<![CDATA[Background. Personalized learning systems are expanding rapidly in higher education, but many recommendation pipelines still depend on structured indicators such as grades, attendance, task completion, and clickstream activity. In transnational educational settings, those indicators cannot adequately explain how students interpret disciplinary language, negotiate cultural expectations, or express learning difficulties. Purpose. This study develops a narrative-informed framework for personalized learning recommendations by integrating structured academic data and student-authored digital narratives within a multimodal learning analytics perspective. Method. The manuscript is positioned as a design science and framework-development study rather than a completed quantitative experiment. It synthesizes recent literature on learning analytics, educational recommender systems, multilingual education, natural language processing, and human-centred AI in education to specify a technical workflow for narrative preprocessing, multimodal fusion, learner-state modelling, recommendation generation, and evaluation. The small learner records presented in tables are synthetic examples used only to illustrate the data architecture. Results. The main output is a technically explicit and theoretically grounded framework that explains how narrative text can be anonymized, segmented, normalized, encoded into multilingual embeddings, combined with numerical learner indicators through early and late fusion, and evaluated using both predictive and recommender metrics. The framework also operationalizes transnational variables, including language diversity, culturally indirect participation, and contextual adaptation needs. Conclusion. Digital narratives can enrich learner profiling, improve contextual sensitivity, and strengthen culturally responsive recommendations. The study contributes a coherent blueprint for future empirical implementation in multilingual and transnational learning environments]]>
