Young Learners' Motivation for AI ‐Supported Extracurricular French Vocabulary Learning: A Q Methodological Study

ABSTRACT Artificial intelligence (AI) is increasingly reshaping extracurricular language learning, yet little is known about how young learners construct motivation when using AI to learn less commonly taught foreign languages. This study investigates primary school students' motivational typologies and the factors related to these configurations in AI‐supported extracurricular French vocabulary learning. Twenty learners completed a Q sorting task based on 34 motivational statements, and eight participants were subsequently selected for semi‐structured interviews. Q factor analysis identified four motivational typologies: socially supported learners, future‐oriented learners, enjoyment‐driven learners and mastery‐oriented learners. Thematic analysis further revealed four cross‐cutting factors related to these motivational configurations: human support, AI‐supported autonomy, interactive enjoyment and competence feedback. The findings show that the motivational value of AI is not uniform but depends on how young learners connect technological affordances with interpersonal relationships, personal agency, enjoyment, future goals and perceived progress. By integrating Q methodology with Self‐Determination Theory, this study advances a configurational understanding of young learners' motivation and shows that the same AI‐supported learning opportunities can acquire different motivational meanings depending on learners' social and psychological configurations.