An Intelligent Assistant to Support Teachers and Students in Simulation-Based Science Learning

该项目将开发一个基于人工智能的对话框架（iLab），为中学生和教师在基于模拟的科学实验中创建基于对话的交互式实验室体验。该框架的一个关键组件是智能对话代理 (SimPal)，它将与教师进行对话，征求他们与模拟实验相关的教学目标，并使用计算表示来存储它们。然后，当学生进行实验以增强他们的学习体验时，代理将使用这种表示来促进和调解与学生的交互式对话（由最先进的大型语言模型提供支持）。座席会主动询问学生反思问题，为他们提供实时定制反馈，跟踪学生的进度，然后分析他们的回答并向老师报告。与现有的智能辅导系统和教学对话代理不同，该框架将与任何领域的任何现成的第三方模拟一起使用，并可供任何教师或美国学区使用，这是该项目的独特功能。此外，教师将作为合作伙伴共同开发和部署这项技术。因此，该项目预计将为学生的学习做出独特的贡献，从而在美国学校中具有广泛的影响力和吸引力。根据该项目开发的框架将成为教师与大教室中每个学生之间的桥梁，而教师不可能亲自为每个学生提供支持。它将让学生参与有意义的对话，根据学生的反应和教师的教学目标，同时适应学生和教师，从而引导学生更深入地理解基本的科学概念和原理。评估研究将在威斯康星州的多个教室进行，教师将把技术融入到常规课程中。该项目将规划一条智能支持之路，旨在改善科学课堂学习的四大挑战：（1）随时随地获得高质量的学习材料； (2) 适应性，确保具有不同背景、经验和能力的学生收到基于教师目标的定制反馈； (3)可重复性，学生可以在短时间内进行多次实验并获得实时反馈； (4) 可扩展性，以减轻开展面向探究的科学研究的负担。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will develop an artificial intelligence-based conversational framework (iLab) to create dialog-based interactive laboratory experiences for middle school science students and teachers in the context of simulation-based science experiments. A key component of the framework is an intelligent conversational agent (SimPal) that will engage with teachers in a dialog to solicit their instructional goals associated with simulation experiments and store them using a computational representation. The agent will then use this representation to facilitate and mediate an interactive dialog (powered by state-of-the-art large language models) with students as they run experiments to enhance their learning experience. The agent will proactively ask students reflection questions, provide them with real-time customized feedback, track students' progress, and then analyze their responses and report back to the teacher. Unlike existing intelligent tutoring systems and pedagogical conversational agents, the framework will work with any off-the-shelf third-party simulations in any domain and be used by any teacher or U.S. school district, a unique feature of this project. Further, teachers will work as partners in developing and deploying this technology. As such, the project is expected to make unique contributions to benefit student learning and, thereby, have a broad reach and appeal in U.S. schools.The framework developed under this project will serve as a bridge between the teacher and each student in large classrooms where it is not possible for the teacher to support each student personally. It will engage the student in a meaningful dialog, informed both by the responses from the student and by the teacher’s instructional goals while adapting to both the student and the teacher, thereby guiding the student toward a deeper understanding of the underlying scientific concepts and principles. Evaluation studies will be conducted in multiple Wisconsin classrooms where teachers will integrate the technology into the regular curriculum. The project will chart a path toward intelligent support designed to ameliorate four major challenges to learning in the science classroom: (1) access to high-quality learning materials at any time and place; (2) adaptability to ensure that students with a variety of backgrounds, experiences, and abilities receive custom feedback based on the teacher’s goals; (3) repeatability so students can run several experiments in a short time and receive real-time feedback; and (4) scalability to reduce the burden of running inquiry-oriented science investigations.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.