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AI Learning Priorities
for All K-12 Students

AI Learning Priorities
for All K-12 Students

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About the Project

Vision

All students must learn about artificial intelligence (AI) as part of a foundational computer science (CS) education in order to be prepared for a world powered by computing.

Over the last few years, we have witnessed an unprecedented expansion in artificial intelligence capabilities, with ChatGPT experiencing the most rapid adoption of any consumer app in history. Generative AI tools (that is, AI capable of generating text, audio, images, and/or video) have significant economic, legal, societal, and national security implications. Increasing public awareness of these impacts – ranging from environmental consequences to debates about intellectual property to concerns over potential job losses due to automation – have made AI a staple topic of the news cycle in the last few years.

It is thus no surprise that eight out of ten CS teachers believe that learning about and using AI should be part of a foundational CS learning experience and that CS standards should include AI. In fact, when asked to identify the topics they teach, over two-thirds of CS teachers stated they covered AI specifically, despite the lack of explicit definition in most CS standards.

Chart summarizing results from a survey of CS teachers. 85% of teachers believe that a foundational CS experience should include using and learning about AI. 80% of teachers believe that CS standards should include AI topics. 87% of teachers say that students should learn about how AI impacts careers. 70% of teachers teach AI in their classrooms. 42% of teachers feel equipped to teach about AI.

As part of its standards revision effort, CSTA, in partnership with AI4K12, spearheaded the Identifying AI Priorities for All K-12 Students project. The project gathered experts – including teachers, researchers, administrators, and curriculum developers – to articulate AI learning priorities within CS. This project was designed to support the revision of CSTA standards so that the new standards will be positioned to incorporate appropriate AI learning outcomes.

What AI Content and Skills are Important for ALL Students?

A primary outcome of the Identifying AI Priorities for All Students project is a list of foundational AI learning outcomes organized by grade band across five categories. Explore the foundational AI content using the summarizing figure below.

Interactive graphic showing AI Learning Priorities for All K-12 Students. The priorities are organized in five categories: Humans and AI, Representing and Reasoning, Machine Learning, Ethical AI Systems Design and Programming, and Societal Impacts of AI.

Humans and AI Prioritized Learning Outcomes

Subtopic

Grades K-2

Grades 3-5

Grades 6-8

Grades 9-12

The Nature of
Humans and of AI

Compare and
contrast the nature
of humans versus
the nature of AI
(e.g., living versus
nonliving).

Compare and
contrast the ability of
humans and of AI to
perform various tasks
and serve in various
roles (e.g., create art,
recognize emotions,
be a friend, serve as
a tutor).

Identify the
assumptions inherent
in the operation
and output of an
AI model and how
these assumptions
might have different
implications for
different people.

Debate what
differences do or
should exist between
human and artificial
intelligence, sentience,
consciousness, rights,
and responsibilities.

The Human Role
in Creating AI

Understand that AI
is a tool created by
humans to make
decisions or to
generate something
(e.g., an image).

Describe the role
of humans in the
creation of AI.

Describe the roles
that humans play
(including in data
curation and labeling)
in creating and
refining AI models.

Evaluate and
analyze the roles
of humans and
human decision-
making in the
creation of AI.

The Choice to
Use AI

N/A

Evaluate when AI is
or is not a helpful
resource to carry
out a task.

Debate when
humans should or
should not use AI
to perform a
specific task.

Analyze the risks,
benefits, and
effectiveness of
using AI for specific
tasks (e.g., coding,
brainstorming),
including when AI is
used to fully automate
a process or is used
with a human-in-the
loop approach.

Representation and Reasoning Prioritized Learning Outcomes

Subtopic

Grades K-2

Grades 3-5

Grades 6-8

Grades 9-12

Understanding
Representation

N/A

Understand how
a representation is
an abstraction that
focuses on some
features and leaves
others out.

Understand that
representation
includes modalities (text,
speech, audio, image,
video) and symbolic
mappings (text, graphs).

Describe how
current AI models
(e.g., LLMs) use
data representation.

Creating a
Representation

Create a
representation
of a physical
object (e.g., line
art drawing).

Create an abstract
representation of
a physical system
that can be used
to solve a problem
(e.g., a map).

Create and evaluate
different abstract
representations
(e.g., subway map).

Choose and use
an appropriate
representation of
complex data for
processing by an
AI algorithm.

