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Envisioning AI for K-12: What Should Every Child Know about AI?

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Abstract

The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
Envisioning AI for K-12: What should every child know about AI?
David Touretzky1, Christina Gardner-McCune2, Fred Martin3, Deborah Seehorn4
1Carnegie Mellon University, Pittsburgh, PA 15213
2University of Florida, Gainesville, FL, 32611
3University of Massachusetts Lowell, Lowell, MA, 01854
4CSTA Curriculum Committee, Cary, NC, 27513
dst@cs.cmu.edu; gmccune@ufl.edu; fred_martin@uml.edu; deborah.seehorn@outlook.com
Abstract
The ubiquity of AI in society means the time is ripe to
consider what educated 21st century digital citizens should
know about this subject. In May 2018, the Association for the
Advancement of Artificial Intelligence (AAAI) and the
Computer Science Teachers Association (CSTA) formed a
joint working group to develop national guidelines for
teaching AI to K-12 students. Inspired by CSTA's national
standards for K-12 computing education, the AI for K-12
guidelines will define what students in each grade band
should know about artificial intelligence, machine learning,
and robotics. The AI for K-12 working group is also creating
an online resource directory where teachers can find AI-
related videos, demos, software, and activity descriptions
they can incorporate into their lesson plans. This blue sky talk
invites the AI research community to reflect on the big ideas
in AI that every K-12 student should know, and how we
should communicate with the public about advances in AI
and their future impact on society. It is a call to action for
more AI researchers to become AI educators, creating
resources that help teachers and students understand our
work.
Introduction1
Advances in AI have led to both beneficial and unintended
negative effects, which have not escaped our research
publications or mainstream media. For example: machine
learning has greatly improved pattern recognition for
medical diagnosis, but bias in a training set can lead to
fairness issues in evaluating loan applications or problems
with non-Caucasian facial recognition. These stories drive
the imaginations and the fears of the general public. Despite
the increased frequency of AI headlines in the media, there
is a persistent lack of understanding of AI (West & Allen,
1Copyright © 2019, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
2018). We must consider the role we play as AI researchers
and educators in helping people understand the science
behind our research, its limits, and its potential societal
impacts. On the graduate level, courses have been keeping
pace with advances in the field. In undergraduate education,
we’ve sought to provide fun and inspiring ways to engage
students in various aspects of AI, such as robotics, modeling
and simulation, game playing, and machine learning
(Dodds, Hirsh, and Wagstaff, 2018). In addition, we’ve
sought to write textbooks and design tools, nifty and model
AI assignments, and curricula that help students learn and
apply the fundamentals of AI. More recently, companies
have been making AI API’s such as IBM Watson, Google
Web Speech, DialogFlow, and Azure Cognitive Services
publicly available to educators and students.
Our hope for undergraduates is to prepare them for a
bright future in the computing industry, or spur them on to
graduate studies. But it is just as important now to think
about what AI education should look like in K-12, not only
to ensure a more informed populace that understands the
technologies they interact with every day, but also to inspire
the next generation of AI researchers and software
developers. For many in this generation, AI will be an oft-
overlooked, magical force that powers their lives much as
electricity, the internal combustion engine, and networking
technology power ours.
Background
Over the past five years there has been a rapid expansion of
CS education into formal K-12 curricula for all students,
both nationally and internationally. In the United States this
work has been undertaken by the National Science
Foundation, Code.org, Google, Microsoft, the Computer
Science Teachers Association (CSTA), the Association for
Computing Machinery’s (ACM) Special Interest Group for
Computer Science Education (SIGCSE), CS for All, and
other organizations that aim to ensure that all students learn
computer science and that it is taught regularly throughout
their K-12 years. Standardization of what K-12 students
should know about computer science has been supported by
the development and implementation of the AP Computer
Science Principles curriculum (College Board, 2017), the
CS K-12 Framework (2016), the CSTA Standards for K-12
Computing Education (CSTA, 2017), and similar
documents. Many software tools, resources, and curricula
have been developed to make computing accessible for
younger students. These tools allow students to focus on
learning core programming concepts while designing
personally meaningful artifacts. This process facilitates
personal expression, creativity, and learning, allowing
students to become producers of technology, not just
consumers (Resnick, Bruckman, & Martin, 1996).
At the same time as investment in CS for K-12 has been
increasing, AI has had an increasing impact on society, both
in the U.S. and elsewhere. Internationally, China has
mandated that all high school students learn about artificial
intelligence (Jing, 2018). However, despite the increased
media attention and ubiquity of AI technologies in our
everyday lives, we are just beginning to think about how to
introduce AI to K-12 students. In the new CSTA K-12
Computing Standards, there are only two sentences about
AI. Both standards appear in the 11-12 grade band (CSTA,
2017). And unlike the general subject of computing, when it
comes to AI, there is little guidance for teaching at the K-12
level.
