10 Policies For Digital Fluency
CodeAI is moving beyond binary policy indicators and toward a more nuanced understanding of progress, capacity, and ambition.
This document reframes CodeAI’s Ten Policy Ideas to Make Computer Science Foundational to K–12 Education through a tiered, future-facing lens that explicitly integrates artificial intelligence, computer science, and data science, as core components of education in the digital sciences. As AI, computer science, and data science increasingly shape the economy, civic life, and the tools students use every day, CodeAI is moving beyond binary policy indicators and toward a more nuanced understanding of progress, capacity, and ambition. The structure — Emerging/Historic, Developing, and Transformative — is not intended to suggest that states must proceed linearly through the tiers. States may enter at any level and are encouraged to adopt Transformative policies first wherever possible. States that have not met the listed criteria, at any level, for a policy will be listed as “no” for that policy. The ultimate goal of this document is for all states to reach the Transformative level across all ten policy areas, ensuring that every student graduates with the digital fluency to understand, direct, question, and create with the technology shaping their world. This approach replaces a simple “yes”, “no”, or “in progress” rubric used in prior reports and policy position documents with a more flexible and adaptive model. By distinguishing between levels of policy maturity, this rubric allows CodeAI and state leaders to recognize meaningful progress while maintaining a clear vision of what excellence looks like. Importantly, the tiered model enables the Transformative level to more easily evolve over time in response to changes in technology, workforce demands, and educational research without penalizing states by retroactively shifting them from “yes” to “no.” In this way, the rubric supports continuous improvement, shared accountability, and long-term alignment with the rapidly evolving field of artificial intelligence. For the purposes of this document, CodeAI uses the following terms: Digital Sciences is the area of study which encompasses AI Science, Computer Science, and Data Science — grounded in Ethics & Responsible Computing and Human-Centered Design / Human-AI Interaction — which represent the foundational sciences of the next century.
AI Science refers to the systematic study of artificial intelligence systems, including how they are designed, built, trained, evaluated, and improved across the technical stack from data and model architecture through training, deployment, and iteration, and considerations those systems raise: bias, misinformation, and the ethical questions that come with technologies that can learn and act autonomously
Computer Science refers to the systematic study of computers and algorithmic processes, including their principles, their hardware and software designs, their implementation, and their impact on society.
Data Science refers to the systematic study of the processes, analytical techniques, computational methods, and technologies used to gain knowledge and insight from data.
Digital Fluency is a student’s ability to understand, direct, question, and create with the technology shaping their lives. This includes understanding how AI systems work, why they produce what they produce, where they break, how data and design choices shape behavior, and how to direct AI toward purposeful outcomes.
This document reframes CodeAI’s Ten Policy Ideas to Make Computer Science Foundational to K–12 Education through a tiered, future-facing lens that explicitly integrates artificial intelligence, computer science, and data science, as core components of education in the digital sciences. As AI, computer science, and data science increasingly shape the economy, civic life, and the tools students use every day, CodeAI is moving beyond binary policy indicators and toward a more nuanced understanding of progress, capacity, and ambition.
The structure — Emerging/Historic, Developing, and Transformative — is not intended to suggest that states must proceed linearly through the tiers. States may enter at any level and are encouraged to adopt Transformative policies first wherever possible. States that have not met the listed criteria, at any level, for a policy will be listed as “no” for that policy. The ultimate goal of this document is for all states to reach the Transformative level across all ten policy areas, ensuring that, “every student in every school has the opportunity to learn about artificial intelligence (AI) and computer science (CS) as part of their core K–12 education.” — https://code.org/about
This approach replaces a simple “yes”, “no”, or “in progress” rubric used in prior State of AI + CS Reports with a more flexible and adaptive model. By distinguishing between levels of policy maturity, this document allows CodeAI and state leaders to recognize meaningful progress while maintaining a clear vision of what excellence looks like. Importantly, the tiered model enables the Transformative level to more easily evolve over time in response to changes in technology, workforce demands, and educational research without penalizing states by retroactively shifting them from “yes” to “no.” In this way, the rubric supports continuous improvement, shared accountability, and long-term alignment with the rapidly evolving field of artificial intelligence.
For the purposes of this document, CodeAI distinguishes between AI education and AI in education; each of which is defined at the end of the document. These terms are often used interchangeably in policy conversations, but they represent distinct concepts with different implications for instruction, curriculum, and state policy. References to AI education are intended to describe students learning about AI not the adoption or regulation of AI tools.
10 Policies
1.
Create a Statewide Plan and School Guidance for K–12 Digital Sciences Education
Transformative
State plan includes clear timelines, strategic goals, and implementation supports for K–12 Digital Sciences, explicitly integrating CS with developmentally appropriate AI Science and Data Science concepts across all grade bands and clear pathways to the inclusion of Digital Sciences in upper grades, AND
State guidance on implementation of Digital Sciences, clearly distinguished from only AI tool use, is published.
