STREAM: A Semantic Transformation and Real-Time Educational Adaptation Multimodal Framework in Personalized Virtual Classrooms

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Abstract
Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational Adaptation Multimodal) framework. This modular pipeline decomposes multimodal educational content into semantically tagged, pedagogically annotated units for regeneration into alternative formats while preserving source traceability. STREAM integrates automatic speech recognition, transformer-based natural language processing, and planned computer vision components to extract instructional elements from teacher explanations, slides, and embedded media. Each unit receives metadata including timecodes, instructional type, cognitive demand, and prerequisite concepts, enabling format-specific regeneration with explicit provenance links. We report results from a tightly scoped feasibility pilot processing a single five-minute elementary STEM video offline under clean audio-visual conditions. For a predefined visual-learner profile, the system generates annotated path diagrams, two-panel instructional guides, and entity pictograms with complete back-link coverage. Ablation studies confirm individual components contribute measurably to output completeness without compromising traceability. This narrow scope precludes claims about classroom effectiveness, real-time streaming, scalability across content types, or educational impact across learner populations. We position these as testable hypotheses requiring validation across diverse content domains, authentic deployments with ambient noise and bandwidth constraints, multiple learner profiles including multilingual students and learners with disabilities, and controlled comprehension studies. The contribution is a transparent technical demonstration and methodological scaffold for investigating whether within-lesson content transformation can meaningfully support personalized learning at scale.

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