Research Report: AI and Technology Integration

The emergence of publicly available generative artificial intelligence (AI) in late 2022 has significant implications for education. Despite deficit views of AI as providing increased opportunity for students to engage in cheating and plagiarism, evidence is emerging that AI does have the potential to assist and enhance teaching practice within all levels of education (UNESCO, 2023). This session presents initial findings from a study concerned specifically with elementary school mathematics education. This discipline is typically lagging in its use of digital resources when compared to other disciplines such as language and science (OECD, 2016). The study is currently being conducted in Australian elementary schools to explore the ways in which teachers are able to use AI to develop rich mathematical tasks along with associated resources such as reflection prompts and assessment rubrics. In this session, qualitative data gathered from teacher interviews, lesson observations, student focus groups, and AI-generated resource documentation will be presented. Analysis of teachers' pedagogical content knowledge required to effectively engage with tools such as ChatGPT, Copilot, and Tutero will also be presented, along with details of the designed tasks.  

The integration of generative artificial intelligence (GenAI) into K–12 mathematics instruction presents an opportunity to enhance and transform teaching and learning, but to realize this potential, it is imperative to understand how teachers use GenAI tools and the learning supports they rely on for adoption. In this session, we present the findings of an exploratory study aimed at understanding teachers' adoption of GenAI tools for various instructional tasks such as lesson planning, assessment, and communication. Through in-depth case studies with 15 diverse math and science teachers and 20 colleagues in their professional network, we examined how teachers incorporated GenAI tools to augment lesson preparation, formative assessment, and personalized student feedback. Preliminary findings suggest that GenAI can significantly reduce teachers' cognitive workload, improve instructional efficiency, and provide greater opportunities for more exploratory and personalized learning and deeper conceptual discourse in class. Through the professional network interviews, we also investigated the professional, social, and learning supports that helped teachers adopt GenAI tools for transforming their practice to provide greater student opportunities to learn. In this session, we will discuss the conceptual frameworks guiding the study, including substitution, augmentation, and transformation models of technology adoption (Hamilton et al., 2016), and we will present initial insights and implications for teachers and the school leaders who support them. Our session Q&A and discussion will focus on the practical applications of GenAI in classrooms and the necessary teacher training to optimize GenAI use in education. 

This NSF-funded study examines how AI can support K-12 mathematics teachers in designing high-quality lesson plans, using the Curricular Noticing Framework. The study involves 20 in-service teachers from diverse educational settings, utilizing AI to enhance their intended curriculum and evaluate outcomes through lesson plan quality rubrics, focusing on aspects such as increasing cognitive demand, adapting to diverse student needs, and fostering student-centered learning. The study aims to provide practical insights into how AI integration can streamline the lesson planning process and boost content rigor, student engagement, and inclusive instruction. Preliminary findings suggest that supporting teachers in learning the appropriate skills and knowledge is crucial for maximizing AI's potential in education. By leveraging AI effectively, teachers may have more bandwidth to focus on in-class instruction, potentially leading to improved student learning experiences.