Trust, encompassing cognitive and emotional dimensions, is foundational for the adoption and sustained use of technology. As generative AI (GenAI) tools reshape information-seeking behaviours in higher education, understanding students' trust in these systems is crucial. This study explores models of trust in GenAI, drawing from Jacovi et al.’s (2021) contractual trust framework and others. These models highlight trust as a dynamic construct shaped by user expectations, satisfaction, risk perception, competence, and benevolence.

Through the lens of calibrated trust, we examine how students form trust judgments when using GenAI for academic research. Using a scenario-based survey and a validated trust measurement instrument, we assessed students' perceptions of two AI chatbots. The research aims to:

1. Assess the extent to which students trust or distrust GenAI in academic research.
2. Identify key factors that influence trust perceptions, including transparency and reliability.

Findings from this study will provide insights into how students calibrate trust in AI-based search tools, informing AI system design to foster appropriate trust levels. Additionally, results will help educators and administrators guide students in effectively integrating GenAI into academic research practices.