Stewart Brown · Research note · October 3, 2026
Learning to understand the technology around us.
Schools make rules about technology. Children also need the knowledge and judgment to understand how it works, question its outputs and make decisions for themselves. This tracker connects those two responsibilities.
My research question is: What do policy and implementation evidence tell us about children’s opportunities to understand and shape technology? I focus on foundational computer science, AI and digital literacy, especially in elementary and middle school. Career pathways matter, but understanding algorithms, data, systems and their effects also matters in everyday life.
Start with the exact learning requirement.
A state can publish standards, require schools to offer a course, require students to receive instruction, or require a credit for graduation. Each creates different planning questions. This collection separates those requirements and keeps implementation dates alongside them.
| Example | Documented requirement | Implementation question |
|---|---|---|
| Iowa | CS instruction in at least one grade from 1–6 and in grade 7 or 8; a high-school offer-and-teach requirement. | How do the locally selected grades connect into a coherent sequence? |
| Tennessee | Elementary integration, a middle-school grading period, and a diploma credit starting with the class of 2028. | Who teaches each stage, with what preparation and assessment? |
| Nebraska | K–12 instruction from 2025–26; annual reporting from December 2026; graduation credit from 2027–28. | What will the first progress report show about the preceding year? |
| Maryland | AI literacy in workforce and K–12 CS standards by June 1, 2027, alongside an educator-PD requirement. | How will standards become age-appropriate lessons and teacher support? |
Policy findings come from the linked official sources below. Implementation questions are my interpretation; they do not assert a legal requirement or a measured gap in delivery.
Learning about AI and using AI are separate questions.
AI literacy includes examining data, recognizing limitations, evaluating outputs and considering effects on people. Those concepts can be taught through discussion, models, unplugged activities and computing projects. A rule about access to a chatbot does not, by itself, establish what students are taught.
New York City’s published district guidance illustrates the distinction: it restricts student-facing generative AI in younger grades and requires two 45-minute AI-literacy modules for high-school students. That is a district example, not a New York statewide learning mandate. The policy page establishes the requirement; it does not establish completed instruction or student understanding.
North Carolina’s future AI provisions require K–12 standards and alignment of covered CS courses for 2028–29. This is a specific standards and course-content requirement. Treating it as a separate AI course required of every K–12 pupil would overstate the text.
Look for the conditions that make instruction possible.
For a curriculum leader or instructional coach, the next step is a delivery plan: time in the timetable, a progression across grades, teachers who understand the content, accessible materials and evidence of student learning.
The support register gives concrete starting points. Iowa publishes current CS teacher-development grant deadlines. New Jersey documents free regional CS Hub support through August 2027. Massachusetts publishes a district implementation process and a reviewed curriculum guide. These are different forms of support. A free service is not a district allocation, and an authorized grant program is not proof that awards are currently available.
- Sequence: Identify where students meet computing systems, algorithms, data and digital judgment, and how those ideas develop across grades.
- Teachers: Name who teaches each part, including generalist K–8 teachers, and provide preparation time and support.
- Learning evidence: Examine student explanations, designs, projects and reasoning alongside participation and completion.
- Access: Check whose timetable includes learning, whose work is visible, and which accommodations make participation possible.
- Resources: Separate a legal requirement, a support program, an actual award and the local cost of delivery.
These are an editorial planning framework. The tracker does not yet establish that any state has achieved universal, high-quality delivery.
How this research relates to Rob Dickson’s.
Rob Dickson’s research provides a wider school-system leadership lens, with policy studies and implementation examples. He also covers CS and AI literacy. His restriction research is a useful discovery and comparison source.
This tracker’s distinct contribution is a repeatable, grade-specific connection between learning requirements, relevant rules for use and classroom delivery conditions. Its emphasis is K–8 foundations, teacher capacity, cross-curricular learning and children’s agency. Official law, agency guidance and district documents control the policy findings. Rob’s categories and scores are not imported as this tracker’s findings.
Sources and scope
This dated release contains 230 action records and a separate 51-jurisdiction device register. These counts describe the collection. They do not establish a complete census of laws, district practice or student outcomes. Missing evidence remains a research gap.
- Iowa DOE · HF2629 instruction and requirements guidance
- Tennessee DOE · CS legislation overview and timeline
- Nebraska DOE · September 2026 CS and technology guidance
- Maryland · Chapter 634, Education 7-2203
- NYC Public Schools · AI and screen-time guidance
- North Carolina · S.L.2026-41 §7.39
- Teacher support and funding register · sources and review dates (CSV)
Author disclosure: Stewart Brown’s professional work includes K–8 CS and AI literacy at Code4Kids. That interest informs the research question. This is personal research; the cited sources and stated method govern the findings.