Scheduled: Luan Anh NGUYEN-TRAN

Jury

Abstract

Generative artificial intelligence is increasingly integrated into higher education as an AI-based information system whose outputs can be incorporated into students’ learning tasks. Through prompt-based interaction, generative AI chatbots produce textual responses that students may use to explain concepts, rephrase content, prepare answers for assignments, organize ideas, translate materials, or check their understanding. These outputs are often useful and convincing, yet unreliable: they may be incomplete, inaccurate, generic, poorly aligned with course expectations, or difficult to verify. This raises a central problem for information systems research: how users rely on the outputs of a complex and partly opaque AI system after it has been adopted, when those outputs become part of their actual work practices. This dissertation examines this problem through the evolution of students’ trust in generative AI during their learning practices, focusing on how students perceive its role, regulate their trust, and reshape the boundaries between AI assistance, student work, teacher validation, and pedagogically legitimate knowledge. The empirical study adopts an interpretive qualitative approach and is situated in a microbiology course at a Vietnamese public university, following 61 second-year food technology students across three assignments during one semester. Students were not required to use generative AI, but they reported their use and provided documentation of prompts when they did. The empirical data combines assignment submissions, AI-generated artifacts, responses to an open-ended questionnaire, one teacher interview, assignment grades used as contextual indicators, and three student case vignettes constructed from multiple sources. The findings indicate that students perceived generative AI as both capable and fallible: useful for explanation, translation, organization, reformulation, and assignment preparation, yet sometimes incorrect, incomplete, generic, or poorly aligned with the course. Trust therefore evolved through situated reliance, as students copied, modified, verified, selected, combined, or rejected AI-generated outputs. Three forms of reliance were observed: stable direct reliance, partial reliance, and variable heavy reliance. Partial reliance was dominant, indicating that generative AI had become integrated into the sociotechnical organization of learning without simply replacing course materials, teacher validation, or student judgment. From a boundary theory perspective, trust shaped the extent to which AI-generated outputs could transition from external assistance to assignment preparation, submitted work, and pedagogically legitimate knowledge. This dissertation contributes to Information Systems research by conceptualizing the evolution of trust in generative AI as situated reliance. It explains how recognized fallibility reorganizes reliance through verification, modification, selection, and limitation rather than interrupting use. It also contributes to boundary theory by conceptualizing trust as a mechanism through which AI-generated outputs move between assistance, student work, teacher validation, and pedagogically legitimate knowledge.