Developing
State plan includes broad CS goals and standards adoption with optional inclusion of AI Science and Data Science topics, AND
State guidance on implementation of AI Science or Data Science, but not both, is published, OR
The state has both a state plan and state guidance on implementation of Digital Sciences, but only one of the two meets the criteria to be at the transformative level.
Emerging/Historic
The state only has one of the following: a CS state plan, state guidance on implementation of AI Science, or state guidance on implementation of Data Science.
Any have not been updated in over four years (Historic).
Note
State plans and guidance may be developed by the state internally or by external partners; however, they must be endorsed by the state education agency or state board of education to meet these requirements. If the state plan or guidance development has been initiated by legislation or the SEA and this information is publicly available, but neither the state plan or guidance has been published, this policy will be marked as “in progress.”
2.
Establish K–12 Digital Fluency Standards
Transformative
State includes AI Science and Data Science learning expectations scaffolded throughout the adopted K–12 CS standards.
Developing
State adopts CS standards in which either AI Science or Data Science learning expectations are integrated across grade bands.
Emerging/Historic
State adopts a general or outdated (Historic — have not been revised within the past 5 years) set of CS standards without mention of AI Science or Data Science.
Note
If standards creation/revision has been initiated by legislation or the SEA, and this information is publicly available, but neither K–12 AI or CS standards have been published, this policy will be marked as “in progress.”
3.
Establish Dedicated Digital Science Positions in State Education Agencies
Transformative
State education agency includes full-time Digital Science (AI Science, Computer Science, AND Data Science) leadership with responsibilities including policy integration, instructional support, and implementation of Digital Science guidance with clearly defined authority to coordinate Digital Science policy across standards, professional learning, data reporting, and implementation guidance.
This leadership may be one full-time employee dedicated and focused solely on Digital Science education or two (or more) full-time employees, with split focus on AI Science, Computer Science, and Data Science.
Developing
Dedicated CS leadership exists, with AI Science and Data Science as occasional initiatives but not a strategic focus.
Emerging/Historic
Dual role CS leadership exists (CS Supervisor that has other duties not related directly to Digital Sciences; or
There has been a dedicated CS leader within the past four years (Historic).
Note
State plans and guidance may be developed by the state internally or by external partners; however, they must be endorsed by the state education agency or state board of education to meet these requirements. If the state plan or guidance development has been initiated by legislation or the SEA and this information is publicly available, but neither the state plan or guidance has been published, this policy will be marked as “in progress.”
4.
Maintain Data, Reporting, and Accountability for Implementation of the Digital Sciences
Transformative
The state maintains a transparent, publicly accessible data and accountability system that tracks the implementation and impact of the Digital Sciences across every grade K–12.
The state publicly reports, at minimum: Student participation and completion in AI Science, Computer Science, and Data Science courses disaggregated by course type, grade level, and student subgroup. School-level access to Digital Sciences offerings, including which courses meet graduation requirements. Teacher capacity indicators, including certifications, endorsements, or Digital Sciences-focused professional learning.
Reporting occurs on a regular, predictable schedule and is integrated into existing state accountability or data dashboards.
The state establishes clear responsibility within the state education agency for data quality, analysis, and public communication related to Digital Sciences implementation
Developing
The state collects partial data related to CS implementation, with limited inclusion of AI Science or Data Science-specific indicators, or has data that is not consistently publicly-facing.
The state tracks at least one of the following: Student enrollment or completion in CS courses, School-level CS course availability, Teacher participation in CS professional learning.
AI Science and Data Science data are collected inconsistently, tracked informally, or embedded within broader technology reporting without clear visibility.
Data is primarily used for internal planning or grant reporting and is not consistently published or easily accessible to the public.
Accountability mechanisms exist but are limited in scope or frequency.
Emerging/Historic
The state has limited or no formal data collection or accountability mechanisms related to Digital Sciences implementation.
Digital Sciences participation data is not systematically collected, is optional, or is unavailable at the state level.
Reporting relies on one-time studies, voluntary surveys, or external partners rather than sustained state systems.
The state previously tracked CS implementation data under earlier policy efforts but no longer maintains or updates those systems (Historic).
Note
For “in progress” the state must have formally initiated efforts (through mandating legislation or regulation) to collect, align, or publicly report data related to Digital Sciences implementation, but a comprehensive, recurring system is not yet in place.
5.
Require All Students to Take a Digital Science Course to Earn a High School Diploma
Transformative
The state requires that all students earn a trackable credit for a dedicated Digital Sciences course for high school graduation.
All courses that meet the requirement must include at minimum foundational instruction in AI Science, Computer Science and Data Science.
The description of the requirement is publicly accessible.
Developing
All students must take a student-level trackable Digital Sciences course; AI Science and Data Science may be included in eligible Computer Science courses but it is not an explicit requirement of the coursework that meets the grad requirement.
Emerging/Historic
A student-level trackable Computer Science requirement has been adopted through legislation or regulation, but a graduating class has not yet been subject to the law or rule. The state met this policy under our prior rubric (Historic).
