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Session Type: Roundtable Session
Advances in Artificial Intelligence (AI) offer new possibilities for classroom assessment and feedback, yet scholars have raised concerns surrounding trustworthiness, implementation, and ethical use. This roundtable session brings together scholars who are examining these topics across multiple AI and VR-based assessment approaches. Paper topics include large language model scoring with learning progression rubrics, generative AI feedback, and virtual reality-enabled authentic assessments. Roundtable presenters will explore findings that will support stakeholders (e.g., scholars, teachers) in understanding both the benefits and detriments of AI use in classroom assessment.
Designing Authentic Assessment with Virtual Reality and AI: A Systematic Review - Emmanuel Chukwunenye Amaechi, University of Calgary; Kim H. Koh, University of Calgary
Evaluating GenAI Feedback in Classroom Assessment: A Meta-Synthesis - Elie Chingyen Yu, University at Albany - SUNY; Carla Evans, Center for Assessment
An AI Framework for Identifying Uncertainty and Weaknesses in Written Responses to Usable Knowledge Tasks - Namsoo Shin, Michigan State University; Xunlei Qian, Michigan State University; Yue Xing, Michigan State University; Cory S. MIller, Michigan State University; Joseph S. Krajcik, Michigan State University
GenAI-Enhanced Automated Scoring of Scientific Explanation Open-Ended Responses Using Learning Progression Rubrics - Lingxiao Hao, East China Normal University; Xiangdong Yang, East China Normal University; Guangzhen Gao, Jiangsu Normal University