Pairing exam syllabus pages with evidence-grounded candidate Q&A, this dataset helps models answer questions about tested content and exam rules.

Help models clarify misconceptions with textbook-based explanations and strengthen specialized concept reasoning.

Turn terminology, concept relationships, and disciplinary context from digital textbooks into training data for specialized knowledge understanding.

Connect GRE passages with external web evidence and information-boundary analysis to help models explain reading context with verifiable support.

The classroom explanation voice content classification dataset is used to improve the classification and analysis capabilities of audio content in the education and training field.

The Restaurant Learning Posture Image Dataset focuses on posture correction research in the field of educational training.

Train and evaluate evidence-grounded SAT responses on rhetorical purpose, logical cohesion, and text organization.

Evaluate fact verification and evidence reasoning with claims from online course captions paired with web evidence.

Train and evaluate systems to find passage evidence for SAT Reading and Writing questions and ground tutoring answers in the text.

Turn vocational digital textbook content into workflow and safety question-answer examples with source excerpts to support domain-specific model training.

Evaluate how well models understand and answer reading-based questions with evidence and semantic annotations.

Specialized Q&A on SAT accommodations and digital accessibility helps models answer candidates' procedural questions more accurately.

Explore specialized practice questions and explanations that help models learn special education teaching and support knowledge.

Connect SAT math questions to traceable concept, formula, and theorem evidence to build grounded solution retrieval workflows.

Train models to answer specialized humanities questions with context grounded in digital textbook sources and traceable evidence.

A high-quality voice dataset focused on children's bedtime stories.

Help models master multi-step physics reasoning with structured quantity extraction and step-by-step solutions for university entrance exam problems.

Evaluate whether RAG systems retrieve complete, applicable evidence for SAT math solutions with step-by-step annotated evidence chains.

Train models to answer common SAT score questions with focused coverage of score components and report interpretation.

Train and evaluate SAT data analysis assistants with focused questions and answers that explain the reasoning behind each result.