Whose French does the Machine Speak? Generative AI, Linguistic Norm, and Francophone Plurality in the Beginning Language Classroom

Generative artificial intelligence is increasingly reshaping beginning French instruction by expanding access to conversational practice, corrective feedback, vocabulary development, translation, and individualized support. Yet the linguistic forms produced by large language models also raise a fundamental sociolinguistic question: which varieties of French are rendered visible, legitimate, and teachable through machine-generated language? This review examines the relationship between generative AI, standard-language ideology, and Francophone plurality in introductory French education. Drawing on scholarship from applied linguistics, sociolinguistics, artificial intelligence in education, critical digital literacy, and Francophone studies, it analyzes training-data representation, algorithmic preferences for standardized forms, regional and African varieties, diasporic French, learner exposure, corrective feedback, representational bias, and emerging approaches to pluralistic AI literacy. The review finds that generative systems frequently operate within data, benchmarking, and alignment environments that privilege standardized, high-resource and often metropolitan forms of French. Consequently, African, Canadian, Caribbean, diasporic, colloquial, and contact-influenced varieties may receive less consistent representation, while novice learners may interpret fluent machine output as neutral linguistic authority. At the same time, generative AI offers important pedagogical benefits through repeated practice, rapid feedback, accessible interaction, and personalized language support. The review concludes that AI should function as a critically mediated pedagogical resource rather than an autonomous arbiter of correctness. It recommends deliberate exposure to diverse Francophone voices, explicit distinction between grammatical error and legitimate variation, systematic verification of AI outputs, teacher professional development, and the integration of critical AI literacy from the earliest stages of instruction. Future research should prioritize longitudinal, comparative, corpus-based, and geographically diverse studies that evaluate both learning effectiveness and representational equity.