Generative AI as a Cognitive Co-Participant: Disciplinary Modulation of EFL Academic Reading Load and Motivation - Wang, Yingqi; Luo, Xiaohang (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
As generative AI rapidly enters higher education, its cognitive, motivational, and social impacts across disciplines remain underexplored. This qualitative study examines disciplinary epistemologies and digital literacy on AI-assisted academic reading among EFL Chinese students. Guided by Cognitive Load Theory and Self-Determination Theory, participants were 46 university students across Biglan’s disciplinary dimensions. We analyzed 46 questionnaires and 32 interviews. Students in soft and applied fields more often report AI reducing intrinsic load, supporting deeper semantic elaboration. In pure and hard fields, students tend to use AI as an interactive tool for questioning, but multi-contextual examples are more likely to introduce extraneous load. By contrast, terminology glossing and decomposition of complex sentences are more often applied in soft and applied fields. Excessive reliance is associated with cognitive offloading and an illusory sense of mastery, shaped by digital literacy and metacognitive awareness. Socially, AI sometimes displaces routine exchanges, but when integrated into group contexts, it facilitates collaboration. The study elaborates applications of CLT and SDT by showing how disciplinary and individual factors shape AI’s cognitive and motivational roles. Practically, it proposes discipline-sensitive design principles and metacognitive prompts, pointing to deployable interventions. Ethical approval and consent were obtained.