artificial intelligence (AI) applications in higher engineering education. We examine how AI technologies are reshaping teaching, learning, and assessment in engineering disciplines. Key implementation areas include personalized learning, student performance prediction, intelligent tutoring systems, and laboratory enhancements. Quantitative evidence reveals significant impacts: AI-driven platforms have improved student grades by up to 25%, and predictive models have achieved accuracy rates exceeding 99%. However, significant challenges persist, including algorithmic bias, accessibility barriers, and academic integrity concerns, with generative AI correctly answering up to 85% of engineering assessment questions. The review also explores tensions between AI-driven automation and constructivist learning approaches. We conclude that while AI offers immense potential, its successful integration requires thoughtful curriculum design, faculty development, and robust institutional support to prepare students for an AI-driven professional landscape. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1498028 |
| Database: | ERIC |
| Abstract: | This paper presents a comprehensive narrative review of artificial intelligence (AI) applications in higher engineering education. We examine how AI technologies are reshaping teaching, learning, and assessment in engineering disciplines. Key implementation areas include personalized learning, student performance prediction, intelligent tutoring systems, and laboratory enhancements. Quantitative evidence reveals significant impacts: AI-driven platforms have improved student grades by up to 25%, and predictive models have achieved accuracy rates exceeding 99%. However, significant challenges persist, including algorithmic bias, accessibility barriers, and academic integrity concerns, with generative AI correctly answering up to 85% of engineering assessment questions. The review also explores tensions between AI-driven automation and constructivist learning approaches. We conclude that while AI offers immense potential, its successful integration requires thoughtful curriculum design, faculty development, and robust institutional support to prepare students for an AI-driven professional landscape. |
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| ISSN: | 2731-5525 |
| DOI: | 10.1007/s44217-025-00954-0 |
