# Explanation as Question Answering based on Design Knowledge

arXiv: https://arxiv.org/abs/2112.09616 · HTML: https://mlsanity.com/paper/2112.09616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019)
- [AI-Powered Learning: Making Education Accessible, Affordable, and Achievable](https://mlsanity.com/paper/2006.01908/) (2020)

## Built on by (most cited first, 1 of 1)

- [Explanation as Question Answering based on a Task Model of the Agent's Design](https://mlsanity.com/paper/2206.05030/) (2022)

## Abstract (arXiv)

Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access and use this design knowledge for generating explanations. Many AI agents used in practice, such as intelligent tutoring systems fielded in educational contexts, typically come with a User Guide that explains what the agent does, how it works and how to use the agent. However, few humans actually read the User Guide in detail. Instead, most users seek answers to their questions on demand. In this paper, we describe a question answering agent (AskJill) that uses the User Guide for an interactive learning environment (VERA) to automatically answer questions and thereby explains the domain, functioning, and operation of VERA. We present a preliminary assessment of AskJill in VERA.