BOARD # 99: Work in Progress: AI in online laboratory teaching - A Systematic Literature Review

Johannes Kubasch; Dominik May; Doha Meslem

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: Computers in Education Division (COED) Poster Session (Track 1.A)
Tagged Division: Computers in Education Division (COED)
Page Count: 10
Permanent URL: https://peer.asee.org/55916

Paper Authors

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Johannes Kubasch University of Wuppertal

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Johannes Kubasch is a mechanical engineer and research associate at the Chair of Technical and Engineering Education at the University of Wuppertal. As a engineer in automotive engineering, he initially worked in the automotive supply industry in the development of airbag systems before moving to the University of Wuppertal to work in the field of engineering education. In the past, he worked on the AdeLeBk.nrw project to digitize the university training of prospective teachers at vocational schools and to adapt the learning content to the requirements of later professional practice.

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Dominik May University of Wuppertal orcid.org/0000-0001-9860-1864

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Dr. May serves as a Professor for Technical Education and Engineering Education Research at the School of Mechanical Engineering and Safety Engineering at University of Wuppertal. His work revolves around generating both fundamental and practical knowledge that defines, informs, and enhances the education of engineers.

His primary research thrust centers around the development, implementation, practical utilization, and pedagogical value of online laboratories. These laboratories span a range of formats, including remote, virtual, and cross-reality platforms. Dr. May's scholarly pursuits extend into the sphere of online experimentation, particularly within the context of engineering and technical education. Prior to his role at the University of Wuppertal, Dr. May held the position of Assistant Professor within the Engineering Education Transformations Institute at the University of Georgia (Athens, GA, USA).

Central to Dr. May's scholarly endeavors is his commitment to formulating comprehensive educational strategies for Technical and Engineering Education. His work contributes to the establishment of an evidence-based foundation that guides the continual transformation of Technical and Engineering Education. Additionally, Dr. May is actively involved in shaping instructional concepts tailored to immerse students in international study contexts. This approach fosters intercultural collaboration, empowering students to cultivate essential competencies that transcend cultural boundaries.

Beyond his academic role, Dr. May assumes the position of President at the "International Association of Online Engineering (IAOE)," a nonprofit organization with a global mandate to advocate for the broader advancement, distribution, and practical application of Online Engineering (OE) technologies. His leadership underscores his commitment to leveraging technological innovation for societal progress. Furthermore, he serves as the Editor-in-Chief for the "International Journal of Emerging Technologies in Learning (iJET)," a role that facilitates interdisciplinary discussions among engineers, educators, and engineering education researchers. These discussions revolve around the interplay of technology, instruction, and research, fostering a holistic understanding of their synergies.

Dr. May is an active member of the national and international scientific community in Engineering Education Research. He has also organized several international conferences himself – such as the annual "International Conference on Smart Technologies & Education (STE)" – and serves as a board member for further conferences in this domain and for several Divisions within the American Society for Engineering Education.

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Doha Meslem Bergische Universität Wuppertal

