An AI-Enhanced Colleague for Teachers: Developing and Studying an Innovative Platform for Efficient, Inclusive Middle-Grade Mathematics Lesson Planning

Artificial intelligence (AI) is being used more widely to streamline routine tasks and generate resources that can then be adapted and improved by professionals. For teachers, this could mean using AI to plan quality mathematics lessons to meet the diverse needs of their students. This lesson preparation is the first step of effective instruction but often time-consuming and needs substantial training to do it well. On the other hand, the current offerings by many AI technologies generate instructional materials that are inconsistent in quality, often do not reflect research-based best practices of teaching, or sometimes can be filled with mathematical errors and inaccuracies. To address these challenges, this project will develop a technology platform that can streamline lesson planning and allow teachers to adapt resources to their students' needs. The project would design and investigate an AI-powered lesson plan tool for middle-grades mathematics teaching called Colleague. Using existing, open-access lesson plans that have been vetted in prior work, the project would refine the tool for generating math lesson plans and supporting teachers to iteratively improve their instruction. Streamlining lesson planning would open more time for teacher creativity and reduce job stress. The study would explore how teachers use Colleague to plan and adapt lessons, the influence on teaching, and the students' learning. The study would investigate the usability and feasibility of Colleague. To address these challenges, this project has three aims: (1) integrates domain knowledge (e.g., lesson plan quality measures and lesson templates) into the latest AI developments in semantic search, recommender systems, nudging chatbot, and multimodal content generation. Our algorithm development heavily relies on math educators and researchers’ judgment and guidance to ensure algorithm accuracy, usefulness, and trustworthiness; (2) test and iteratively enhance Colleague’s performance through studying teachers’ activities on the platform and their lesson planning behavior; and (3) using mixed methods embedded in an implementation research design, study Colleague’s efficacy on reducing teachers’ job stress, improving teachers’ instructional effectiveness and students’ mathematical learning, and the factors and conditions that enable Colleague’s implementation in schools. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.