Buch, Englisch, Format (B × H): 155 mm x 235 mm
ISBN: 978-3-032-34399-4
Verlag: Springer
This volume captures a pivotal moment in education: the emergence of AI as both a transformative tool and a significant disruptor. AI has become deeply embedded within classrooms, examinations, tutoring platforms, and policymaking. This volume adopts an interdisciplinary approach, integrating psychometrics, cognitive science, classroom practice, AI engineering, and ethics to explore the impact of AI affordances. It examines how we define fairness, creativity, and equity in an AI-driven landscape, addressing questions of critical importance to researchers, policymakers, and practitioners alike.
“This volume, edited by Alina von Davier and Duanli Yan, makes a major contribution because it addresses the developments in AI in education and human learning with breadth, rigor, and conceptual seriousness. The topic is expansive, the implications are substantial, and the field is moving quickly. Few edited collections could do justice to that combination of urgency and complexity. This volume manages to do so with impressive breadth and coherence. The volume brings together an outstanding group of authors across psychometrics, cognitive science, learning sciences, classroom practice, language assessment, and artificial intelligence. The result is a collection that captures the complexity of the present moment while offering a clear account of how AI is reshaping learning and assessment.”
----- Dragan Gaševic
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1. Introduction: Artificial Intelligence in Educational Learning and Assessment: Tensions and Futures.- PART 1: Human cognition in the era of AI.- Chapter 2. Thriving in AI-saturated Learning Environments: Rethinking Metacognition and Self-Regulation.- Chapter 3. Assessment in the Age of Artificial Intelligence: Rethinking Constructs, Measurement, and Responsibility.- Chapter 4. Beyond “Human in the Loop”: Human Agency before and after Generative Artificial Intelligence in Education, Assessment, and Measurement.- Chapter 5. Human Spark – Creativity and Critical Thinking in the Age of AI.- PART 2: AI for Teaching and Learning.- Chapter 6. The Evolution of a Generative AI-Powered Tutor.- Chapter 7. Modeling and Personalization in Open-ended Tutor-Student Dialogues.- Chapter 8. Agents of Learning: Designing AI Agents for Sustainable Education and Educational Change.- Chapter 9. Toward Universal Collaborative Intelligence for Explainable Pedagogy and Next-Generation Assessment.- Chapter 10. AI-Enhanced Interactive Reports for Teachers.- Chapter 11. Practical Advice for Using LLM Based Grading Systems in the Classroom.- PART 3: Measurement Science and Computational Psychometrics.- Chapter 12. Artificial Intelligence Across the Exam Development Lifecycle: A Series of Applications in a Large-Scale Certification Program.- Chapter 13. Integrating AI and Psychometric Principles into Automated Item and Content Generation and Tagging Systems.- Chapter 14. Automated Generation of Mathematics Word Problems by a Symbolically Constrained LLM.- Chapter 15. Agentic AI with Human Governance for Assessment Content Generation.- Chapter 16. Bringing LIFE (Likelihood-Free Estimation) to Small-Sample Piloting and Item Calibration: A Step-by-Step Introduction Using Neural Networks in R.- Chapter 17. Machine Learning and Large Language Models for Predicting Item Difficulty.- Chapter 18. Automated Essay Scoring in the Age of AI.- Chapter 19. Data Augmentation in Computational Psychometrics.- Chapter 20. Predicting Missing Response with Multi-Level and Multi-Class Machine Learning Approach on Process Data.- Chapter 21. Exploring the Use of Toulmin Diagrams and Generative AI in Conversation-based Assessment.- PART 4: AI for Multilingual International Assessment.- Chapter 22. Using GenAI for Translation in an Education Setting.- Chapter 23. Large Language Models for Multilingual Assessment Translation: Empirical Evidence and Lessons Learned.- Chapter 24. Large Language Models for Multilingual Scoring in TIMSS and PIRLS: Reliability, Efficiency, and Cross-National Comparability.- PART 5: Responsible Use of AI.- Chapter 25. Custodianship of AI in Education: Foundations for Sustainable and Responsible Practice.- Chapter 26. The Concept of Fairness in Educational Measurement - From Classical Measurement Models to Machine Learning and AI.- Chapter 27. Responsible Use of Speech Processing Technology.- Chapter 28. Detecting AI-Generated Essays in Writing Assessment: Responsible Use and Generalizability Across LLMs.- Chapter 29. Afterword: Learning and Assessment in the Era of Artificial Intelligence.
