A QUALITATIVE EXPLORATION OF STUDENTS USE OF HUMATA.AI TO UNDERSTAND THESIS LITERATURE: A COGNITIVE LOAD PERSPECTIVE

This qualitative study explores how Humata.ai, an AI summarization tool, helps final-year English Education students understand thesis literature from a Cognitive Load Theory (CLT) perspective. The study involved 10 final-year students from a university in Surabaya who used Humata.ai in their literature review process. Data were collected through semi-structured interviews and analyzed thematically using a theory-based approach. Findings indicate that Humata.ai helps alleviate extraneous cognitive load by simplifying complex academic texts, filtering out irrelevant information, and improving time efficiency. It also supports extraneous cognitive load by fostering critical thinking and encouraging strategic reading. However, students reported challenges such as technical issues, over-reliance, and occasional inaccuracies in summaries. These results suggest that while Humata.ai can serve as an aid in understanding thesis literature, its use must be supported by ethical and strategic use to maintain students' analytical skills. This study contributes to the growing discussion on AI-assisted learning and cognitive load management in higher education and recommends further research across disciplines and tools to explore the broader implications of AI pedagogy.