Qgen

Qgen is a software framework for automatic question generation, used to create practice items, assessments, and training data. It combines domain templates, natural language generation, and data-driven content selection to produce questions with answer keys and rationales. While the name is used by several projects, there is no single canonical implementation, and features vary by provider.

Core components include a templating system for question shells, a realization engine that converts templates and structured data into natural language, a data ingestor to pull content from textbooks, corpora, or knowledge bases, and an evaluation module to estimate difficulty and ensure quality. Most implementations expose a command-line interface and an API for integration with learning management systems or research pipelines.

Typical workflows involve selecting a domain and difficulty, providing a corpus or knowledge graph, configuring constraints (question type, length, language), and generating items in formats such as JSON, CSV, QTI, or Moodle import. Reproducibility is supported by seed control and versioned templates, and some projects offer human-in-the-loop review.

Applications span educational publishing, classroom instruction, and research. Qgen-generated item banks are used to assemble practice sets, exams, adaptive quizzes, and benchmark datasets for question-answering systems. Limitations include reliance on template quality and data provenance, potential biases in source content, and the need for post-generation validation.

See also: Question generation, Natural language generation, Assessment data generation.