The Effects of Trust and Cognitive Load on Human-Generative AI Communication in Higher Education: Evidence from Indonesian Undergraduate Students
DOI:
https://doi.org/10.18326/inject.v11i1.6235Keywords:
Generative Artificial Intelligence, Human-Machine Communication, Human-AI Communication, Trust, Cognitive LoadAbstract
This study examines the influence of trust and cognitive load as fundamental factors associated with Human-Generative AI Communication in a higher education context. As student interactions with generative artificial intelligence systems through natural language increase, understanding the conditions that support effective and meaningful communication between humans and AI becomes crucial. Using a quantitative research design, survey data were collected from 400 Indonesian undergraduate students who had actively used a generative AI platform for academic purposes in the past three months. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the theoretically hypothesized influence of trust and cognitive load on Human-Generative AI Communication within a theory-driven modeling framework. Results indicate that trust has a strong and significant influence. Students who perceive AI systems as reliable and credible are more likely to engage in open, dialogic communication. Cognitive load also showed a significant influence, indicating that lower cognitive load facilitates clearer, more efficient, and more dialogic communication with AI systems. Furthermore, the combined effects of trust and cognitive load accounted for a significant portion of the variance in Human-AI Generative Communication, underscoring the role of both factors in shaping interaction quality in academic settings. The findings indicate that students' communication with generative AI is closely related to relational evaluations and perceived cognitive demands, supporting the view of generative AI as a communicative participant and not simply an instrumental tool. This study contributes to communication research by providing empirical evidence on the socio-cognitive underpinnings of Human-Generative AI communication in higher education.
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