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Starts 28 June 2025 03:48

Ends 28 June 2025

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Pedagogy Meets AI: Challenges and Innovations in LLM-Powered Education

Discover the transformative potential of artificial intelligence in education through the insightful presentation by Shashank Sonkar. This event delves into the integration of AI and pedagogy, focusing on how large language models can revolutionize teaching methods. Participants will gain a deeper understanding of the challenges faced and the.
University of Central Florida via YouTube

University of Central Florida

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Overview

Discover the transformative potential of artificial intelligence in education through the insightful presentation by Shashank Sonkar. This event delves into the integration of AI and pedagogy, focusing on how large language models can revolutionize teaching methods.

Participants will gain a deeper understanding of the challenges faced and the innovative solutions that are emerging in the field of AI-powered education.

Whether you're an educator, a student, or an AI enthusiast, this discussion is your gateway to understanding the future landscape of education. Available on YouTube, this session is part of a series of Artificial Intelligence and Computer Science courses designed to equip you with the knowledge needed for the coming educational advancements.

Syllabus

  • Introduction to LLMs in Education
  • Overview of Large Language Models (LLMs)
    Historical context and evolution of AI in education
  • Pedagogical Foundations
  • Core principles of effective pedagogy
    How AI can support and transform these principles
  • Technical Understanding of LLMs
  • Basic architecture and functioning of LLMs
    Current capabilities and limitations
  • Integration of LLMs in Educational Tools
  • Examples of LLM-powered educational applications
    Case studies of successful implementations
  • Innovations in LLM-Powered Learning
  • Personalized learning paths and adaptive learning
    Enhancing student engagement and motivation
  • Addressing Challenges in LLM Implementation
  • Data privacy and ethical considerations
    Ensuring accessibility and equity
  • Evaluating AI Tools in Education
  • Metrics for assessing effectiveness and outcomes
    Qualitative feedback mechanisms from educators and students
  • Future Directions and Trends
  • Emerging technologies and their potential impact
    Predictions for the future of AI in education
  • Conclusion and Reflections
  • Recap of key lessons learned
    Open discussion on ongoing challenges and opportunities
  • Additional Resources
  • Suggested readings and research papers
    Online communities and forums for educators integrating AI

Subjects

Computer Science