Project Description

The goal of this research is to leverage automatic question-answer generation to develop an integrated system where teachers can collaborate with AI to create and customize interactive reading resources with question-answering functions for their students from kindergarten through second grade. Throughout this project, students will have a chance to 1) develop innovative AI models that expand the state-of-the-art NLP techniques (e.g., BERT, GPT) for automatically generating question-answer pairs for reading materials and customizing the training to meet the unique requirements of an educational context; and 2) building a dialog system with graphical user interfaces that is able to a) ask children a question, b) provide tailored feedback and explanation to children’s response, and/or c) rephrase the original question (usually open-ended) to a multiple-choice question as a way of scaffolding if the children do not answer the original question or answer it incorrectly.

Team Members

  • Liliana Nguyen
  • Hugo Lin
  • Wesley Truong
  • Edwin Yee
  • Shamita Gurusu

Professor and Mentors

  • Prof. Shiyu Chang
  • Grad mentor: Yujian Liu and Jiabao Ji

Meeting Time

  • Meeting with the Professor
    • TBD
  • Meeting with Grad mentor
    • TBD
  • ERSP meeting with central mentors
    • Chinmay: TBD
    • Diba: TBD
  • ERSP team meeting
    • Fridays 5-7p

Links to Proposals and Presentation

  • Instructor feedback on initial draft of proposal: link
  • Final Proposal (after instructor's feedback): link
  • Final presentation: link

Individual Logs

Peer Review

Project Documentation and Resource