In response to these needs, the Association for the
Advancement of Artificial Intelligence (AAAI) and the
Computer Science Teachers Association (CSTA)
announced a joint initiative in May 2018 to develop national
guidelines for teaching K-12 students about artificial
intelligence (AAAI, 2018). Inspired by CSTA's national
standards for K-12 computing education (CSTA, 2017), the
AI for K-12 Working Group will define for artificial
intelligence what students should know and be able to do.
Other organizations including AI4All (http://ai-4-all.org/)
and the International Society for Technology in Education
(ISTE) have also recognized these needs and are beginning
to address them (ISTE, 2018; Baloch, Crompton, Gerl,
Harrison, Law, McGirt, Ramos, and South, 2018). These
initiatives are laying the groundwork for AI education in K-
12, but there is much more we as a community need to do to
support this work.
To frame the guidelines we’re developing, we have defined
what we think are the “big ideas” (Wiggins and McTighe,
2005) in AI that every student should know. Before laying
out these ideas, we discuss the environment in which
students will learn them.
Tools & Resources for AI in K-12
In order for K-12 students and teachers to appreciate the big
ideas of AI, they need to be able to tinker with AI. There has
recently been an explosion of products and tools that make
AI accessible to younger students. Most cellphones today
include a voice assistant (Google Assistant, Apple’s Siri,
Microsoft’s Cortana), and there are a number of home
appliances with similar functionality (Google Home,
Amazon Echo, Apple HomePod). Computer vision is also
present “under the hood” of some consumer products, such
as Snapchat filters, or the popular Osmo app that uses vision
to recognize game pieces and children’s drawings. These
help to familiarize children with AI technologies.
Going a step further, a variety of new software and
hardware tools are providing AI components to young
programmers who can incorporate them into their own
creations. For example:
Cognimates (Druga, Vu, Likhith, Oh, Ocejo, Qui, &
Breazeal, 2018) offers a set of Scratch extensions that
provide access to speech generation, speech recognition,
text categorization, object recognition, and robot control
APIs. https://cognimates.me
eCraft2Learn (Kahn and Winters, 2017) offers similar
extensions for the Snap! language, a Scratch variant.
https://ecraft2learn.github.io/ai/
Machine Learning for Kids is another site that provides
online demos where students train classifiers using web
applications or Scratch extensions.
https://machinelearningforkids.co.uk/
The Cozmo robot by Anki is an inexpensive mobile
manipulation platform with built-in computer vision
including object and custom marker detection, face
recognition, object manipulation, path planning, and
speech generation.
Calypso for Cozmo (Touretzky, 2017) is a rule-based
visual programming language for Cozmo that adds
speech recognition (using the Google Speech API),
landmark-based navigation, a visible world map, and
support for state machine programming.
https://Calypso.software
Google has released a series of online “AI experiments”
such as “Teachable Machine” (training a visual classifier)
and “QuickDraw” (a neural net tries to guess what you’re
drawing).
https://experiments.withgoogle.com/collection/ai
Google’s AIY (“AI and You”) vision and voice kits offer
Raspberry Pi Zero-based image and speech recognition at
affordable prices. The vision kit uses a neural network
classifier, while the voice kit connects with the cloud-
based Google Assistant.
TensorFlow Playground is an interactive graphical tool
that allows high school students and undergraduates to
explore neural networks and backpropagation learning
(Thomas, 2018). https://playground.tensorflow.org
What are the “Big Ideas” in AI?
We are at the beginning stages of developing the guidelines
for AI for K-12 via a collaboration between AI experts and
K-12 teachers. The guidelines we will unpack AI’s “Big
Ideas” along five thematic strands, and organize them by
four grade bands: K-2, 3-5. 6-8, and 9-12. The current draft
of the big ideas is set out below. We recognize that others
might frame the field differently. Some may prefer a more
traditional division into application areas (speech, vision,
planning, game playing, natural language, robotics, etc.),
but we think the current formulation better meets the needs
of K-12 students and teachers. We welcome feedback from
the AI community as part of this initiative.
Big Idea #1: Computers perceive the world using
sensors.
Perception is the process of extracting information from
sensory signals. The ability of computers to “see” and
“hear” well enough to be practically useful is one of the most
significant achievements of AI. Students should understand
that machine perception of spoken language or visual
imagery requires extensive domain knowledge, e.g., for
speech one must know not just the sounds of the language
but also its vocabulary, grammar, and usage patterns. In the
absence of such knowledge, speech recognition by machine
cannot approach human-level accuracy.
