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1. Quest: Human Learning - A Framework for Incorporating Generative-AI Teaching and Learning Instruction in STEM Pre-Service Teacher Preparation (Evaluation). Paper presented at the 2025 ASEE Annual Conference & Exposition, Montreal, Canada.

   Liebe, C; Schill, S; Smith, CE; Ahammed_Ovi, J (June 2025, 2025 ASEE Annual Conference & Exposition)

   Generative Artificial Intelligence (GenAI) offers tools to transform K-12 science, engineering, technology, and mathematics (STEM) education. Pre-service teachers are in a prime position to learn about and engage with this emergent technology, preparing them to enter the workforce with knowledge on how GenAI can impact K-12 learning outcomes. This paper examines the implementation of GenAI teaching and learning instruction in a computer science (CS) pre-service teacher preparation program at a small engineering-focused R1 institution. Applying the Quest: Human Learning model, pre-service teachers learned prompt engineering and tested AI output. Quest stands for Question + Test. The pre-service teachers attended the Teach@Mines K-12 STEM teacher preparation program. Teach@Mines offers pathways for undergraduate and graduate students to obtain licensure for teaching in K-12 science, math, and computer science. Students were both undergraduates and graduates. Given the recent advances in GenAI, Teach@Mines asks STEM students to develop innovative future-oriented STEM curriculum and instruction focused on the future of human and GenAI interaction. This paper uses a case study approach to review curriculum and student artifacts related to GenAI teaching and learning instruction using new AI Education model, Quest: Human Learning (QHL). Case study examples of CS pre-service teachers evaluating, developing, creating, and testing AI-assisted curriculum provide insight into how the QHL model can be applied for other STEM disciplines and pre-service teacher education broadly. Teach@Mines’ implementation of GenAI teaching and learning education in pre-service teacher education empowers future K-12 educators to thoughtfully and responsibly integrate GenAI into the classroom to enhance learning experiences, support diverse learners, and prepare K-12 students for the future. As GenAI advances, applying the QHL model with a focus on teaching future generations provides a novel pathway for STEM majors to reconnect with and define human talents and abilities to solve human problems and develop technological solutions. 

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2. Pre-Service Teachers and Generative AI: Perceptions, Professional Impacts, and Pedagogical Readiness

   Yilmaz, G; Yadav, A (April 2026, American Educational Research Association)

   This single-case study explores pre-service teachers’ perceptions of AI in education, focusing on its roles, impacts, required competencies, and risks. Eighteen senior teacher candidates engaged in a four-week asynchronous online module, contributing reflections and discussions. Participants viewed AI as a supportive tool for lesson planning, differentiation, and student engagement, while expressing concerns about over-reliance, bias, privacy, and inequity. They emphasized the need for digital literacy, critical thinking, and ethical awareness. Participants advocated for professional development grounded in real-world cases and reflection to help teachers navigate AI’s complexities in educational contexts. The study argues that teacher education must go beyond technical skills to promote reflective approaches to AI integration in classrooms. 

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3. Creating engineering design integrated science units and lessons

   https://doi.org/10.1111/ssm.18346  

   Mumba, Frackson; Chiu, Jennie; Bailey, Reid (February 2025, School Science and Mathematics)

   Abstract The Next Generation Science Standards (NGSS) require science teachers to integrate engineering design into science lessons. However, many science teachers need support to create such lessons because they lack preparation in engineering. Similarly, most pre‐service teachers enter teacher preparation programs without preparation in engineering. Furthermore, the NGSS do not provide templates for creating engineering design integrated science (EDIS) lessons and activities. To address this problem in our science teacher education program, we have created templates for creating EDIS units and lessons. In this paper, we describe the EDIS templates, outcomes, and suggestions for using the templates in science teacher education. The templates were first introduced into our science methods course in 2015. Our research shows that pre‐service teachers developed skills for creating EDIS units using these templates. Some EDIS units created by our pre‐service teachers using these templates have been published for other teachers to use. Students who received EDIS instruction from our pre‐service teachers in schools improved their understanding of science concepts and engineering design process. Therefore, these templates can be used in other teacher education programs to prepare teachers in creating EDIS units, lessons and activities. 

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4. Shared Practices in Faculty-Driven Integration of Generative AI into Teacher Education Courses

   Davis, R S; Margulieux, L E; Karlin, M; Liao, Y-C (March 2025, Association for the Advancement of Computing in Education)

   Generative Artificial Intelligence (GenAI) is shifting the types and uses of computing tools in classrooms, necessitating study of how and what decisions are made about the use of AI in courses. All teachers will need to understand and be able to make important choices about GenAI for their learners. In this project, 25 teacher educators took an AI professional development course, and this paper describes their reflections and final projects. Participants at two large southeastern universities represented teacher preparation programs across all grade bands and disciplines. The shared practices the teacher educators used revealed ways GenAI can be a tool to support, not replace, a teacher. Limitations of GenAI applications are also discussed. The inclusion of all teacher educators in the project, and not just “faculty”, enabled more participation, along with the flexible timing of the online course, and personalization of the final project. 

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5. Use of Generative AI for Assessment Creation by High School Mathematics Teachers

   Grover, S; Grunge, B; Lynch, R; Clarkson, C (December 2024, Proceedings of the International Conference on Technology 4 Education 2024, Springer Nature)

   Mishra, S; Kothiyal, A; Iyer, S; Sahasrabudhe, S; Lingnau, A; Kuo, R (Ed.)

   This paper describes an experience report centered on high school mathematics teachers’ use of ALICE, a Generative AI (GenAI) module of the Edfinity homework system. Given natural language prompts (from teachers), ALICE generates the programming code (in WeBWorK format) for the corresponding interactive, isomorphic, auto-gradable problem along with hints and a solution. Writing such code would normally require programming skills. Working with teachers in high schools across a mid-western US state, this paper presents teachers’ experiences using ALICE, on prompt engineering, and the factors that influence these experiences. The implementation study also examines the impact of this experience on teachers’ classroom practice and their views about AI. Findings suggest that teachers’ experiences were largely very positive, however these experiences are shaped by several factors including their context, their attitudes toward technology and AI use, and the perceived usefulness of the tool. These factors hold different levels of importance for individual teachers. The promising results contribute to the burgeoning field of GenAI in education and understanding teacher-AI teaming. 

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