Work

Recommendations for the integration of generative artificial intelligence in support of engineering education research workflows

Year: 2025

Type: article

Abstract: Generative artificial Intelligence (AI) is a disruptive technology that is altering the way we live and work, and disruptions in academia are not the exception.Specifically in academia, generative AI is making its way into research, the classroom, and administrative workflows.For instance, recent work has documented how generative AI can support the creation of systematic literature reviews (Gwon et al., 2024;Hossain, 2024;Mozelius & Humble, 2024; among others) and even automate scientific workflows (e.g., Ghafarollahi & Buehler, 2025).As generative AI becomes increasingly integrated into research processes, it is essential to demonstrate its efficacy and accuracy-while also safeguarding human agency (Watkins & Barak-Medina, 2024)-in mimicking human activities (Miao & Holmes, 2023).In tandem, it is equally important to identify what are acceptable and perhaps controversial uses of generative AI in support of research workflows among discipline-specific research communities (Andersen et al., 2025;Watkins, 2023).For instance, a study identified that Danish researchers from varied disciplines hold both positive and negative views on the use of generative AI in support of research workflows.Although uses in data analysis may be viewed positively, uses for experimental design may be viewed as controversial (Andersen et al., 2025).Furthermore, for the case of qualitative analysis, which is common in engineering education research, as you will read in this special issue, researchers may be exposed to a variety of ethical dilemmas, such as data ownership and rights, data privacy and transparency, interpretive sufficiency, and potential biases, among others (Davison et al., 2024).As a response to this technological disruption, this special issue contributes new knowledge of how generative AI can be used and is being used in support of engineering education research workflows.In May of 2024, we invited the engineering education research community and other STEM education disciplines to contribute manuscripts that showcase ways in which generative AI can be used to support engineering education research workflows.In response, we received 43 extended abstracts with proposals.After an initial review by all three editors, 19 proposals were invited for manuscript submissions, and a total of 13 full manuscripts were received and sent out to peer review.The final collection of seven published manuscripts showcases how generative AI can be used for supporting data collection, data scoring, and data analysis processes, as shown in Figure 1.In each of the contributions, authors were asked to consider the validity and reliability of the AI-generated results, aspects of reproducibility and transparency, and overall benefits and limitations of the use of generative AI.Authors were also highly encouraged to explicitly address issues of ethics, bias, privacy, and security considerations, as applicable.Furthermore, each of the studies delivers not only important methodological contributions for the use of generative AI in research workflows but also theoretical contributions in engineering education research, as is the standard for the Journal of Engineering Education (JEE).In the following sections, we elaborate on the contributions from each study as indicated in Figure 1.

Source: Journal of Engineering Education

DOI: 10.1002/jee.70043

Authors: Alejandra J. Magana, Ryan Watkins, Camilo Vieira

Institutions: Purdue University West Lafayette, George Washington University, Universidad del Norte

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: National Science Foundation