The Impact of Hallucinated Information in Large Language Models on Student Learning Outcomes: A Critical Examination of Misinformation Risks in AI-Assisted Education

Abstract

Large Language Models rely on extensive training corpora and sophisticated neural architectures to generate linguistic output that can exhibit coherent reasoning. Educational institutions increasingly adopt these systems to supplement instructional content and automate routine tasks. Students who interact with AI-generated material face exposure to text that may include factual inaccuracies, misleading statements, or hallucinated sources. Researchers document instances where these models create spurious references or invent data that can degrade learners’ conceptual frameworks. Administrators who depend on AI outputs risk introducing unvetted material into digital classrooms, thus creating a latent hazard for propagating incorrect information on scientific, historical, or procedural topics. Scholars argue that the subtle nature of such errors complicates detection, since plausible stylistic features can obscure underlying falsities. Hallucinated information can erode trust in validated knowledge, undermine the development of critical thinking skills, and impede the accurate formation of disciplinary expertise. Teachers who rely on unverified AI-generated content may inadvertently endorse erroneous claims, thereby complicating their attempts to cultivate reliable understanding among students. This research paper scrutinizes how hallucinated information in AI systems circulates within academic environments and analyzes its consequences for pedagogical objectives. Robust examination of these dynamics addresses the multifaceted risks posed by Large Language Models to the integrity of student learning outcomes.