Students in K-2 should know how to interact with voice-
based agents, and have some experience with machine
vision (e.g., face or object recognition using a webcam and
a web-based app, or Google’s QuickDraw demo). In grades
3-5 students should be able to modify simple perception-
based applications written in children’s programming
frameworks that include AI primitives. For example, they
can create applications that respond to spoken phrases, or
the presence of visual markers or specific faces. In grades 6-
8, students should be able to create more complex
applications on their own. By grades 9-12, students should
be able identify and demonstrate the limitations of machine
perception systems and use machine learning tools (see Big
Idea #3) to train perceptual classifiers.
Big Idea #2: Agents maintain models/representations of
the world and use them for reasoning.
AI systems are commonly described as intelligent agents
that perceive and represent the world, deliberate, and
produce outputs that affect the world. Representation is one
of the fundamental problems of intelligence, both natural
and artificial. Students should understand the concept of a
representation, e.g., the way a map represents a territory, or
a diagram represents the state of a board game. Students
should further understand that computers construct
representations using data, and these representations can be
manipulated by applying reasoning algorithms that derive
new information from what is already known. While AI
agents can reason about very complex problems, they do not
think the way a human does. Many types of reasoning that
are easy for humans are still beyond the abilities of today’s
AI systems.
In grades K-2 we expect students to be able to examine
representations created by intelligent agents (e.g., the world
map created by Calypso for Cozmo), and create simple
representations using paper and pencil. In grades 3-5 we
expect students to be able to work with representations in
simple computer programs, e.g., in Scratch a sprite can treat
the canvas and sprites as a representation of the world and
use the Touching block to query it. Students at this level can
also investigate inference algorithms through exercises such
as constructing a decision tree to determine which animal a
person is thinking of based on a series of yes/no questions,
such as “does it have wings?”. In grades 6-8 students should
be able to examine representations such as the Google
knowledge graph and simulate simple graph search
algorithms. In grades 9-12 students should be able to make
use of elementary data structures (lists and dictionaries) to
program simple inference algorithms.
Big Idea #3: Computers can learn from data.
Machine learning algorithms allow computers to create their
own representations using training data that is either
supplied by people or acquired by the machine itself. Many
areas of AI have made significant progress in recent years
thanks to machine learning technology, but for the approach
to succeed, tremendous amounts of data are needed. For
example, the Open Image Dataset V4 contains 9 million
training images and 30 million labels. Processing data at this
scale requires computing power that was unavailable a few
years ago. Care must be taken in the collection of this data
to avoid introducing biases into the training set.
Students should understand that machine learning is a
kind of statistical inference that finds patterns in data. In K-
2 they can experience this by having a computer learn to
recognize their face or simple gestures. In grades 3-5
students should be able to modify object recognition
applications, e.g., write a Scratch program that responds to
a specific object in the camera image. In grades 6-8 students
should be able to measure how well a trained system
generalizes to novel inputs, and they should understand how
biases in the training data can affect performance. In grades
9-12 students should be able to train a network using an
interactive tool like Tensorflow Playground, and advanced
students should be able to code simple machine learning
applications using Python tools like scikit-learn.
Big Idea #4: Making agents interact comfortably with
humans is a substantial challenge for AI developers.
Understanding people is one of the hardest problems faced
by intelligent agents. This includes tasks such as conversing
in natural language, recognizing emotional states, and
inferring intentions from observed behavior. Students
should understand that while computers can understand
natural language to a limited extent, at present they lack the
general reasoning and conversational capabilities of even a
child.
Graceful interaction with humans is especially important
for robotic agents that will share our living and working
spaces. We may want a robot assistant to stay close so it’s
always ready to help, but it shouldn’t stick to us so closely
that it’s constantly in the way. Inferring a person’s future
intentions by observing their actions is challenging even for
humans. Robots will need to acquire some of this skill if
they are to be welcome in our lives.
Students in K-2 should be able to describe the types of
requests an intelligent assistant understands, and use a web
app to demonstrate facial expression recognition. In grades
3-5 students should be able to distinguish a chatbot from a
human, and analyze natural language examples to determine
which ones would be difficult for a computer to understand,
and why. In grades 6-8 students should be able to use parser
demos to demonstrate syntactic parsing of sentences, and
construct sentences that purely syntactic parsers will mis-
handle due to problems such as erroneous prepositional
phrase attachment (e.g., “I pour syrup for pancakes from a
bottle”). They should also be able to show how parsers that
take semantic information into account do a better job
resolving attachment problems.
