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As generative AI (GenAI) becomes an integral component of the socio-technical landscape in teaching and learning, it is crucial for teacher education programs to prepare pre service teachers to assess its affordances and limitations. However, research on pre-service teachers’ attitudes toward GenAI is still emerging. Understanding these perspectives is essential for informing teacher education programs about where pre-service teachers stand and how to advance their AI literacy. This article explores pre-service teachers’ understanding and application of GenAI for their envisioned future classrooms. We collected data from pre-service teachers at a Midwestern United States public university, analyzing 17 survey responses quantitatively and conducting qualitative analyses of eight interviews and two lesson design challenge case studies. Our findings indicate that pre-service teachers generally held positive views about GenAI, particularly in terms of its potential for lesson planning, resource creation, and reducing workload. However, they expressed concerns about students’ over-reliance on AI, which they thought could potentially undermine teacher-student interactions. Furthermore, the study found that pre-service teachers identified limitations while using ChatGPT for lesson design and recognized its shortcomings, especially when they had strong content knowledge.
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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.
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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- Award ID(s):
- 2243554
- PAR ID:
- 10700959
- Publisher / Repository:
- 2025 ASEE Annual Conference & Exposition
- Date Published:
- Format(s):
- Medium: X
- Location:
- Montreal Canada
- Sponsoring Org:
- National Science Foundation
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