Exploring public perceptions of generative AI and education

Topic modelling of YouTube comments in Korea

Bibliographic Data

ID21484773
AuthorsHyo-Jeong So (0000-0002-1713-9653, Ewha Womans University, corresponding author), Hyeji Jang (0000-0001-9529-1610, Ewha Womans University), Minseon Kim (Ewha Womans University), Jieun Choi (0000-0002-1989-2091, Ewha Womans University), Choi Jieun (0000-0002-5445-1910, Ewha Womans University)
Year2024
Volume44
Issue1
Pages61-80
Publication date2024-01-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAsia Pacific Journal of Education (JOURNAL)
Journal identifiersISSN: 0218-8791 • E-ISSN: 1742-6855
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/02188791.2023.2294699
OpenAlexW4389856535
LanguageEN
Citations received3
References cited36

This study aims to investigate the public’s perceptions regarding the integration of Generative AI (GenAI) in education by analysing comments on YouTube news clips. The study collected public comments from YouTube news clips disseminated by three prominent broadcasters in South Korea between December 2022 and June 2023. Two dimensions of public perceptions were examined: sentiments and prevalent topics. Employing machine learning techniques, we conducted sentiment analysis and topic modelling on the crowdsourced dataset of 18,566 comments from 66 YouTube news clips. The first research question focused on public sentiments towards GenAI and education. Findings reveal a predominance of neutral sentiments. Rather than adopting extreme positions of complete acceptance or rejection, the public displayed an inclination to appreciate the intricate nuances of GenAI’s implications. The second research question sought to identify the main topics emerging from public comments on GenAI and education. We identified 11 distinct topics where two topics are directly linked to educational implications: demands for changes in learning and assessment methods, and the use of GenAI in higher education. Based on the key findings, we draw implications that can inform a broader understanding of public sentiment and perspective towards GenAI and education

CLIPS · Generative grammar · Perception · Political science · Public education · Public relations · Sentiment analysis · Sociology · Topic model · Computational and Text Analysis Methods · Computer Science · Misinformation and Its Impacts · Psychology · Sentiment Analysis and Opinion Mining · Artificial Intelligence

Tools

Open DOI