Five Points STEM Educators Should Consider When Integrating GenAI Within Their Methods Courses

By: Ruthmae Sears, Ph.D., Professor, University of South Florida
Yvonne Franco, Ph.D., Assistant Professor, University of Tampa
Stephanie Arthur, Ph.D., Assistant Professor, University of South Florida
Sandra Vernon-Jackson, Ph.D., Associate Professor, University of South Florida

STEM teacher educators are responsible for preparing prospective and in-service teachers with the knowledge and skills to optimize positive learning outcomes within their own classrooms. Thus, faculty often have to ensure the content of the professional learning activities they facilitate is timely and responsive to their community needs and is sensitive to constraints that may impact individuals’ engagement levels (such as usability of the information, access to resources, teachers’ beliefs, and knowledge). Therefore, given the technological advancement of generative artificial intelligence (GenAI) in the current digital era, STEM teacher educators are now challenged to prepare prospective teachers to use GenAI meaningfully. Luo (2024) noted, “By GenAI, we refer to an artificial intelligence technique capable of generating a variety of new content including but not limited to texts, videos and images (Cao et al., 2023).” Based on findings from our collaborative self-study, we (the authors) describe five points that STEM teacher educators need to consider if they seek to integrate GenAI into their STEM education methods courses: (1) reflect on belief systems and mindsets that can impact your readiness to use GenAI, (2) engage in professional learning activities to strengthen your knowledge of GenAI, (3) identify policies in the syllabus as to how and when GenAI is to be used to address ethical considerations, (4) provide explicit examples within the methods courses of how it can be used within STEM learning environments, and (5) monitor students’ feedback of how they are using GenAI to ensure it does not stagnate creativity or the development of critical thinking skills.

Context for Our Work

As mathematics and science teacher educators with a wide range of expertise and experiences in using GenAI, we engaged in a collaborative self-study in a safe, supportive, and accountable community to reflect on how we use GenAI practice within our teacher preparation program. Notably, two of the researchers (Dr. Franco and Dr. Arthur) engaged in co-planning to integrate GenAI within science and STEM methods courses. At the same time, the other two authors (Dr. Sears and Dr. Vernon-Jackson) sought professional development training about GenAI and reflected on how it can be integrated within their mathematics education methods courses. They were more cautious to utilize it due to ethical concerns and efforts to maintain cognitive rigor. Considering that a collaborative self-study is self-initiated, promotes improvement, and uses qualitative methods (LaBoskey, 2004), we documented our practices and identified factors that emerged organically, which influenced the extent to which we utilized GenAI. The five points we identify emerged based on factors that helped or impeded if and how GenAI was ultimately used within our settings.   

Point 1. Reflect on belief systems and mindsets.

STEM teacher educators’ beliefs and mindsets can impact their readiness and willingness to use GenAI within practice. According to Voss et al (2013), beliefs are “…psychologically held understandings and assumptions about phenomena or objects of the world that are felt to be true, have both implicit and explicit aspects, and influence people’s interactions with the world” (p.249-250), which can vary based on content and context. It was also noted that teachers’ beliefs can be classified in five areas, namely “beliefs about teaching and learning, about instruction, about the subject, about learning to teach, and about the self” (Voss et al., 2013, p. 250). STEM teacher educators’ beliefs can impact their desire and willingness to use GenAI within their practice. Therefore, STEM teacher educators may need to assess and alter their belief systems to increase the likelihood of using GenAI within their practice.

We advocate for STEM educators to exhibit a growth mindset if they plan to integrate GenAI within their methods course. Dweck (2016) noted that “Individuals who believe their talents can be developed through hard work, good strategies, and input from others have a growth mindset” (Dweck, 2016, p.1). A growth mindset is critical for STEM educators as they explore how GenAI can be used within their disciplines and subsequently integrated within their methods courses.  Based on our personal experiences, we adopted a growth mindset to explore the possibilities of integrating GenAI into STEM methods courses, aiming to transform and enhance teaching and learning experiences. Particularly, drawing on feedback from preservice and in-service teachers, we further modified the professional development offered, the expectations for course assignments, and refined the GenAI policies utilized within our teacher preparation programs.