In grades 9-12 students should be able to construct context-
free grammars to parse simple languages, and use language
processing tools to construct a chatbot. They should also be
able to use sentiment analysis tools to extract emotional tone
from text.
Big Idea #5 - AI applications can impact society in both
positive and negative ways.
Students should be able to identify ways that AI is
contributing to their lives. The societal impacts of AI
involve two kinds of questions: what applications should AI
be used for (there is growing interest in “AI for social
good”), and what ethical criteria should AI systems be
required to meet? In the near future many people will work
alongside intelligent assistants or autonomous robots, but in
the long term, the automation of many jobs may lead to
massive unemployment or a shift in the kinds of work
people pursue. Technologies that allow intelligent agents to
better understand humans could give us the robotic servants
we’ve long been dreaming of, or home health aides for the
elderly, but they could also enable massive government
surveillance and complete loss of privacy.
Students should understand that the ethical construction of
AI systems that make decisions affecting people’s lives
requires attention to the issues of transparency and fairness.
Transparent systems provide justifications for their
conclusions so their reasoning can be checked and wrong
assumptions identified. Fairness is tricky because when
some level of error is unavoidable in a decision system, the
best one can hope for is to distribute those errors equitably,
avoiding socially undesirable biases.
Students in grades K-2 should be able to identify how AI
contributes to their daily lives, and how it may contribute
more in the future (e.g., robot servants). Students in grades
3-5 should exhibit critical thinking about the impacts of new
AI applications, e.g., self-driving cars will be a boon to
people who cannot drive themselves, but may also put taxi
drivers out of work. Students in grades 6-8 should be able to
draw parallels between earlier industrial revolutions and
what some AI futurists are calling the fourth industrial
revolution. In grades 9-12 students should be able to
evaluate new AI technologies and describe the ethical or
societal impact questions raised by them.
Curriculum Development
There are already significant efforts to address the need for
curriculum resources. Australian AI researchers
collaborated with K-6 teachers through the Scientist-in-
Schools program to deliver a three year curriculum covering
basic AI concepts, AI vocabulary, and the history of AI
(Heinze, Haase, & Higgins, 2010). Another recent example
is IRobot: an AI curriculum for high school students
(Burgsteiner, Kandlhofer, & Steinbauer, 2016). ISTE has
announced a partnership with GM to develop an AI
curriculum for high school students (ISTE, 2018), and
AI4All recently received a $1 million grant from Google to
develop an open, online AI curriculum to launch in 2019
(AI4ALL, 2018)
The joint AAAI/CSTA AI for K-12 initiative will not be
developing a curriculum of its own. Rather, it will establish
guidelines that future curricula should meet, and curate an
online resources directory that will help educators locate
tools and curricula like those described above. We welcome
feedback and resource suggestions for K-12 educators as we
continue to develop the AI for K-12 guidelines.
Conclusion
We conclude with a call to action for the AI community. K-
12 students will be searching the internet trying to
understand how AI works and how it will shape their future.
Think about how your research can be made into an easily
available demo, resource, or activity that students and
teachers can use and share.
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... This integration of AI is one of the primary research in computers and education, with the potential to transform human knowledge and promote educational reforms (Yang, 2022;Chatterjee & Bhattacharjee, 2020). However, Touretzky et al. (2019) argued that while AI has the potential to revolutionize many areas of society, its use can also have negative consequences. The study of Popenici and Kerr (2017) argued that further research in this field is necessary to develop ethical guidelines and practical implementation strategies to address the drawbacks of this technology for teaching, learning, and administration. ...
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The widespread adoption of support tools, such as artificial intelligence, is evident across various fields, including the academic community. Students' attitudes towards using Al tools like ChatGPT significantly impact their utilization. The research employs the Theory of Reasoned Action (TRA) framework, incorporating academic stress and risk propensity as additional constructs to examine students' attitudes toward ChatGPT. The study focuses on college students using Al tools for academic purposes. A survey was conducted across educational institutions, yielding 413 responses. Analysis using the Partial Least Squares — Structural Equation Model revealed that academic stress and peer influence do not positively affect the intention to use ChatGPT. However, academic stress and risk propensity positively impact students' attitudes toward ChatGPT, influencing the intention to use the tool. The study recommends expanding research to include teachers and other professionals, considering diverse cultural settings and employing various research methods. The findings also provide insights for academia to enhance the adoption and integration of Al tools.
