  In this work we investigate how children ages 5-12 perceive, understand, and
use generative AI models such as a text-based LLMs ChatGPT and a visual-based
model DALL-E. Generative AI is newly being used widely since chatGPT. Children
are also building mental models of generative AI. Those haven't been studied
before and it is also the case that the children's models are dynamic as they
use the tools, even with just very short usage. Upon surveying and
experimentally observing over 40 children ages 5-12, we found that children
generally have a very positive outlook towards AI and are excited about the
ways AI may benefit and aid them in their everyday lives. In a forced choice,
children robustly associated AI with positive adjectives versus negative ones.
We also categorize what children are querying AI models for and find that
children search for more imaginative things that don't exist when using a
visual-based AI and not when using a text-based one. Our follow-up study
monitored children's responses and feelings towards AI before and after
interacting with GenAI models. We even find that children find AI to be less
scary after interacting with it. We hope that these findings will shine a light
on children's mental models of AI and provide insight for how to design the
best possible tools for children who will inevitably be using AI in their
lifetimes. The motivation of this work is to bridge the gap between
Human-Computer Interaction (HCI) and Psychology in an effort to study the
effects of AI on society. We aim to identify the gaps in humans' mental models
of what AI is and how it works. Previous work has investigated how both adults
and children perceive various kinds of robots, computers, and other
technological concepts. However, there is very little work investigating these
concepts for generative AI models and not simply embodied robots or physical
technology.
