Conference paper
Systematic Literature Review of GenAI Integration in Higher Education and Analysis of Opportunities for Engineering Education
Proceedings of AAEE 2024, pp.1-10
Australasian Association for Engineering Education (AAEE) Annual Conference, 35th (Canterbury, New Zealand, 08-Dec-2024–12-Dec-2024)
Australasian Association for Engineering Education
2024
Abstract
CONTEXT
New Generative Artificial Intelligence (GenAI) tools have gained significant attention in higher education, with studies across disciplines evaluating their performance against university assessments. Findings suggest that GenAI can generate acceptable responses with minimal input modification. This suggests the necessity to recalibrate current educational practices, particularly with the ongoing evolution and improvement of new GenAI tools. Integrating these tools into the classroom could enhance productivity, and their increasing use in industry professional practice.
PURPOSE OR GOAL
This study aims to systematically review case studies and practical examples of GenAI integration in university teaching and learning. The goal is to identify the factors that facilitate or hinder the effective use of GenAI in educational contexts and to provide insights for its application in engineering education.
APPROACH OR METHODOLOGY/METHODS
A systematic scoping review was conducted. A Scopus database search conducted in February 2024 identifies 487 publications, the titles and abstracts of which were screened by eight academics from seven Australian universities. Of these, 21 appeared to meet the inclusion criteria – thus, the data from these publications were extracted and analysed.
ACTUAL OR ANTICIPATED OUTCOMES
Results demonstrate that the number of publications in relation to GenAI in higher education has increased dramatically, confirming the importance of the topic. However, relatively few publications present research designs that demonstrate case studies and evaluation of the integration of GenAI in the classroom. The identified case studies can be applied in engineering education to enhance problem-solving, interactive learning, project-based learning, written communication, coding skills, and professional competencies, as well as to promote creative and critical thinking.
CONCLUSIONS/RECOMMENDATIONS/SUMMARY
The identified case studies provide practical, evidence-based insights for engineering academics to integrate Generative AI into their teaching practice.
Details
- Title
- Systematic Literature Review of GenAI Integration in Higher Education and Analysis of Opportunities for Engineering Education
- Authors
- Marina Belkina (Corresponding Author) - Western Sydney UniversityPeter Neal - UNSW SydneySarah Grundy - UNSW SydneyGhulam Mubashar Hassan - The University of Western AustraliaRezwanul Haque - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringScott Arthur Daniel - University of Technology SydneySasha Nikolic - University of WollongongSarah Lyden - University of Tasmania
- Publication details
- Proceedings of AAEE 2024, pp.1-10
- Conference details
- Australasian Association for Engineering Education (AAEE) Annual Conference, 35th (Canterbury, New Zealand, 08-Dec-2024–12-Dec-2024)
- Publisher
- Australasian Association for Engineering Education
- Date published
- 2024
- Copyright note
- © 2024 Names of authors: The authors assign to the Australasian Association for Engineering Education (AAEE) and educational non-profit institutions a non-exclusive licence to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. The authors also grant a non-exclusive licence to AAEE to publish this document in full on the World Wide Web (prime sites and mirrors), on Memory Sticks, and in printed form within the AAEE 2024 proceedings. Any other usage is prohibited without the express permission of the authors. - Marina Belkinaa, Peter Nealb, Sarah Grundyb, Ghulam Hassanc, Rezwanul Haqued, Scott Daniele, Sasha Nikolicf and Sarah Lyden
- Grant note
- This work was supported by the 2023 Australian Council of Engineering Deans (ACED) and the Australasian Association for Engineering Education (AAEE) Grant.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991083398802621
- Output Type
- Conference paper
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