Pedagogical Assessment Prover Connector for AI agents.

It ensures your goals are measurable and your activities actually match what you want students to learn. It catches mistakes where the test is too easy or too hard for the stated goal.

Yes, it forces the AI to create behaviorally anchored descriptors. This means every grader knows exactly what constitutes 'exemplary' work, leading to more consistent and fair grading.

It ensures your feedback follows the Hattie model, providing specific 'Feed Up, Back, and Forward' guidance. This tells students where they are going, how they are doing, and what to do next.

It performs a structured audit of your questions to find cultural, linguistic, or accessibility issues. It ensures your content is inclusive and doesn't favor one group over another.

It's grounded in Bloom's Taxonomy, Vygotsky's Zone of Proximal Development, Hattie's research on Visible Learning, and Universal Design for Learning (UDL).

It works for both. Whether you're designing a high school science unit or a corporate compliance workshop, the science of learning remains the same.

By verifying that learning objectives use observable verbs at the same cognitive level as the assessment tasks. It rejects unmeasurable verbs like 'understand' or 'appreciate'.

It demands a clear plan for diagnosing prior knowledge, sequencing prerequisite concepts, and scaffolded instruction models (like the Graduated Release of Responsibility) rather than just giving extra practice sheets.

It scans assessment descriptions and rubrics for cultural assumptions, language barriers, and accessibility issues, ensuring compliance with Universal Design for Learning (UDL) principles.