The Relationship between Online Learning Type Preferences and AI-based Learning Support Needs: An Analysis based upon Academic Discipline

The purpose of this study was to identify learners' preferred types of online learning (synchronous/asynchronous), examine differences in AI-based learning support needs according to online learning types, and explore how these patterns vary across academic disciplines. The study participants consisted of 743 students enrolled at K University, and data were collected through an online survey. The composition of participants was as follows: foreign languages (48.2%), humanities and social sciences (16.3%), business and economics (12.6%), science and engineering (19.9%), and other fields (3.0%). Frequency analysis, cross-tabulation analysis, chi-square test, McNemar-Bowker test, and residual analysis were conducted using SPSS 29.0. The main findings of the study are as follows. First, learners overwhelmingly preferred asynchronous recorded classes regardless of their academic discipline, and the differences across disciplines were not statistically significant. Second, 'content summarization and organization' was the most requested AI support in both synchronous and asynchronous classes. The differences in AI support needs according to online learning type were not statistically significant. Third, differences in AI support needs according to academic discipline appeared in asynchronous recorded classes. Science and engineering students showed significantly higher needs for 'content summarization and organization' and significantly lower needs for 'translation/interpretation.' Foreign language students showed relatively higher needs for 'translation/interpretation,' while business and economics students showed specific needs aligned with their disciplinary characteristics. This study has academic significance in extending disciplinary education research to the area of AI-based learning support and empirically confirming disciplinary differences in AI support needs in asynchronous learning environments. Universities should provide discipline-specific AI support functions. Furthermore, instructors must be mindful of assignment design and AI guidance to meet students' current needs while ensuring that AI-enhanced learning transcends simple content summarization.