Teaching Students How to Effectively Interact with LLMs at University: Insights on the Longitudinal Development and Plasticity of Locus of Control

Background: As AI disrupts education systems worldwide, there is an urgent need for students to develop AI literacy while maintaining human agency. While locus of control (LOC), defined as the belief that outcomes are contingent on one's own behavior versus external forces, is traditionally viewed as a stable trait, its plasticity in response to AI experiences remains unexplored. Objectives: This study examined how explicit instruction and guided practice with multiple frontier Large Language Models (LLMs) impact students' knowledge, attitudes, self-efficacy, and sense of control over AI-mediated outcomes. Methods: A longitudinal qualitative case study followed 12 undergraduate students through a semester-long experimental AI unit. Data collected through surveys, focus group discussions, and weekly observations were analysed thematically, using LOC theory as an interpretive framework. Results and Conclusions: Students demonstrated a clear progression toward internal AI-LOC, developing critical awareness and agency in their AI interactions. The findings revealed that sustained, scaffolded AI instruction enabled students to view themselves, rather than the technology, as the primary driver of outcomes. This transformation suggests that structured AI literacy education can empower learners to maintain agency in their AI use.