| Title |
Chatbots and generative AI in engineering education: a structured literature review (2020–2024) |
| Authors |
Staneviciene, Evelina ; Gudoniene, Daina ; Cerneckiene, Jurgita ; Valancius, Rokas ; Jankauskas, Kestutis ; Bulseco, Dylan ; Xie, Charles ; Kashyrskyy, Andriy ; Deng, Hongling |
| DOI |
10.1002/cae.70232 |
| Full Text |
|
| Is Part of |
Computer applications in engineering education.. Hoboken, NJ : Wiley. 2026, vol. 34, iss. 4, art. no. e70232, p. 1-21.. ISSN 1061-3773. eISSN 1099-0542 |
| Keywords [eng] |
chatbots ; engineering education ; generative AI ; generative technologies ; higher education |
| Abstract [eng] |
The integration of chatbots and generative artificial intelligence (AI) tools into engineering education is rapidly changing the way students learn, interact, and solve complex problems. These technologies offer new opportunities for personalized learning, real‐time feedback, and enhanced student engagement. However, a comprehensive understanding of their implementation, pedagogical value, and limitations in engineering education remains limited. This structured literature review examines how chatbots and generative AI tools are integrated into engineering education and evaluates their educational impact and associated challenges. The review focuses on their effects on student learning outcomes, engagement, and skills development, as well as the challenges associated with their implementation. Following PRISMA guidelines, literature was identified through Scopus, Google Scholar, Taylor & Francis Online, and ScienceDirect. After applying predefined inclusion criteria, 16 studies were included in the final review and were analyzed thematically. The findings show that chatbots and generative AI tools can improve learning outcomes in engineering education by promoting student engagement, supporting conceptual understanding, encouraging self‐directed learning, and contributing to the development of problem‐solving and creative thinking skills. However, the review also identifies challenges related to ethical concerns, accuracy limitations, and the risk of student overreliance, which can impact the development of deeper learning and collaboration. |
| Published |
Hoboken, NJ : Wiley |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