Note
Currently, there is no “in progress” status for this policy. The state shall make available a list of courses or standards that satisfy the requirement. Course titles alone do not determine qualification; course content must include AI Science, Computer Science, and Data Science concepts.
6.
Allow Digital Sciences to Count Toward a Core Graduation Requirement
Transformative
Any AI Science, Computer Science, or Data Science course used to satisfy another core content area graduation requirement must include: foundational or advanced AI Science, foundational or advanced Computer Science, AND foundational or advanced Data Science.
Developing
AI Science, Computer Science, or Data Science courses count toward another core content area graduation requirement: AI Science and Data Science topics may be present in select Computer Science courses but are not required. Data Science courses must include AI Science and Computer Science topics.
Emerging/Historic
CS credit may count toward another core content area graduation requirement but with no oversight on content or inclusion of AI Science or Data Science; or
The state previously met the “Make CS Count” policy suggestion, but has reduced its impact or flexibility for students (Historic).
Note
State plans and guidance may be developed by the state internally or by external partners; however, they must be endorsed by the state education agency or state board of education to meet these requirements. If the state plan or guidance development has been initiated by legislation or the SEA and this information is publicly available, but neither the state plan or guidance has been published, this policy will be marked as “in progress.”
7.
Require All Schools to Offer the Digital Sciences
Transformative
All high schools must offer at least one dedicated Digital Sciences course and all students in grades K–8 must receive age appropriate Digital Sciences instruction each year.
These offerings are tracked with data to ensure compliance.
Developing
CS offerings are required statewide and tracked with data to ensure compliance; AI Science and Data Science are optional and not tracked.
Emerging/Historic
CS is required, but no tracking mechanism is available; AI Science and Digital Science are not considered in course offerings; or
The state had an all high schools must offer policy that is no longer being enforced (Historic).
Note
State plans and guidance may be developed by the state internally or by external partners; however, they must be endorsed by the state education agency or state board of education to meet these requirements. If the state plan or guidance development has been initiated by legislation or the SEA and this information is publicly available, but neither the state plan or guidance has been published, this policy will be marked as “in progress.”
8.
Allocate Funding for Rigorous Teacher Professional Learning
Transformative
A state has yearly dedicated funding for professional learning that includes concepts and skills related to the Digital Sciences (AI Science, Computer Science, and Data Science) and is intended to support teachers in teaching Digital Sciences.
Developing
Computer Science PD is funded and recurring but AI Science and Data Science content is optional or not embedded across offerings.
Emerging/Historic
CS PD exists but is limited in scope, voluntary, or excludes AI Science and Data Science, OR
CS PD funding has stopped, but was provided within the past four years (Historic), OR
The state has a minimum of 75% of schools offering at least one high school CS course and the state has provided funding in the past (Historic).
Note
There is no “in progress” status for this policy. Funding for Digital Sciences professional learning may come from state, federal, or external sources. Regardless of the funding source, the state must maintain oversight responsibility for how funds are allocated, used, and monitored to ensure alignment with Digital Sciences instructional priorities and educator capacity-building goals.
9.
Implement Clear Credentialing and Recognition Systems for Educators
Transformative
Certification pathways require or incentivize AI Science and Data Science training and offer micro-credentials or endorsements as part of or in addition to CS established certification pathways and are formally recognized by the state for instructional assignment and, where applicable, licensure advancement or compensation.
Developing
Pathways support CS certification broadly with minimal AI Science or Data Science content, perhaps as electives.
Emerging/Historic
A CS specific certification or pathway exists and
AI Science or Data Science is not addressed in licensure preparation.
Note
State plans and guidance may be developed by the state internally or by external partners; however, they must be endorsed by the state education agency or state board of education to meet these requirements. If the state plan or guidance development has been initiated by legislation or the SEA and this information is publicly available, but neither the state plan or guidance has been published, this policy will be marked as “in progress.”
10.
Create Programs for Preservice Teachers to Gain Exposure to Digital Sciences
Transformative
All preservice teachers are required to demonstrate completion of instruction that includes foundational Digital Science content, which may be integrated across the preservice curriculum and includes practical examples for non-CS classrooms.
The state has at least one preservice program actively offering certification pathways that prepare teachers to teach foundational Digital Sciences.
Developing
CS exposure is required, but AI Science and Data Science topics are optional AND
The state has at least one preservice program actively offering certification pathways for teachers to become certified to teach foundational CS.
Emerging/Historic
Preservice programs and the state encourage, but do not require, exposure to Digital Sciences through program availability or funding incentives; or
The state previously met this policy suggestion under the prior rubric (Historic).
Note
State plans and guidance may be developed by the state internally or by external partners; however, they must be endorsed by the state education agency or state board of education to meet these requirements. If the state plan or guidance development has been initiated by legislation or the SEA and this information is publicly available, but neither the state plan or guidance has been published, this policy will be marked as “in progress.”