Automating question generation from educational text
Ayan Kumar Bhowmick Ashish Jagmohan Aditya Vempaty Prasenjit Dey Leigh Hall Jeremy Hartman Ravi Kokku Hema Maheshwari
The use of question-based activities (QBAs) is wide-spread in education,
traditionally forming an integral part of the learning and assessment process.
In this paper, we design and evaluate an automated question generation tool for
formative and summative assessment in schools. We present an expert survey of
one hundred and four teachers, demonstrating the need for automated generation
of QBAs, as a tool that can significantly reduce the workload of teachers and
facilitate personalized learning experiences. Leveraging the recent
advancements in generative AI, we then present a modular framework employing
transformer based language models for automatic generation of multiple-choice
questions (MCQs) from textual content. The presented solution, with distinct
modules for question generation, correct answer prediction, and distractor
formulation, enables us to evaluate different language models and generation
techniques. Finally, we perform an extensive quantitative and qualitative
evaluation, demonstrating trade-offs in the use of different techniques and
models.