Hello. My name is Duri Long and I am a human-centered computing PhD student working in the Expressive
Machinery Lab at Georgia Tech. My advisor and co-author is Dr. Brian Magerko. Today
I am going to be talking with you about our paper which is entitled What is AI Literacy?
Competencies and Design Considerations.
Artificial Intelligence is becoming increasingly integrated in user-facing technologies in
our homes, workplaces, and schools.
But there are still a lot of existing questions and misconceptions about what AI is, how it
works, and how to use it. These types of misconceptions can limit people’s ability to effectively
use, collaborate with, and act as critical consumers of AI technologies.
There is a need for additional research investigating what new competencies and design considerations
will be necessary in a future in which AI transforms the way that
we communicate, work, and live with each other and with machines. In other words, there is
a need for improved public AI Literacy and a better understanding of how to design technologies
and learning experiences that can promote AI literacy.
The term AI literacy is used loosely in a few papers, but has not been clearly defined
in the literature as of yet.
We present a working definition of AI literacy as a starting point for research in this area.
We define AI literacy as a set of competencies that enables individuals to critically evaluate
AI technologies; communicate and collaborate effectively with AI; and use AI as a tool
online, at home, and in the workplace.
We see AI literacy as being related to but distinct from other previously defined literacies,
such as scientific literacy, data literacy, digital literacy, and computational literacy.
While we see these all as being related, we see scientific literacy and computational
literacy as being able to inform AI literacy, so maybe being helpful for AI literacy but
they are not necessary prerequisites. So for example, individuals can learn key concepts
about AI without necessarily needing to know how to program. Digital literacy, on the other
hand, is required for AI literacy, as you need to have a familiarity with computers
and technologies to be able to understand AI. And we see data literacy as having a significant
overlap with AI literacy, particularly in relationship to understanding
the AI subfield of machine learning. All of these literacies have emancipatory consequences,
potentially broadening access to knowledge and the ability for people to share and communicate
ideas.
But what are the competencies that make up AI literacy? And how can we design technologies
and learning experiences to promote those competencies? These are the primary research
questions that were driving our research in this paper.
In the past few years, there has been some initial research into similar question such
as how to teach AI to K-12 students. The working group “AI4K12”
has developed a set of five “big ideas” of AI that can provide an initial basis for
exploring AI education for learners who don't have technical backgrounds - for example the
idea that computers learn from data or the idea that computers perceive the world using
sensors. This group is working to develop a set of
standards for K-12 education. In addition, there have been books published on practical
strategies for teaching AI in K-12 classrooms such as Michelle Zimmerman's Teaching AI,
and researchers at MIT have developed an AI ethics curriculum for middle school students.
Several industry partners have also been working on developing
AI curricula, including the companies ReadyAI and AI4All.
There is also some existing research that has focused on designing tools and technologies
that enable individuals without technical backgrounds to learn about AI. There are interactive
web demonstrations that offer insight into AI’s reasoning processes, such as Google’s
QuickDraw program, which allows individuals to inspect how a neural network interprets
human-drawn doodles. There are a variety of toolkits that allow individuals to “tinker”
or learn by interacting with and programming commercially available AI devices such as
Cozmo or Alexa--for example, Cognimates, Scratch Nodes ML, and Calypso for Cozmo.
All of this research provides a great foundation for understanding AI literacy and how to communicate
it to non-technical audiences. However, most of this research is really
recent - from the past two or three years. In contrast, AI as a field has been active
since the 1950s, and there is a variety of existing research (scattered across disciplines
and venues) that could potentially contribute to understanding what competencies should
be included in a definition of AI literacy and how to better design educational experiences
that foster AI literacy.
Towards this end, we engaged in an exploratory review of literature with the goal of distilling
ideas from various fields that could inform our understanding of how learners
make sense of artificial intelligence. Due to the limited amount of existing peer-reviewed
literature on AI education and the variety of research in related fields such as CS education
or human-computer interaction that can inform AI education, we did not conduct a
traditional systematic literature review. Our methods were instead more closely aligned
with an approach called scoping studies, which aim to map rapidly the key concepts underpinning
a research area and the main sources and types of evidence
available...especially where an area is complex or hasn't been reviewed comprehensively before.
Our review was guided by two key questions: 1) What do AI experts think that non-technical
learners should know about AI? and 2) What existing perceptions
and misconceptions do non-technical learners have when interacting with AI?
The literature we reviewed in response to these two questions included 150 documents
on topics including explainable AI, data literacy, AI education efforts in K-12 and university
environments, human-robot interaction, AI ethics,
and public perceptions of AI. These documents spanned over 20 years of research and ranged
from peer-reviewed conference and journal papers to grey literature such as public polls,
classroom syllabi, and textbooks. More details on the methodology of the literature
review can be found in the paper.
