# Did Chatbots Miss Their 'Apollo Moment'? A Survey of the Potential, Gaps and Lessons from Using Collaboration Assistants During COVID-19

arXiv: https://arxiv.org/abs/2103.05561 · HTML: https://mlsanity.com/paper/2103.05561/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Recipes for Safety in Open-domain Chatbots](https://mlsanity.com/paper/2010.07079/) (2020)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Ethical Challenges in Data-Driven Dialogue Systems](https://mlsanity.com/paper/1711.09050/) (2017)
- [Overview of the Ninth Dialog System Technology Challenge: DSTC9](https://mlsanity.com/paper/2011.06486/) (2020)
- [Planning for Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1910.08137/) (2019)
- [Mapping the Landscape of Artificial Intelligence Applications against COVID-19](https://mlsanity.com/paper/2003.11336/) (2020)
- [Generating Dialogue Agents via Automated Planning](https://mlsanity.com/paper/1902.00771/) (2019)
- [An Automated Testing Framework for Conversational Agents](https://mlsanity.com/paper/1902.06193/) (2019)
- [Decision-support for the Masses by Enabling Conversations with Open Data](https://mlsanity.com/paper/1809.06723/) (2018)

## Abstract (arXiv)

Artificial Intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people. In this survey paper, we look at how AI in general, and collaboration assistants (CAs or chatbots for short) in particular, have been used during a true global exigency - the COVID-19 pandemic. The key observation is that chatbots missed their "Apollo moment" when they could have really provided contextual, personalized, reliable decision support at scale that the state-of-the-art makes possible. We review the existing capabilities that are feasible and methods, identify the potential that chatbots could have met, the use-cases they were deployed on, the challenges they faced and gaps that persisted, and draw lessons that, if implemented, would make them more relevant in future health emergencies.