Reasoning

Explain how binary
choices (e.g., up/down,
on/off, under/over) can
be used to make decisions
that lead to a specific
goal by either a human
or a machine.

Train a model that
can make decisions
based on defined
criteria (e.g., a
dichotomous key
to determine which
movie to see).

Identify the kinds
of AI models (e.g.,
classifier, predictor,
recommender)
people interact with
in their daily lives.

Describe different
types of AI algorithms
and models, and
compare and contrast
the strengths and
limitations of their
reasoning.

Machine Learning Prioritized Learning Outcomes

Subtopic

Grades K-2

Grades 3-5

Grades 6-8

Grades 9-12

Sensing

Compare and
contrast human
sensing with
computer sensors.

Describe various
ways that a human
might interact with
an AI system
(e.g., through voice,
text, or gestures).

Use sensors to
collect data, and
then train an AI
model using the
sensor data.

Using sensor
data (e.g., from
autonomous
vehicles), train
an AI model.

Data

Explore how AI
models learn
from data.

Explore the
relationship between
the properties of
training data (e.g.,
size, features, biases)
and an AI model’s
output.

Describe the ways
that bias can be
introduced and
mitigated in an
AI model.

Evaluate the data
used to solve a
problem, including its
source(s) and whether
privacy is protected,
if/how the data has
been processed, data
quality (e.g., accuracy,
reliability, validity),
what the data
represents, and biases.

How Computers
Learn

Understand
how computers
learn from data
and patterns.

Investigate how
AI models learn by
using data (including
why examples and
non-examples are
required in training
sets) and algorithms
to find patterns and
generate output.

Create and
evaluate an
appropriate
AI algorithm
(e.g., a decision
tree classifier) to
accomplish a task.

Select and use an
appropriate AI
algorithm for a
classification task
(e.g., KNN, decision
tree).

Building and
Using AI Models

Use data to construct
a model for making
decisions (e.g., a
decision tree to
determine what
to wear based on
the weather).

Using a dataset,
develop an AI model
to classify inputs.

Using a dataset and
a machine learning
pipeline, develop
an AI model, and
consider the impact
of the model on
various users.

Using a dataset
and a systematic
process, develop an
AI model to generate
for classification
or prediction,
and articulate the
assumptions made at
each of these steps:
(1) develop a question
solvable with AI, (2)
collect or curate data,
(3) evaluate the data,
(4) train an AI model
on the data, (5),
evaluate the model,
and (6) iteratively
improve the model.

Ethical AI System Design and Programming Prioritized Learning Outcomes

Subtopic

Grades K-2

Grades 3-5

Grades 6-8

Grades 9-12

Ethical Design
Criteria

N/A

Investigate an
example of AI
decision making,
considering if it is
fair – as well as what
it means to be fair.

Explore strategies
to turn ethical
considerations into
actions, such as
mitigating bias in
datasets.

Evaluate an AI model
(e.g., using a model
card) to determine
the model’s features
as well as its biases,
explainability, fairness,
privacy, accuracy, and
transparency.

Ethical Evaluation
of AI Systems

Explore how
an AI system can
help and harm
different groups
at the same time.

Investigate
examples of AI,
considering
differences in
experience by
different people in
different contexts.

Describe the
properties, biases,
and assumptions
of various kinds
of AI models (e.g.,
classifier, predictor,
recommender).

Evaluate the
design, motivation,
outcomes, and
potential impacts
of AI systems using
ethical design criteria
and/or ethical
frameworks.

Ethical Creation
of AI Systems

N/A

Describe an AI
design process that
considers the impact
on end users and
others who are
impacted by the
AI system.

Create a program
using available AI
tools, AI plugins,
APIs, and/or AI
models, with the
following ethical
considerations for
the model’s end
users as well as
others who are
impacted by the
model: fairness,
bias, and accuracy,
and then create a
model card.

Train, iteratively
improve, and then
develop a model card
for an AI model with
the following ethical
considerations for the
model’s end users as
well as others who
are impacted by
the model: fairness,
bias, safety, security,
intellectual property,
privacy, robustness,
explainability, accuracy,
transparency, and
accountability.

Societal Impacts of AI Prioritized Learning Outcomes

Subtopic

Grades K-2

Grades 3-5

Grades 6-8

Grades 9-12

Individual Impacts

Identify where
AI is being used
in daily life.

