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Generative Al for College Students: A Collaboratively Developed Online Microcourse on GenAI in the College Classroom

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Wydane w:Communications in Information Literacy vol. 19, no. 1 (2025), p. 113-132
1. autor: Willenborg, Amber
Kolejni autorzy: Withorn, Tessa
Wydane:
Communications in Information Literacy
Hasła przedmiotowe:
University of Louisville
User training
Collaboration
Academic libraries
College students
Librarians
Literacy
Ethics
Higher education
Teachers
Research ethics
Artificial intelligence
Libraries
Teaching
Cooperative learning
Digital media
Classrooms
Teaching methods
Scholarship
Artificial intelligence literacy
High Achievement
Departments
Critical Thinking
Educational Development
Influence of Technology
College Faculty
Expertise
Educational Technology
Information Literacy
Doctoral Degrees
Library Instruction
Faculty Development
Academic Support Services
Learning Management Systems
Electronic Learning
Library Personnel
Course Content
Learner Engagement
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Streszczenie:Academic libraries have taken a variety of approaches to addressing the opportunities and challenges of generative artificial intelligence (GenAI) in higher education. For universities that have no standard policy around GenAI, instructors are left with little guidance on how to teach students about GenAI in the classroom and may have varying levels of comfort with GenAI and its applications. To address the need for more instruction around GenAI, a team of librarians and university writing center and digital media suite staff took an innovative approach to teaching AI literacy by creating a six-lesson microcourse in their learning management system all about GenAI: how it works, its limitations, and how to use it efficiently and ethically for college research and writing. Microlearning offers a robust avenue for delivering instruction created by multiple experts, while also considering instructors' time constraints around addressing both course content and AI literacy.
ISSN:1933-5954
DOI:10.15760/comminfolit.2025.19.1.7
Źródło:Library Science Database