This volume gives an overview on how Artificial Intelligence (AI) is transforming educational learning and assessment by addressing challenges in test development, administration, scoring, and student learning progression. AI integrates advanced technologies into teaching and evaluation, enhancing learning experiences, personalizing education, and streamlining assessments through data-driven algorithms and machine learning techniques. AI-powered systems adapt to individual learners, provide real-time feedback, and automate administrative tasks like grading.

Applications of AI in education include automated item generation (AIG), natural language processing (NLP), learning analytics, adaptive learning, intelligent tutoring systems, and automated scoring. Recently, generative AI has enabled large-scale test item development, such as "the item factory," which automates item generation, quality assurance, and crowdsourcing techniques for adaptive testing. AI enhances assessment accuracy by reducing human biases and errors while enabling personalized learning paths that cater to different student needs and learning styles. It empowers educators with data-driven insights, improving instructional strategies and early intervention for struggling students. Additionally, AI's scalability allows for the efficient delivery of personalized education without increasing teaching staff.

However, ethical concerns, including bias, data privacy, and equitable access are also carefully addressed. Rather than replacing educators, AI should serve as a collaborative tool that enhances teaching and learning, fostering a more inclusive, effective, and student-centered education system.

This book is useful for graduate students, researchers and education policy makers.

Dr. Alina A. von Davier is the Chief of Assessment at Duolingo, where she provides strategic leadership for the Duolingo English Test. She is also the Founder and CEO of EdAstra Tech, a partner with LearnLaunch Accelerator, and a renowned researcher specializing in computational psychometrics, and AI for educational applications. An executive leader with expertise in building exceptional R&D teams, she serves as a board member, strategic advisor, and investor, bringing extensive technology and commercial acumen, and sector insight to EdTech organizations globally.

Dr. Duanli Yan is an innovative researcher at Measurement Incorporated, where she focuses on AI-based applications in educational assessment. She previously served as the Director of Data Analysis and Computational Research at ETS, where she led evaluations for automated scoring engine upgrades. Dr. Yan possesses extensive expertise in adaptive testing, psychometric research, test security, and the implementation of emerging technologies.

The editors have published extensively in peer-reviewed journals and authored several influential books. Their co-edited volume, Computerized Multistage Testing: Theory and Applications (2014), received the 2016 AERA Division D award for Significant Contribution to Educational Measurement.