Point 2. Engage in professional learning activities.

STEM educators ought to engage in professional learning to support their understanding of GenAI and employ research-based recommendations of how it can be utilized within their practice. For instance, UNESCO (2024) provides an “AI Competency Framework” to support teachers using GenAI. The framework identifies five aspects (human–centered mindset, ethics of AI, AI foundations and applications, AI pedagogy, and AI for professional development) and three progression levels (acquire, deepen, and create), which can provide insights as to how they can plan to use GenAI within their methods courses. 

It will be valuable for STEM teacher educators to participate in webinars, workshops, and professional network conversations to gain personal insights into what could be done and possible barriers that need to be addressed. For instance, teacher educators can enroll in microcredentials related to GenAI (such as USF microcredentials focusing on Course enhancement with GenAI, and GenAI in action: Impact and Possibilities), as was the case with several authors of this blog. It will also be valuable to review relevant literature to ensure instructional decisions are grounded in evidence-based research findings. Knowledge is power and can inform planning and subsequent enactment of using GenAI in STEM methods courses.  

Point 3. Provide policies and guidelines on when it is appropriate to use GenAI.

It is dire that clear guidelines be provided within the syllabus regarding when and how GenAI will be used within the learning environment. For instance, various universities and districts have provided GenAI guidelines for educational settings (e.g., USF Guidance for Ethical Generative AI Usage, and Hillsborough County Public School Artificial Intelligence Implementation Guide). The clarification of when it is appropriate or not appropriate can help ensure that professional expectations and ethical considerations are addressed. The guidance offered can help to promote individual responsibilities and accountability and transparency for how and when it is used, and foster a human-centered approach to using GenAI. Given that AI detectors may not always be accurate and can result in false positive reporting or lack of certainty in classification (Elkhatat et al., 2023), establishing clear rules can reduce instances of plagiarism or using GenAI inappropriately within an academic context.  

Point 4. Model how GenAI can be used to support STEM teaching and learning.

STEM teacher educators ought to model how GenAI can positively enhance and transform STEM teaching and learning. For example, Dr. Franco modeled a 5E (engage, explore, explain, extend, and evaluate) lesson on space with a GenAI-generated letter written from the point of view of an alien to hook preservice teachers’ interest and guide their exploration into what is in the Earth’s atmosphere. The preservice teachers reviewed the lesson, provided extensive feedback, and modified the lesson content, process skills, and nature of science using a GenAI of their choice. STEM teacher educators should become familiar with various types of generative AI (such as ChatGPT, Khanmigo, Perplexity AI, Google Gemini, Claude, GitHub CoPilot, Dall-E, and Udio) or websites that consolidate a list of tools (such as teacherserver.com) and demonstrate how GenAI can support planning instruction and assess for understanding. Also, it will be valuable to illustrate how the amount of detail provided in prompt engineering can shift the depth and nature of the information created. Given that GenAI can hallucinate or contribute to misinformation, there is a need to emphasize the importance of reviewing the information for accuracy and bias (Ferrara, 2024). 

Point 5. Monitor students’ feedback and adjust instruction as appropriate.

STEM teacher educators should plan to gain students’ feedback on how they conceptualize GenAI within their practices and the potential challenges they experience. Insight is needed as to whether students are using GenAI as an instructional crutch or to scaffold an idea to support their learning (Wang et al., 2024). For instance, in Dr. Sear’s course, students were asked to write reflectively on how they use GenAI, and offer their perspectives on using it within mathematics teaching and learning. Gaining students insights about how they use GenAI can help ensure that it is not diminishing cognitive rigor or limiting critical thinking or the development of reasoning skills.

Conclusion

As STEM teacher educators plan to integrate GenAI within their methods courses, they must reflect on critical points that can impact the norms and culture regarding how it is used. Particularly, being aware of one’s belief system, acquiring appropriate knowledge and skills, establishing clear guidelines for GenAI, modeling desired practices, and monitoring students’ usage of GenAI can be valuable if the goal is to ensure it is integrated and supports cognitive rigor. Given GenAI’s increased visibility in STEM educational environments, researchers are encouraged to explore these points further and provide additional insights into how GenAI can be integrated into STEM teaching and learning.