Explore how
one’s actions may
result in the
collection of data.

Explore the tradeoffs
related to human
agency (including
privacy, safety,
creativity, autonomy,
and intellectual
property) when
AI is used.

Evaluate how AI use
impacts an individual’s
decision making and
other behavior.

Societal Impacts

Explore how some
people use AI in their
jobs and in their
communities.

Explore ways in
which some jobs
involve the creation
and/or use of AI.

Identify the intended
and unintended
impacts of AI on
society — including
government, education,
entertainment, culture,
careers, and national
security — while
considering how
these impacts may
differ among diverse
communities.

Evaluate the intended
and unintended
impacts of AI on society
(e.g., deep fakes, job
loss) — including
government, education,
entertainment, culture,
careers, and national
security — while
considering how
these impacts may
differ among diverse
communities.

Environmental
Impacts

N/A

Explore the
impact of AI on
the environment.

Investigate the
positive and negative
environmental impacts
of AI (e.g., minimizing
deforestation via
application of AI,
energy use by AI).

Design ways to
minimize negative
environmental impacts
of AI and communicate
those ways to others.

Given the wide scope of computer science and already full curriculum, the project team emphasized the need to define levels of priority. As a result, rows within each table are highlighted in light green to indicate the most important of the foundational AI content. This determination was made based on artifacts of the convening and detailed feedback during the review process for this report. Prioritized content (i.e., highlighted rows) across all five categories is compiled into a singular table in Appendix F.

Promising Practices for Teaching AI

To meet the project’s goal of sharing promising practices across the AI and CS education communities, the convening featured brief presentations from participants that highlighted their work in this field.

stars

Based on these presentations, we offer the following recommendations for AI curriculum:

*Additional examples can be found in the report.

Resources

This resource is a glossary of key AI-related terms and definitions based on the AI4K12 glossary.

Glossary (Appendix E)

This resource is a glossary of key AI-related terms and definitions based on the AI4K12 glossary.

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The guidelines serve as a framework to assist standards writers and curricula developers on AI concepts, essential knowledge, and skills by grade band.

AI4K12 Guidelines

The guidelines serve as a framework to assist standards writers and curricula developers on AI concepts, essential knowledge, and skills by grade band.

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The heatmaps represent an analysis of coverage of the AI4K12 Guidelines by a set of AI curricula.

Heatmaps

The heatmaps represent an analysis of coverage of the AI4K12 Guidelines by a set of AI curricula.

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The Future of CS in an Age of AI briefs address critical questions related to the role of AI in primary and secondary computer science (CS) education.

TeachAI Briefs

The Future of CS in an Age of AI briefs address critical questions related to the role of AI in primary and secondary computer science (CS) education.

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An outcome of the Reimagining CS Pathways project included defining AI content beyond a foundational CS learning experience to inform the development of a high school AI pathway.

Reimagining CS Pathways – AI specialty area

An outcome of the Reimagining CS Pathways project included defining AI content beyond a foundational CS learning experience to inform the development of a high school AI pathway.

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This resource includes the most essential AI content for all K-12 students.

Prioritized Foundational K-12 AI Learning Outcomes

This resource includes the most essential AI content for all K-12 students.

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SUGGESTED CITATION

CSTA & AI4K12 (2025). AI Learning Priorities for All K-12 Students. New York, NY: Computer Science Teachers Association.
Retrieved from https://csteachers.org/ai-priorities.

This work is licensed under CC BY-NC-SA 4.0.

REFERENCES

Berthelot, A., Caron, E., Jay, M., & Lefèvre, L. (2024). Estimating the environmental impact of Generative-AI services using an LCA-based methodology. Procedia CIRP, 122, 707–712.


Lucchi, N. (2024). ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence Systems. European Journal of Risk Regulation, 15(3), 602–624.


Gmyrek, P., Berg, J., & Bescond, D. (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality. International Labour Organization.


CSTA, IACE, ACM, Code.org, College Board, CSforALL, & ECEP Alliance. (2024). Reimagining CS Pathways: Every student prepared for a world powered by computing.


TeachAI & CSTA. (2024). Guidance on the Future of Computer Science Education in an Age of AI. CSTA & Kapor Foundation. (2025) [Forthcoming]. Landscape Study of PreK-12 CS Teachers in the United States.