Examining the Impact of Flipped Learning for Developing Young Job Seekers' AI Literacy

Hyo-Jin Kim, Hyo-Jeong So, Young-Joo Suh

Abstract: While AI literacy is regarded as an essential competency to become a citizen in a rapidly changing society, it is challenging for people without computer science (CS) backgrounds to develop a sufficient level of AI competency. The main goal of this research is to examine the impact of the flipped learning approach to equip non-CS major students who intend to pursue careers in AI-related fields with basic AI literacy. Among various learner-centered methods, flipped learning was chosen as the main pedagogical frame to design an AI literacy curriculum. The participants were 80 adult learners who enrolled in the AI education program in Korea. The control group (N = 40) was taught in traditional instructor-centered methods whereas the experimental group (N = 40) was taught with a flipped learning method. Our research results indicate that AI literacy education with flipped learning improves the learning achievements of both CS majors and non-majors, especially effective for higher-order problem-solving skills.