Curiosities in the AI in Education Executive Order
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Curiosities in the AI in Education Executive Order, like the Disappearance of Computer Science Education
By Erin Anderson
The National Need for Computer Science
At the SITE Conference in March 2025, I argued that computer science should be taught to all students and teachers as a matter of urgent national security. In the weeks leading up to the conference, I watched DOGE programmers dismantle American technological infrastructure. I struggled to convey the magnitude of this dismantling when speaking with community members. However, as a fan of two major computer science (CS) initiatives (i.e., the development of the K-12 Computer Science Framework, a framework rooted in equity and established to guide the enactment of computer science education, and the AI4K12 initiative, which provides national guidelines & resources on AI in education), I wondered if this conversation with the general populace would have been easier if everyone spoke the language of computer science, as codified in these two initiatives. For example, the K-12 Computer Science Framework has students:
Interrogate assumptions embedded in technology, evaluate whether technology harms individuals, and advocate for better developmental methods (Practice 1: Fostering an Inclusive Computing Culture)
Evaluate if problems can/should be solved computationally (Practice 3: Recognizing and Defining Computational Problems)
Identify and justify how/why computational products are developed, including ideation, testing, and implementation (Practice 5: Creating Computational Artifacts; Practice 6: Testing and Refining Computational Artifacts; Practice 7: Communicating About Computing).
The AI4K12 initiative presents five big ideas about artificial intelligence, primarily focusing on how computers perceive the world, how AI agents represent and reason, how computers learn from data, how AI emulates natural interactions, and the positive and negative ways AI impacts society.
If we all spoke the language of computer science, as outlined by these two CS initiatives, could we come together to engage in richer discussions about DOGE’s programmatic hijacking of our data and government technology?
Curiosity 1: Computer Science’s Disappearance
A month later, while I attended the AERA conference, the administration issued an executive order highlighting the urgent need to scale artificial intelligence in education (AIED EO). For a moment, I wondered if the administration had also recognized the necessity of scaling computer science. Thus, you can imagine my surprise when I read the almost 2000-word AIED EO and saw that computer science was only acknowledged a measly three times.
While simultaneously absent, many facets of computer science education were strangely co-opted into this EO. As a thought experiment, re-read the AIED EO and replace “AI” with “computer science.” Most paragraphs still work. It’s almost as if the authors took language from CS initiatives, removed CS, and pasted AI in its place. Yet the disappearance of computer science, of which AI is a subset, from the EO wasn’t the only curiosity within the AIED EO.
Curiosity 2: Where are the CS Education Experts?
Let’s start with section 4 of the AIED EO. Participatory design, a design approach rooted in the 1960s Scandinavian labor movements and technology design, brings diverse stakeholders into the technological development process. Participatory design significantly influenced the development of the K-12 CS Framework through the Framework’s development process and its final product. For instance, creating the K-12 CS Framework was a community-led effort, involving individuals of various genders, experiences, ethnicities, states, school districts, companies, and nonprofits. Representatives from the Association for Computing Machinery, Code.org, the Computer Science Teachers Association, the Cyber Innovation Center, and the National Math and Science Initiative, all of whom possess extensive experience in CS education, led the development process.
In the spirit of participatory design, the AIED EO also calls for developing a task force of diverse stakeholders to establish a national AIED policy. However, the AIED EO designates representatives from the departments/agencies of Agriculture, Labor, Energy, Education, National Science Foundation, Domestic Policy, AI & Crypto, and the President’s office to lead this development work, rather than CS education experts. The AIED EO even calls on Michael Kratsios, former Chief of Staff to Peter Thiel and current Director of the Office of Science and Technology Policy, to lead this task force.
Curiosity 3: What type of Critical Thinking?
Section 6 allocates funds…. not to K-12 CS education… but to K-12 AI education. As referenced earlier, such AIED initiatives are already underway under the umbrella of CS, like those compiled by the AI4K12 initiative. Unlike the task force called for in the AIED EO, the AI4K12 initiative is a joint effort led by AAAI, one of the oldest scientific organizations in AI, and CSTA, the largest coalition of computer science educators. Interestingly, this section specifies that AI education should teach “foundational AI literacy and critical thinking skills,” skills already taught through the K-12 CS Framework and the AI4K12 initiative. Those critical thinking skills also include evaluating the appropriateness and necessity of computational approaches/tools and encouraging advocacy to push back when necessary. This may even involve opposing tools mentioned in this section such as "AI-based high-quality instructional resources; high-impact tutoring; and college and career pathway exploration, advising, and navigation” especially since such tools have bias in the algorithms and their outputs.
