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Doctoral dissertation engineering a scalable multimodal AI ecosystem designed to process, segment, and interpret heterogeneous classroom variables with a fundamental focus on architectural explainability.
View Thesis →Combined text (BERT) and audio features to classify different types of teacher interventions in classrooms.
View Publication →Systematic review of 82 studies (2014–2024) on using audio processing in educational research.
View Publication →Explored techniques to make multimodal AI models generalize better across different classroom settings.
View Publication →Built a system to automatically analyze classroom recordings and generate insights for teachers.
View Publication →Analyzed how student response systems work in real classrooms using multimodal AI.
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