CourseBlueprint: A Structured Pipeline for Adaptive Pedagogical Video Generation Grounded in Course Corpora

CourseBlueprint introduces a structured pipeline for generating pedagogical videos that encode teaching expertise through typed intermediate representations, prerequisite graphs, and engagement contracts. The system demonstrates that explicit instructional frameworks significantly outperform ad-hoc approaches, with ablation studies showing engagement scores drop from 5.0 to 1.2 when contracts are removed.

CourseBlueprint addresses a critical gap in generative AI for education: while text-to-video systems produce visually polished content, they typically lack pedagogical rigor needed for effective instruction. This research demonstrates that video quality in educational contexts depends less on surface fluency than on explicit, auditable teaching structures built into the generation pipeline.

The system's innovation lies in its architecture rather than novel language models. By introducing typed intermediate representations—scaffolding modules that build prerequisite concept graphs, adaptive controllers that assign style specifications, and engagement generators following fixed narrative contracts (hook→retrieval→core→analogy→forward)—the pipeline makes instructional decisions transparent and testable. This contrasts sharply with contemporary AI systems that rely on chain-of-thought prompting without structural validation.

The benchmarking approach itself carries implications for AI evaluation methodology. The researchers developed both subjective metrics (repeated LLM-judge scoring) and objective ones (regex-grounded measurement), revealing that removing engagement contracts devastates pedagogical outcomes while leaving surface metrics largely intact. This suggests current benchmarks for generative systems may miss crucial dimensions of quality.

For educational technology developers, this work provides a replicable template for grounding AI systems in domain knowledge through formal contracts. The corpus-grounding strategy—reusing instructor slides when confidence is high—demonstrates how hybrid approaches combining generated and real content can achieve higher fidelity than purely synthetic pipelines. As AI increasingly enters educational spaces, this research establishes methodological foundations for ensuring generated instruction maintains cognitive and pedagogical integrity rather than optimizing only for visual coherence.

• →Explicit pedagogical contracts (hook-retrieval-core-analogy-forward) improve engagement scores 4.2x over unstructured generation
• →Typed intermediate representations with validation enable auditable instructional design, moving beyond opaque prompt chaining
• →Hybrid approaches combining generated video with real instructor slides achieve 90% corpus-grounding success versus 0% for purely synthetic alternatives
• →Current AI evaluation metrics may fail to capture pedagogical quality dimensions that matter most for educational applications
• →Domain-grounded AI systems require formal instructional contracts rather than relying on model fluency to encode teaching expertise