... Truster characteristics -students' readiness and intent to trust. It has been shown in the educational domain that increasing users' familiarity and epistemic knowledge about AI-powered technology influence their intent to trust it (Hoff & Bashir, 2015;Touretzky et al., 2019;Luckin et al., 2022;Nazaretsky et al., 2022a). This refers to a distinct set of characteristics predominantly centered around AI literacy and AI readiness (Laupichler et al., 2022;Long & Magerko, 2020;Luckin et al., 2022;Ng et al., 2021). ...
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In recent decades, we have witnessed the democratization of AI-powered Educational Technology (AI-EdTech). However, despite the increased accessibility and evolving technological capabilities, its adoption is accompanied by significant challenges, predominantly rooted in social and psychological aspects. At the same time, limited research has been conducted on human factors, especially trust, influencing students' readiness and willingness to adopt AI-EdTech. This study aims to bridge this gap by addressing the multidimensional nature of trust and developing a new instrument for measuring students' perceptions of adopting AI-EdTech. With 665 student responses, we employ Exploratory and Confirmatory Factor Analysis to provide evidence of the instrument's internal validity and identify four key factors influencing students' trust and readiness to adopt AI-EdTech. We then utilize Structural Equations Modeling to explore the causal relationships among these factors, confirming that students’ trust in AI-EdTech positively influences AI-EdTech's perceived usefulness both directly and indirectly through AI-readiness. Finally, we use our instrument to analyze 665 student responses, covering eight courses and Bachelor's and Master's degree programs. Our contribution is two-fold. First, by introducing the empirically validated instrument, we address the need for more consistent and reliable assessments of trust-related factors in student adoption of AI-EdTech. Second, our findings confirm that student demographics, specifically gender and educational background, significantly correlated with their trust perceptions, emphasizing the importance of addressing the specific needs of students with various demographics.
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We applied a mixed‐method survey approach to explore STEM teachers' perceptions, familiarity, and the support needed for integrating generative artificial intelligence (GenAI) in K‐12 education. The study collected 48 responses from Idaho, USA, predominantly from White, female teachers servicing in rural schools. We analyzed data using both descriptive and inferential statistics, along with thematic and content analysis. The findings revealed diverse perceptions among STEM teachers regarding the impact of GenAI on education, with an almost equal split between those who viewed GenAI positively and those who viewed it negatively. Similarly, teachers' familiarity with GenAI integration varied widely, with over half lacking user experience. A significant positive correlation was found between teachers' perceptions of GenAI and their familiarity with its integration. Despite these varied views, there was a strong consensus among teachers on the importance of equipping students with AI‐related knowledge and skills. While professional development was identified as the most crucial support for GenAI integration, STEM teachers pointed to their own resistance and a lack of awareness among school leadership as major challenges to implementing GenAI‐focused professional development. The study discussed the implications for developing support systems that can better facilitate STEM teachers' GenAI integration.
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Middle School students in the United States are exposed to an unprecedented number of AI-driven consumer products. This exposure demands that educators help students develop their personal understandings of these technologies to engage with them responsibly. Designing age-appropriate AI curricula for middle school students calls for collaboration and partnership between computer and learning scientists, as well as middle school teachers. Over a 3-year period, we co-designed and successfully implemented an AI education curriculum across 9 geographically and economically diverse schools, offering it to a total of 1551 students. Drawing from our analyses of the curriculum and teacher and student experiences, we propose an effective format for teaching, assessing, and implementing fundamental AI education for middle school settings in the United States. Our research also highlights the value of empowering teachers through co-design; enriching their professional development and improving students’ AI literacy.
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IRobot: Teaching the Basics of Artificial Intelligence in High Schools
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Burgsteiner, H.; Kandlhofer, M.; and Steinbauer, G. 2016. IRobot: Teaching the Basics of Artificial Intelligence in High Schools. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Menlo Park, California: AAAI Press.
Updated: AP Computer Science Principles: Course and Exam Description including the Curriculum Framework
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AI Education Colloquium
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Understanding Deep Learning with TensorFlow playground
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Understanding by Design
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AI for K-12" Initiative in Collaboration with the Computer Science Teachers Association (CSTA) and AI4All
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AAAI Launches "AI for K-12" Initiative in Collaboration with the Computer Science Teachers Association (CSTA) and AI4All [Press release].
An Action Research Report from a Multi-Year Approach to Teaching Artificial Intelligence at the K-6 Level
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Heinze, C.; Haase, J.; and Higgins, J. 2010. An Action Research Report from a Multi-Year Approach to Teaching Artificial Intelligence at the K-6 Level. First AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press. International Society for Technology in Education (ISTE). 2018. Bold New Program Helps Teachers and Students Explore the Power of AI [Press release]. Retrieved from https://www.iste.org/explore/articleDetail?articleid=2229