AI-based Assessment in STEM Education Conference

K-12科学教育框架提出了一个雄心勃勃的科学学习愿景，通过整合学科科学思想、科学和工程实践以及交叉概念，使学生能够培养应对21世纪STEM挑战的能力。实现这一愿景需要将评估做法从依赖多项选择项目转变为以绩效为基础的使用中知识任务。这种新颖的评估任务既可以让学生利用知识解决问题，又可以跟踪学生的学习进度，以便教师调整教学以满足学生的需求。然而，这些基于成绩的建构反应项目往往阻碍了及时的反馈，这反过来又阻碍了科学教师使用这些评估。人工智能(AI)在应对这一评估挑战方面表现出了巨大的潜力。为了应对这一挑战，评估、人工智能和科学教育方面的专家将聚集在佐治亚大学举行为期两天的会议，以产生将人工智能融入科学评估的知识。会议围绕四个主题组织：(A)人工智能与领域特定学习理论；(B)人工智能与有效性理论和评估设计原则；(C)人工智能与技术整合理论；(D)侧重于评估实践的人工智能与教学理论。它允许参与者分享理论观点、经验发现以及研究经验。它还可以帮助确定挑战和未来的研究方向，以增加基于人工智能的评估在科学教育中的广泛使用。会议将通过Zoom向其他研究人员、博士后和学生开放。预计与会者将在这一新兴的科学评估领域建立一个网络。会议的另一项成果，将人工智能应用于STEM评估，将由哈佛教育出版社以编辑卷的形式出版。探索研究PREK-12计划(DRK-12)旨在通过研究和开发创新资源、模型和工具，显著提高Pre-K-12学生和教师在科学、技术、工程和数学(STEM)方面的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发工作的基础上，为拟议的项目提供了理论和经验上的证明。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The Framework for K-12 Science Education has set forth an ambitious vision for science learning by integrating disciplinary science ideas, scientific and engineering practices, and crosscutting concepts, so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformation of assessment practices from relying on multiple-choice items to performance-based knowledge-in-use tasks. Such novel assessment tasks serve the purpose of both engaging students in using knowledge to solve problems and tracking students’ learning progression so that teachers could adjust instruction to meet students’ needs. However, these performance-based constructed-response items often prohibit timely feedback, which, in turn, has hindered science teachers from using these assessments. Artificial Intelligence (AI) has demonstrated great potential to meet this assessment challenge. To tackle this challenge, experts in assessment, AI, and science education will gather for a two-day conference at University of Georgia to generate knowledge of integrating AI in science assessment. The conference is organized around four themes: (a) AI and Domain Specific Learning Theory; (b) AI and validity theory and assessment design principles; (c) AI and technology integration theory; and (d) AI and pedagogical theory focusing on assessment practices. It allows participants to share theoretical perspectives, empirical findings, as well as research experiences. It can also help identify challenges and future research directions to increase the broad use of AI-based assessments in science education. The conference will be open to other researchers, postdocs, and students via Zoom. It is expected that conference participants establish a network in this emergent area of science assessment. Another outcome of the conference, Applying AI in STEM Assessment, will be published as an edited volume by Harvard Education Press. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. 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 proposed 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.