Full Description
The Role of AI in Assessment: Revolutionizing Education examines how artificial intelligence is transforming the evaluation of learning and addresses the urgent need for innovative assessment methodologies. It analyzes both the opportunities and the constraints of AI-driven assessment, responding to the growing demand for approaches that harness AI capabilities rigorously and responsibly.
Adopting an interdisciplinary perspective, the book introduces foundational AI concepts, including reinforcement learning, neural networks, deep learning, and large language models (LLMs), and makes these complex topics accessible to educators, researchers, and policymakers. Chapters on ethics, equity, and personalized learning provide a systematic treatment of AI's implications for educational measurement and accountability.
With a practical and forward-looking orientation, the volume explores applications such as automatic item generation, automated essay scoring, game-based assessment, and test security. Case studies illustrate implementation strategies and provide guidance for integrating AI into contemporary assessment systems.
This resource is designed for educators, researchers, psychometricians, administrators, and policymakers seeking to design, evaluate, and govern AI-enabled assessment frameworks. It is also well suited for graduate courses in educational technology, assessment, data science in education, and AI ethics.
Contents
Acknowledgement Notes on the Contributors Foreword 1. Introduction to Artificial Intelligence in Educational Assessment - Xinhui Xiong 2. Ethical Considerations and Challenges in AI-Based Educational Assessment - Matthew S. Johnson, Ikkyu Choi, and Andrew McEachin 3. The Evolution of Assessment with AI Technologies - Mark D. Shermis 4. A Call for Transparency in the Development and Validation of AI-Based Writing Evaluation Systems - Sue Lottridge and Amy Burkhardt 5. Personalization in Assessment to Optimize Engagement and Performance - Burcu Arslan, Randy E. Bennett, Sandip Sinharay, Jesse R. Sparks, & Kadriye Ercikan 6. Modern NLP Pipeline in Educational Measurement: From Text Analysis to Scoring and Reasoning - Constanza Mardones-Segovia, Yaxuan Yang, Cheng Tang, Jiawei Xiong, Shiyu Wang, and Allan S. Cohen 7. Rapid Item Generation and the Revolutionary Impact of Generative AI - Kimberly Swygert, Tahereh Firoozi, and Mark Gierl 8. Measuring Learning Through Play: AI-Enabled Game-Based Assessment - Xinhui Xiong and Deniz Eseryel 9. Large Language Models in Assessment - Christopher Ormerod, Alexander Kwako, Kai North and Susan Lottridge 10. Machine Learning-Based Methods for Cheating Detection in Large-Scale Assessments - Hong Jiao, Jiawei Xiong and Chandramani Lnu 11. International Work in AI for Educational Assessment - Hongyun Liu, Yongmei Zhang and Fang Luo 12. Key Considerations for Assessing and Supporting Collaboration with AI - Peter W. Foltz 13. The Impact of Artificial Intelligence on Validity and Test Validation - Stephen G. Sireci, Sergio Araneda, and Javier Suárez-Álvarez 14. The Transformative Potential of AI in Large-scale Assessments - Ummugul Bezirhan & Matthias von Davier Index
