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1. The implications of generative AI for mathematics education

   Walkington, C (November 2024, Proceedings of the forty-sixth annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Generative Artificial Intelligence has become prevalent in discussions of educational technology. These AI models can engage in human-like conversation and generate answers to complex questions in real-time, with education reports accentuating their potential to make teachers’ work more efficient and improve student learning. In this paper, I provide a review of the current literature on generative AI in mathematics education, focusing on four areas: generative AI for mathematics problem-solving, generative AI for mathematics tutoring and feedback, generative AI to adapt mathematical tasks, and generative AI to assist mathematics teachers in planning. I then discuss ethical and logistical issues that arise with the application of generative AI in mathematics education, and close with some observations, recommendations, and future directions for the field. 

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2. 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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3. More confidence more critical: Investigating secondary mathematics preservice teachers’ use of an Artificial Intelligence chatbot as a curriculum development tool

   https://doi.org/10.55284/ajce.v7i2.1278  

   Aga, Zareen Gul; Sawyer, Amanda Gantt; Wolfe, Marcus (December 2024, American Journal of Creative Education)

   The manuscript shares findings from a study engaging secondary mathematics preservice teachers using Artificial Intelligence (AI) chatbots to design mathematics lesson plans. Phenomenology was employed to investigate how six secondary preservice teachers used AI chatbots and navigated this new resource compared to their knowledge and experience in developing culturally responsive mathematics lesson plans that included mathematics and social justice goals. Our data analysis revealed that PSTs’ confidence in their Mathematical Content Knowledge and Pedagogical Content Knowledge allowed them to be critical of using AI-generated lesson plans. This finding contrasted with previous research on elementary education preservice teachers who gave away their decision-making agency to AI chatbots, especially about mathematics. The data suggests that the secondary PSTs had confidence in their Mathematical and Pedagogical Content Knowledge, making them more critical of the AI-generated lesson plans. The findings also indicate that AI tools can help teachers learn about Technological Pedagogical Knowledge (TPK). Overall, the data stressed the need to support PSTs in using AI chatbots critically. The implications of this study provide possible ways to help PSTs overcome their overconfidence in AI chatbots and imply that more professional development tools and programs must be constructed to help inservice teachers use AI tools. 

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4. K-12 Education in the Age of AI: A Call to Action for K-12 AI Literacy

   https://doi.org/10.1007/s40593-023-00358-x  

   Wang, N; Lester, J (June 2023, International journal of artificial intelligence in education)

   The emergence of increasingly powerful AI technologies calls for the design and development of K-12 AI literacy curricula that can support students who will be entering a profoundly changed labor market. However, developing, implementing, and scaling AI literacy curricula poses significant challenges. It will be essential to develop a robust, evidence-based AI education research foundation that can inform AI literacy curriculum development. Unlike K-12 science and mathematics education, there is not currently a research foundation for K-12 AI education. In this article we provide a component-based definition of AI literacy, present the need for implementing AI literacy education across all grade bands, and argue for the creation of research programs across four areas of AI education: (1) K-12 AI Learning & Technology; (2) K-12 AI Education Integration into STEM, Language Arts, and Social Science Education; (3) K-12 AI Professional Development for Teachers and Administrators; and (4) K-12 AI Assessment. 

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5. Mentoring future mathematics teachers: Lessons learned from four mentoring partnerships.

   Hodge, A. (October 2019, Journal of mathematics education at Teachers College)

   Mentoring is an important aspect of mathematics teacher education, and in particular, pre-service teacher education. Faculty at a large Midwestern university developed and refined a mentoring program designed to help pre-service secondary mathematics teachers, called Scholars, become future leaders in mathematics education. This paper describes how faculty mentors leveraged challenges in the mentoring program’s early stages based on their reflections and initial mentee outcomes to create a more effective mentoring program. Recommendations based on research and practice are provided for other university programs interested in mentoring future mathematics teachers. 

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