ReQUESTA is a multi-agent workflow that turns long academic texts into high-quality, cognitively diverse multiple-choice questions. Instead of producing easy recall items, ReQUESTA plans around the source content's key concepts and inferences, then crafts text-based, inferential, and main-idea questions that demand interpretation and synthesis. By ensuring plausible distractors and consistent quality checks, it yields questions that actually measure understanding.
For educators, this means faster creation of valid assessments and richer practice items that reinforce what matters. For learning systems and students, it powers self-assessment that builds metacognition: learners test themselves on central arguments and relationships, get meaningful feedback, and close gaps more effectively. In short, ReQUESTA turns long texts into targeted, high-quality multiple-choice questions that enhance teaching and deepen learning.
Key guidelines for writing effective multiple-choice test items based primarily on Haladyna, Downing, and Rodriguez’s 2002 review.
Alpha Tools are early versions built on strong research foundations, but are still being tested and reviewed for accuracy and user experience. Using Alpha tools helps identify areas for improvements and move them toward beta release.
Beta Tools have been evaluated rigorously, refined through user feedback, and are ready for broader use.