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Artificial Intelligence in Education: Innovations in Assessment, Literacy, and Performance Analysis (Table 5)
Wed, April 23, 12:40 to 2:10pm MDT (12:40 to 2:10pm MDT), The Colorado Convention Center, Floor: Ballroom Level, Four Seasons Ballroom 2-3Session Type: Roundtable Session
Abstract
This session explores innovative applications of AI and machine learning in educational assessment and research ranging from AI literacy, test taking behavior, self-reported instruments. These studies showcase AI's potential to enhance educational measurement, uncover performance patterns, and improve assessment design, while also highlighting the opportunities and challenges of AI integration in education.
Sub Unit
Chair
Papers
Development of an AI Literacy Assessment for Nontechnical Individuals: A Pre- and In-Service Teachers’ Case - Lu Ding, University of South Alabama; Sohee Kim, University of South Alabama
Exploring Student Test-Taking Behaviors in PIRLS 2021 Using Machine Learning Techniques - Zhushan Mandy Li, Boston College; Jihang Chen, Boston College
Improving Student Performance Through the Sequencing of Test Items: Recommendations for AI-Driven Assessment - John Baffoe, University of Wisconsin - Milwaukee; Daniel Asamoah, Universiti Brunei Darussalam; Auwal Halabi Kabara, Universiti Brunei Darussalam
Utilizing Artificial Intelligence Models in Loneliness Item Evaluation - Joshua Spieles, University of Toledo; David Dueber, University of Toledo; Michael D. Toland, University of Toledo; Allyson Givens, University of Toledo; Falynn Thompson, University of Toledo