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2025-11-13
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Abstract
Artificial intelligence, AI, specifically the 'intelligent' chatbots exemplified by chapGPT accessing the raw power of Large Language Models, the underlying GenAI technology, such as OpenAI, offers a variety of ways to support teachers. This raw power can however be problematic. Large Language Models can 'hallucinate' , providing plausible but fictitious information, furthermore, they require considerable skill in drafting queries that give the precise answer required, the so-called engineering of 'prompts' , and lastly, they could provide responses that are dangerous, hurtful or harmful. These are all consequences of the underlying technology, which indiscriminately harvests and recycles the world's digital resources, good or bad, right or wrong, nice or nasty. The educational use of GenAI in rural areas offers possibilities and poses problems, some of which are already implicit in existing rural digital educational provision, objective factors like sparsity, infrastructure and distance, and to cultural factors like the dominance of urban mindsets and understandings. Others are a direct consequence of the nature of GenAI itself. Tools that manage this power and deliver convenient and safe services to educational users can mitigate or eliminate these problems. Teachermatic is one such tool and is critiqued in terms of its rural educational relevance. This introductory and exploratory paper outlines the underlying technical, pedagogic, cultural and ethical challenges of educational AI in rural contexts and reports briefly on trials and workshops with teachers. There is no quick fix or easy answer. The problems of education in rural areas are not obviously or simply ones that GenAI can fix, in fact without supportive policy and resources to focus the direction and deployment of GenAI, it might merely reinforce existing barriers and inequalities.
Citation
Traxler, J, 2025. Learning, Rural Areas and GenAI Tools. Forum for Linguistic Studies, 7(12): pp. 1019–1029. DOI: https://doi.org/10.30564/fls.v7i12.9637
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Journal article
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en
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© 2025 The Authors, published by Bilingual Publishing Group. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.30564/fls.v7i12.9637
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2705-0610
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2705-0602