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.
The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025