Future of Learning with Large Language Models: Applications and Research in Education [Hardcover]

PART I: FOUNDATIONS, FRAMEWORKS, AND ETHICAL CONSIDERATIONS. Responsible, Ethical, and Effective Use of LLMs in Higher Education. Prompting Learning: The EPICC Framework for Effective Prompt Engineering in Education. Improving Large Foundation Models in Education for Multi-cultural Understanding. Engagement Dynamics in AI-Augmented Classrooms: Factors and Evolution. Engagement Diversity in AI-Enhanced Learning: Demographic and Disciplinary Perspectives. PART II: PRACTICAL TOOLS AND APPLICATIONS FOR EDUCATORS. vTA: How an Instructor Leverages Large Language Models for Superior Student Learning. A Step Towards Adaptive Online Learning: Exploring the Role of GPT as Virtual Teaching Assistants in Online Education. Leverage LLMs on Knowledge Tagging for Math Questions in Education. The Educators Co-Pilot: Leveraging Generative AI and OERs for Learning Path Design. PART III: STUDENT-CENTERED LEARNING AND EMERGING TRENDS WITH AI. CHAPTER 10: Examining Graduate Students Experiences in Using Generative AI for Academic Writing: Insights from Cambodian Higher Education. Generating Feedback for Programming Exercises with OpenAIs o1-preview. From Algorithms to Classrooms:? The Future of Education with Large Language Models.

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Large language models (LLMs), advanced AI systems trained on vast text datasets, are reshaping education. This book explores their role in revolutionizing learning through cognitive reinforcement, personalization, curriculum-wide applications, and teacher training.

The book covers theoretical foundations on how LLMs can enhance learning, cognitive reinforcement, improving learning efficiency, and personalization in learning, applications across the curriculum, teacher training and support for LLM integration, using in assessment and evaluation, and measuring the impact and affordances of LLMs. It acknowledges the challenges that come with integrating LLMs into education and will address the rl#r