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Abstract

AI technologies are increasingly permeating everyday life and are set to significantly transform university teaching in the coming years. A growing range of applications and concrete use cases for these technologies are emerging in the context of higher education. In particular, recent advances in AI, especially natural language processing (NLP) tools like ChatGPT, have created opportunities to meaningfully integrate these technologies and enhance the educational experience in laboratory-based instruction. This submission presents work conducted within the KICK 4.0 research project, which focuses on connecting NLP tools with online laboratories in engineering education. The project addresses new competency requirements for students that are becoming increasingly relevant with the growing use of NLP technologies. The purpose of this submission is to provide a structured overview of the use of AI systems in laboratory-based teaching and learning in engineering education. Specifically, it aims to summarize how such systems have been applied in laboratory-based instruction so far and the insights gained from those experiences. The focus is on the opportunities and limitations of NLP systems, their potential to enhance student learning outcomes, and their ability to generate user-oriented feedback. Three research questions guide our systematic literature review: 1) How is AI currently used in laboratory-based engineering education? 2) How can AI systems provide accurate and high-quality feedback to students in this context? and 3) How can students be trained to competently use NLP systems while understanding both their limitations and opportunities? For this review, we examined national and international databases using relevant keywords such as “engineering,” “education,” “laboratory,” “AI,” and “NLP,” in various combinations, along with adjacent search terms like “feedback,” “opportunities,” and “limitations.” The results were screened for relevance and will be further examined for this paper. Preliminary findings show that many educators are exploring the use of NLP technologies, but satisfaction levels largely depend on the quality of feedback and the perception of the results' accuracy. High-quality feedback is essential, and the user plays a significant role in shaping the outcomes by posing the right questions or priming the system effectively. However, many educators are still unclear on how to successfully integrate these tools into their teaching. Additionally, there are critical voices highlighting the challenges that may arise from further integration of NLP technologies in higher education, particularly regarding the assessment and evaluation of student work. As a result of this work, we expect to present an overview of the current research landscape in engineering education regarding the use of AI, specifically NLP technologies, in laboratory-based instruction. Our overall aim is to identify which applications have been introduced, which have been technically implemented and tested for effectiveness, and how students and educators view these technologies.

Citation
Format

Kubasch, J., & May, D., & Meslem, D. (2025, June), BOARD # 99: Work in Progress: AI in online laboratory teaching - A Systematic Literature Review Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55916

TY  - CPAPER
AB  - AI technologies are increasingly permeating everyday life and are set to significantly transform university teaching in the coming years. A growing range of applications and concrete use cases for these technologies are emerging in the context of higher education. In particular, recent advances in AI, especially natural language processing (NLP) tools like ChatGPT, have created opportunities to meaningfully integrate these technologies and enhance the educational experience in laboratory-based instruction.
This submission presents work conducted within the KICK 4.0 research project, which focuses on connecting NLP tools with online laboratories in engineering education. The project addresses new competency requirements for students that are becoming increasingly relevant with the growing use of NLP technologies. The purpose of this submission is to provide a structured overview of the use of AI systems in laboratory-based teaching and learning in engineering education. Specifically, it aims to summarize how such systems have been applied in laboratory-based instruction so far and the insights gained from those experiences. The focus is on the opportunities and limitations of NLP systems, their potential to enhance student learning outcomes, and their ability to generate user-oriented feedback.
Three research questions guide our systematic literature review: 1) How is AI currently used in laboratory-based engineering education? 2) How can AI systems provide accurate and high-quality feedback to students in this context? and 3) How can students be trained to competently use NLP systems while understanding both their limitations and opportunities?
For this review, we examined national and international databases using relevant keywords such as “engineering,” “education,” “laboratory,” “AI,” and “NLP,” in various combinations, along with adjacent search terms like “feedback,” “opportunities,” and “limitations.” The results were screened for relevance and will be further examined for this paper. Preliminary findings show that many educators are exploring the use of NLP technologies, but satisfaction levels largely depend on the quality of feedback and the perception of the results&#39; accuracy. High-quality feedback is essential, and the user plays a significant role in shaping the outcomes by posing the right questions or priming the system effectively. However, many educators are still unclear on how to successfully integrate these tools into their teaching. Additionally, there are critical voices highlighting the challenges that may arise from further integration of NLP technologies in higher education, particularly regarding the assessment and evaluation of student work.
As a result of this work, we expect to present an overview of the current research landscape in engineering education regarding the use of AI, specifically NLP technologies, in laboratory-based instruction. Our overall aim is to identify which applications have been introduced, which have been technically implemented and tested for effectiveness, and how students and educators view these technologies.
AU  - Johannes Kubasch
AU  - Dominik May
AU  - Doha Meslem
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD # 99: Work in Progress: AI in online laboratory teaching - A Systematic Literature Review
UR  - https://peer.asee.org/55916
ER  -