Embracing the future: the use of ChatGPT in Science Teacher Education

Project introduction and background information

Our context is the master (and minor) in science education, a.k.a. STEM teacher education. The student population consists of STEM people, who generally find it difficult to reflect on their learning as a teacher. Lately we found students had asked ChatGPT to write their reflection on teacher personal identity for them. Although this might be considered as fraud, it was also a smart thing to do and their question to ChatGPT also incorporated their reflection; it was the wordy writing part they left to the AI. This is just one example on how an AI can be both desirable and undesirable in STEM teacher education.

The versatile use of GenAI may have benefits in and for education (Trust, Whalen & Mouza 2023), but it comes with inherent drawbacks such as plagiarism since it is quite good at passing for instance engineering courses’ assessments (Nicolic et al., 2023). This has caused the TU/e general examination committee to mark the use of GenAI specifically in exams as fraud.

Research so far has focused on seeing what Gen AI is capable of in terms of teaching, explaining physics or mathematics (Gregorcic & Pendrill, 2023; Kock, Salinas-Hernández, & Pepin, 2023) or writing papers (e.g. Kortemeyer, 2023; ACS, 2023). Initiatives have been taken on how to use a Gen AI in writing academic papers including the proper way to reference to the use of it (ACS, 2023).

There are however also initiatives at TU/e and other universities to use GenAI in assignments and education rather than ban it outright (SURF, 2023). So far there has not been a study into the use of GenAI in university science teacher education, although it has been advised to look into possibilities and to come to terms both in policy and practice on how GenAI can be incorporated in teacher education and teaching (Trust, Whalen & Mouza, 2023). In this study we want to explore how GenAI such as ChatGPT could be used in university STEM teacher education within the 4TU teacher education institutes to come to balanced and well-considered suggestions for curriculum redesign in 4TU STEM teacher education courses and policy. Due to the nature of the master program, future high school science teachers practice will be innovated as a spin-off effect.

Objective and expected outcomes

Interviewing teachers and students in STEM teacher education will reveal there current understanding, use and possible use of Generative AI engines. We expect that some will have a lot of experience with AIs and others might not. All kinds of experiences and possible useful options for using AI in science teacher education will be uncovered to feed into curriculum and course redesign. 25 Teachers and 17 students have agreed to the interview and their interviews are transcribed for coding.

Results and learnings

Preliminary results show that teacher-educators have less experience in using GenAI than their student-teachers. Student-teachers see many potential learning goals for teacher education, whereas teacher-educators express a less informed view. A potential way of defining the ethical use in a policy for teaching education can be suggested from the data. 

A paper on this topic has been submitted. As soon as the paper is accepted the findings will be presented in more detail.