Open Educational Practices in the Artificial Intelligence Era

Chapter 01. Artificial intelligence in the future of education.- Chapter 02. One year of Artificial Intelligence disruption. Preuniversity teacher adoption and evolution.- Chapter 03. Exploring Data-Driven and Human-Centered Methodologies in Engineering Education: A Preliminary Validation Framework.- Chapter 04. Improving Course Design Through LLM-Assisted Educational Process Mining. Chapter 05. AI-Assisted Continuous Assessment in Programming Education: Design and Evaluation.- Chapter 06. Building an intelligent ecosystem for open educational resources: from discovery to personalized learning.- Chapter 07. Iterative Development and Validation of Rubrics for Assessing Educational Robotics Projects.- Chapter 08. Using generative artificial intelligence to conduct a problem-solving workshop.- Chapter 09. Pedagogical innovation in teaching data analysis in engineering through project-based collaborative learning and design thinking.- Chapter 10. Trustworthy and Open Educational Practices with AI: Small Language Models for Digital Sovereignty in Higher Education.- Chapter 11. Designing ethical AI companions for software engineering education: Lessons from a supervised chatbot for UML learning.- Chapter 12. Cocreation and evaluation in the era of Generative Artificial Intelligence tools: Learning experience revisited.- Chapter 13. An Academic Resource Development Experience through Service Learning Supported by Artificial Intelligence Tools for Identifying Earthquake Damage on Buildings.- Chapter 14. UniversiActuality: Artificial Intelligence as an Effective Tool Against Journalism Students’ Disinterest in Current Affairs.- Chapter 15. Artificial Intelligence as a Pedagogical Tool: Student Experiences with Concept Mapping in Higher Education.- Chapter 16. Generative AI for Analyzing Real Teamwork Interactions and Identifying Socioemotional Competencies.- Chapter 17. Integrating Open Educational Resources, Active Learning, and Artificial Intelligence for Lifelong Learning in Engineering Education.- Chapter 18. Fine-tuning Large Language Models for Educational Applications: Use Cases, Architectures, and Techniques.