We distilled a list of key ideas presented in the literature and grouped them based on
similarity. We distilled competencies and design principles from this list by considering
whether the ideas reflected our definition of AI literacy, were supported by multiple
sources, and whether the ideas were a useful guideline for designers and educators.
We then sorted the design considerations and competencies that we derived into thematic
groups, forming the conceptual framework that you see on the slide here. This chart shows
all of the competencies and design considerations that we distilled from the literature, grouped
according to five themes - what is AI, what can AI do, how does AI work, what should AI
do, and how do people perceive AI?
In the following slides, I am going to present a few competencies and design considerations
from each of these themes, but due to time I cannot cover them all in this presentation.
Please refer back to this slide for a full list of the competencies and design considerations
and to the paper for more detailed descriptions of each competency and design consideration
and how they were derived from the literature, including supporting references for each one.
So first I'm going to start by reviewing a few competencies from each of these thematic
categories.
Within the theme “What is AI?” one of the competencies we identified is that AI
literate learners should be able to recognize that AI is interdisciplinary and that there
are many different ways to develop “intelligent”
machines. Learners should be able to identify a variety of technologies that use AI, including
technology spanning the three areas of cognitive science, robotics, and
machine learning, which are the subfields of AI.
Within the second theme, the space of “What can AI do?”, we suggest that learners should
be able to identify problem domains that AI currently excels at and domains that are currently
more challenging for AI, in order to determine when to leverage human vs. AI skills. For
example, AI is really good at recognizing patterns in large amounts of data, but it
can have issues and struggle with sort of encoding bias into its analyses. So its important
for learners to be able to distinguish those strengths and weaknesses.
We divided the theme “How does AI work?” (which is a really big question) into competencies
relating to cognitive systems, machine learning, and robotics - again, the three main subfields
of AI. One of the competencies that we identified for cognitive systems specifically was the
ability to understand what a knowledge representation is and describe some examples of knowledge
representations. This could potentially aid learners in distinguishing between how humans
represent knowledge and how computers represent knowledge.
Within the space of machine learning, one of the competencies we identified was that
it is important to recognize that humans play a role in programming,
choosing models, and fine tuning AI systems.
And finally, within robotics, one of the competencies that we identified was the understanding that
AI systems are able to physically act on the world. And that this action can be directed
by higher-level reasoning, such as walking along a planned path, or it can be reactive,
such as jumping backwards to avoid a sensed obstacle.
Within the space of “How should we use AI", the next sort of thematic category, we suggest
that learners should be able to identify and describe different perspectives
on the key ethical issues surrounding AI, which we outline in more detail in the paper.
Some of these ethical issues might include privacy, accountability, bias, or fairness.
Finally, within the space of how people perceive AI, we suggest that learners should understand
that agents are programmable, a concept that younger learners especially
may struggle with as they tend to personify AI technologies, and attribute more intelligence
to them than they actually have.
Next, I want to highlight a few of the design considerations that we identified in the literature.
One of the design considerations we identified within the “What is AI?” theme is to consider
including graphical visualizations, simulations, explanations of agent decision-making processes,
or interactive demonstrations in order to aid in learners’ understanding of AI.
Several existing design and education initiatives have explored embodied interactions as a way
of helping learners to make sense of agents’ reasoning processes. Designing to facilitate
embodied interaction may involve embodied simulations of algorithms or hands-on physical
experimentation with AI technology.
Within the space of how do people perceive AI, providing opportunities for individuals
to program or teach AI can also aid learners in understanding how it works and recognizing
the role that humans play in AI development. However, when providing these opportunities,
its important to keep coding skill prerequisites to a minimum. There
are a number of strategies that have been explored in the CS education space that can
aid in minimizing programming skills necessary to program or teach AI, therefore thereby
sort of lowering the barrier of entry for individuals and broadening access to AI education
to individuals who may not have the experience or know how to program.
Finally, leveraging learners interests, such as current issues, everyday experiences, or
common pastimes like games or music can help to engage learners who may not otherwise be
interested in or feel included in AI. It can also help in highlighting the relevance of
AI to everyday issues and concerns.
So to summarize, we have synthesized a wide range of interdisciplinary literature in order
to identify a set of competencies that clearly define AI literacy, a term that currently
lacks a cohesive definition in the literature.
We have additionally identified a set of design considerations that can inspire and guide
the design and evaluation of future AI literacy learning interventions and learner-centered
AI technology.
Finally, we hope that this framework is the beginning of a conversation surrounding AI
literacy. We intend for this framework to serve as a jumping off point for future empirical
and design research, and we anticipate the need for modifications or expansions of this
framework to accommodate new findings, technologies, and rapidly changing social norms.
Thank you so much for your time, and if you have any questions, feel free to reach out
to me.