Concurrent Session IV: 2:00 PM – 3:00 PM, EST (60 minutes)
Workshop 10: Unplugged-to-Plugged: A Blended Approach to Coding Instruction
By Jessica Kerr, Barnett Shoals Elementary in Athens, GA; University of Georgia
Target Audience: Grades 3-5 Teachers
This session explores strategies for guiding 3rd- 5th grade students from “unplugged” coding activities - hands-on, screen-free exercises that build computational thinking- into “plugged” coding experiences using digital tools. Participants will learn how to scaffold skills, connect concepts between physical and digital formats, and create engaging transitions that deepen students’ understanding of coding principles.
Workshop 10: Unplugged-to-Plugged: A Blended Approach to Coding Instruction
By Jessica Kerr, Barnett Shoals Elementary in Athens, GA; University of Georgia
Target Audience: Grades 3-5 Teachers
This session explores strategies for guiding 3rd- 5th grade students from “unplugged” coding activities - hands-on, screen-free exercises that build computational thinking- into “plugged” coding experiences using digital tools. Participants will learn how to scaffold skills, connect concepts between physical and digital formats, and create engaging transitions that deepen students’ understanding of coding principles.
Workshop 11: Introducing Machine Learning to Young Learners: Practical Approaches
By Gihan Mohamad, William Paterson University of New Jersey
Target Audience: Grades K–12 Teachers, School Librarians, Instructional Technology Specialists, and Curriculum Coordinators
As artificial intelligence becomes increasingly embedded in daily life, students must begin to develop an early understanding of machine learning (ML), a key branch of AI. This workshop introduces K–12 educators and school librarians to foundational concepts in machine learning and offers age-appropriate, hands-on strategies to help students grasp how machines "learn" from data. No prior coding or technical background is required.
By Gihan Mohamad, William Paterson University of New Jersey
Target Audience: Grades K–12 Teachers, School Librarians, Instructional Technology Specialists, and Curriculum Coordinators
As artificial intelligence becomes increasingly embedded in daily life, students must begin to develop an early understanding of machine learning (ML), a key branch of AI. This workshop introduces K–12 educators and school librarians to foundational concepts in machine learning and offers age-appropriate, hands-on strategies to help students grasp how machines "learn" from data. No prior coding or technical background is required.
Workshop 12: Empowering Computer Science (CS) Teaching with Artificial Intelligence (AI): Practical Uses of Large Language Models in Programming Education
By Meina Zhu, Wayne State University
Target Audience: Middle and High School Teachers
This workshop will introduce K–12 and postsecondary educators to the practical applications of large language models (LLMs), such as ChatGPT and Copilot, in computer science programming instruction. Participants will explore how LLMs can assist teachers in planning lessons, designing activities, and creating assessments, as well as support students with code generation, debugging, and explanation to enhance learning and engagement. The session will feature hands-on demonstrations and provide strategies for integrating AI tools ethically and effectively into programming education. By the end of the workshop, attendees will have a foundational understanding of how to leverage LLMs to support diverse learners, foster computational thinking, and strengthen their instructional practices.
By Meina Zhu, Wayne State University
Target Audience: Middle and High School Teachers
This workshop will introduce K–12 and postsecondary educators to the practical applications of large language models (LLMs), such as ChatGPT and Copilot, in computer science programming instruction. Participants will explore how LLMs can assist teachers in planning lessons, designing activities, and creating assessments, as well as support students with code generation, debugging, and explanation to enhance learning and engagement. The session will feature hands-on demonstrations and provide strategies for integrating AI tools ethically and effectively into programming education. By the end of the workshop, attendees will have a foundational understanding of how to leverage LLMs to support diverse learners, foster computational thinking, and strengthen their instructional practices.
