Copyright/Fair Use + GenAI for Teachers & Librarians
Workshop Description
This workshop provides teachers and librarians with practical guidance on using and sharing digital materials—and GenAI outputs—without running into avoidable copyright or privacy issues. We’ll demystify fair use in school contexts, Creative Commons licenses, and what “AI-generated” does (and doesn’t) change about ownership and attribution. Through short scenarios (slides, handouts, student publishing, remixing images, using excerpts, prompting models with texts), participants practice a simple decision tree: permission needed, license available, attribution required, or use is likely fair. Leave with ready-to-copy attribution language, a classroom remix policy, and a sharing checklist for websites/webpages.
Workshop Outcomes
Workshop Leader
Kamal Chawla | Assistant Professor of Education and Applied Quantitative Methods | University of Maine
Email: kamal.chawla@maine.edu
LinkedIn: www.linkedin.com/in/kamalam2
Dr. Kamal Chawla is a statistician, meta-analyst, and missing data specialist who serves as an Assistant Professor of Education & Applied Quantitative Methods. His work is at the intersection of machine learning, missing data, and meta-analysis, and he is dedicated to leveraging advanced quantitative methods to address critical challenges in education. Dr. Chawla’s research agenda is twofold: methodologically, he focuses on developing and refining research methods through data science and machine learning techniques to produce robust and unbiased outcomes. On the applied side, his research is centered on enhancing student learning in elementary and secondary classrooms by creating teaching strategies that are not only effective but also tailored to the diverse needs of individual students. By integrating these cutting-edge techniques, Dr. Chawla’s work aims to bridge educational gaps, empower students from all backgrounds, and contribute to a more prosperous society.