Curiosity 4: AI to Improve Teacher Evaluation?
While section 7 focuses on scaling AI literacy in teacher training, such efforts are also already embedded in current CS integration teacher training initiatives. However, the language in this AIED EO curiously places the development of teachers' AI literacy AFTER specifying two narrow AI use cases in teacher training: (1) reducing time-intensive administrative tasks and (2) improving teacher training and evaluation. One may hope that teachers possess foundational AI literacy, derived from previously mentioned CS initiatives, BEFORE using AI tools to complete those two tasks. Such literacies would guide teachers’ ability to interrogate and identify embedded assumptions in AI tools before selecting and using them. The AIED EO’s reference to using AI to “improve teacher training and evaluation” also deserves closer examination. Although AI is already being piloted within teacher coaching, such as through video-tagging platforms like EdThena and Sibme or automated rubrics, these efforts are relatively new in the teacher training landscape. While statistical models to predict teacher effectiveness are not new (i.e., Washington, D.C.’s IMPACT System), when teachers and stakeholders were asked about the D.C. IMPACT system, stakeholders reported that it contributed to creating an “unhealthy environment of distrust, fear, and competitiveness in schools that trickles down into the classroom” (p.3). Before concluding that AI-integrated teacher evaluations are the way forward, a broader conversation is needed on whether this future direction is, in fact, desired.
Curiosity 5: WIOA Youth Formula Funds?
The last significant section of the AIED executive order prioritizes the development of apprenticeships in AI-related occupations across industries. While this begins with very broad language, this section then hones in explicitly on using “WIOA youth formula funds to help youth develop AI skills.” What are WIOA youth formula funds? According to the Department of Labor:
“WIOA Youth Program focuses primarily on out-of-school youth, requiring local areas to expend a minimum of 75% of WIOA youth funds on them. The program includes 14 program elements that are required to be made available to youth participants. WIOA prioritizes work experience through a 20% minimum expenditure rate for the work experience program element.”
These funds can facilitate paid and unpaid work experience, occupational job training, tutoring, leadership development, entrepreneurial skills, financial literacy training, and comprehensive mentoring. While this is an interesting funding model, it is especially intriguing in light of the WIOA's continuous funding challenges and “no direct evidence of effectiveness.” While I’m not suggesting that these programs cannot be practical nor arguing against the importance of AI-job related internships, I am concerned that the narrow focus of these directives might inadvertently affect the establishment of larger computer science and STEM-focused internships.
Curiosity 6: A Federal Approach to AIED
The most curious aspect of this AIED EO is that this administration, which has dismantled the Department of Education to push education back to the states, would even require a National AIED EO. This federal approach to promoting a narrowly interpreted CS education contradicts the administration’s broader priorities. So then, whose priorities does this AIED EO serve? In February, the Office of Science and Technology Policy issued a Request for Information from stakeholders for suggestions on shaping its AI action plan. A central theme was the need for clarity amid the various regulatory gaps regarding AI development and application across sectors and states. As Sean Roberts, VP of US Strategy at Code.org, told the audience during his breakout session at the CSforALL Summit in 2024, such a heterogeneous approach to state regulation makes it hard to provide comprehensive guidance to states to help them vet edtech. However, a clarified regulatory landscape also allows tech companies to push their products more easily, with fewer hoops to jump through. To better prepare students and teachers to think critically about these initiatives and tech products, the critical thinking skills delineated in the K-12 CS Framework and the AI4K12 initiative help.
Situating AI Education Back in Computer Science
While a federal AIED EO could help navigate this disparate landscape, situating the focus within the broader context of computer science education would offer a more balanced and holistic approach to AI literacy for both students and teachers. CS education experts possess the content knowledge and pedagogical sophistication necessary to implement AIED initiatives effectively. AI began in CS. CS education better contextualizes AI. Let’s ensure CS education and experts do not disappear from the larger AIED policy pushes.