Mitigating generative AI hallucinations in geographical education

Large language models (LLMs) present opportunities and challenges for geographical education, with AI hallucinations—plausible but factually incorrect information—posing significant risks to spatial accuracy and knowledge integrity. This paper addresses the critical gap in practical guidance for geography educators through an integrative literature review examining misunderstandings about LLMs, types of geographic hallucinations, and evidence-based mitigation strategies. The review identifies six distinct forms of geographic hallucinations: terminological misinterpretation, numeric erratum, factual mirage, spatial and attribute hallucinations, temporal hallucinations, and fabricated academic sources—each with unique implications for teaching and learning. To mitigate these challenges, the framework outlined in this paper promotes the development of critical geographic media literacy, implementing geography-specific verification approaches, employing structured prompting techniques, building AI literacy within the geography curriculum, establishing ethical frameworks, designing AI-aware assignments, and fostering collaborative verification communities. These approaches extend Lane (2025) framework for AI integration in geographical education, transforming potential threats into opportunities for deeper disciplinary engagement. While acknowledging limitations in current understanding, this paper equips geography educators with practical tools to harness AI’s benefits whilst managing its risks, fostering a generation of critical, ethically informed producers and consumers of geographic knowledge.