AI in Assessment
AI-Assisted Assessment in Education: Transforming Assessment and Measuring Learning
Artificial intelligence is reshaping how teachers gauge learning, and this new volume shows you how to harness that change responsibly. Written by assessment veteran Dr. Goran Trajkovski and psychometrician Dr. Heather Hayes, the book starts with plain-language explanations of machine learning in assessment, then walks through practical methods for building AI-driven question formats, running adaptive tests, and interpreting the resulting data. Each chapter blends research findings with field examples to help you redesign quizzes, projects, and high-stakes exams for greater accuracy and speed. (SpringerLink)
Beyond technique, the authors devote full sections to fairness, privacy, and the human judgment that must guide any automated scoring system. Case studies from K-12, higher education, and workforce training reveal the real-world gains and trade-offs institutions face when algorithms give real-time feedback or flag at-risk learners. A concluding “Practical Guide” offers step-by-step checkpoints for piloting AI tools in your own courses or credential programs. (SpringerLink)
Researchers, administrators, instructional designers, and policy makers will find actionable advice for scaling personalized, equitable assessment while meeting emerging regulations. If you are ready to move past manual grading and toward data-rich evidence of learning, this book provides the roadmap.
Recommended citation
Trajkovski, G., & Hayes, H. (2025). AI-Assisted Assessment in Education: Transforming Assessment and Measuring Learning. Springer. https://doi.org/10.1007/978-3-031-88252-4