<![CDATA[Indigenous students across diverse regions continue to experience inequitable learning outcomes due to digital learning platforms that insufficiently reflect their cultural identities, local wisdom, and mother tongues. Conventional EdTech systems often adopt universalized content structures that overlook indigenous epistemologies, resulting in decreased motivation, limited comprehension, and reduced learning engagement. These challenges highlight the need for culturally-responsive digital learning models capable of integrating local narratives, linguistic heritage, and community-based knowledge systems into technology-supported education. This study aims to develop and analyze a culturally-responsive learning technology framework that embeds local wisdom and mother tongues within digital learning platforms to enhance accessibility, inclusivity, and meaningful learning for indigenous students. The research employs a mixed-methods design involving qualitative ethnographic documentation of indigenous cultural practices and quantitative usability testing of a prototype platform. Data are collected from indigenous teachers, students, and community leaders through interviews, observations, and structured questionnaires. Findings indicate that integrating culturally grounded content—such as traditional stories, regional ecological knowledge, and local vocabulary—significantly improves learning relevance, cognitive connection, and emotional engagement. Quantitative analysis shows measurable gains in comprehension and task completion accuracy when learning materials incorporate mother-tongue scaffolding and culturally familiar representations. The study concludes that culturally-responsive learning technology fosters deeper identity affirmation, strengthens intergenerational knowledge transmission, and reduces digital learning barriers among indigenous students. The incorporation of local wisdom and mother tongues not only enhances academic outcomes but also promotes cultural sustainability within digital ecosystems. Future implementations should prioritize community co-design, adaptive language features, and scalable platform architectures to ensure long-term impact and cross-regional applicability.]]>
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