Is the "AI Fever" Really Breaking? The EdWeek AI Survey: Two Competing Interpretations

Nick Potkalitsky

Nov 25, 2024

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Two Kinds of AI Literacy

As I wrap up for the holiday break (amid podcast recordings and AI trainings), I've been thinking about Lauraine Langreo's recent Education Week piece, "'We're at a Disadvantage,' and Other Teacher Sentiments on AI." What caught my eye was Dan Meyer's take on it. Meyer, a prominent ed-tech critic, sees the data showing flat AI usage despite increased training as evidence that the "AI fever" in education is breaking.

But the numbers tell a different story. Two years after ChatGPT's release, 58% of teachers still lack any AI training. When training does happen, it's usually a one-off session - only 6% receive ongoing support. Meanwhile, students forge ahead with these tools. As one Pennsylvania special education teacher noted: "As a special ed teacher who has to help students across 20+ curriculums... I frequently ask [ChatGPT] for background on pieces of literature, summaries of historical events."

The data doesn't reveal AI's limitations - it exposes our failure to properly support teachers.

The Core Data Points

From Education Week's October 2024 survey:

• 43% of teachers received AI training (up from 29% in spring)
• 58% still have no AI training
• Usage rates remain flat: 32% using AI vs 31% in December 2023

Dan Meyer's Critical Reading

Meyer zeros in on a striking paradox in the data: "there has been a 50% increase in teachers getting AI training since December, all while AI usage has remained flat, all while the models have improved, all while more states and districts have crafted AI policies."

His interpretation focuses on teacher resistance as reasoned rejection rather than implementation failure. As he notes: "Only one reason for not using AI INCREASED: 'I don't think the technology is applicable to my subject matter or grade.'"

Meyer concludes this indicates "this fever will break" and sees it as an opportunity for edtech to "get back to making tools that DO feel applicable to teachers and learners."

A Different Reading: The Growing Digital Divide

The article provides compelling evidence for a more concerning interpretation:

1. Teacher Disconnection Direct quote from the article: "I would really like to have some in-depth training on the use of some of the new educational AI tools," said a high school math teacher in Colorado. "Our district has not provided anything at all and it is too expensive to pursue on my own."
2. Competing Priorities The article notes a high school social studies teacher in Ohio wants to learn more about AI but their district is focused on the "science of reading." Their stark assessment: "I feel that we are at a disadvantage."
3. Limited Current Usage When teachers do use AI, the article shows it's primarily for basic tasks. A Maine middle school science teacher reports "using AI to assist me in creating goal setting for my academic advising." While valuable, this represents a fraction of AI's potential.

Why Meyer's Reading Misses Critical Context

Meyer interprets flat usage despite increased training as evidence of AI's limited utility. However, this overlooks:

1. The Quality of Training: The article notes 24% received just one session, with only 6% getting ongoing support. This suggests the "50% increase" in training may represent shallow exposure rather than meaningful professional development.
2. Student Adoption Reality: While Meyer celebrates declining interest as natural, he overlooks that students continue adopting these tools independently. As the EdWeek data shows, teachers' top reason for non-use is "lack of knowledge" - not reasoned rejection.
3. Implementation vs Inherent Value: The increasing sentiment that AI isn't "applicable" to certain subjects may reflect inadequate subject-specific training rather than actual limitations. Without targeted, discipline-specific training that demonstrates concrete applications within math, science, language arts and other subjects, teachers understandably struggle to see AI's relevance to their daily practice.

Moving Forward: From Digital Divide to Digital Bridge

The EdWeek data reveals not AI's limitations but a growing crisis: while students forge ahead with AI adoption, many teachers remain unsupported and uncertain. Meyer's "breaking fever" narrative misreads teacher hesitation as reasoned rejection rather than what the data actually shows - systemic failure in professional development.

As the Pennsylvania special education teacher demonstrated, meaningful support leads to valuable applications. The path forward requires immediate action on three fronts:

1. Sustained Learning Communities

• Replace one-off sessions with ongoing professional development
• Create subject-specific pathways guided by early adopters
• Build training around real classroom scenarios students already face

2. Bridge the Digital Divide

• Study and respond to current student AI usage patterns
• Create shared exploration spaces for teachers and students
• Develop practical AI literacy frameworks across subjects

3. Remove Systemic Barriers

• Integrate AI training into existing professional development structures
• Share resources and best practices across districts
• Create policies that encourage rather than restrict thoughtful integration

The "fever" isn't breaking - we're watching a digital literacy gap widen while celebrating teacher disconnection as vindication. The question isn't whether AI belongs in education - students have already answered that. The question is whether we'll help teachers guide this transformation or leave them, and ultimately their students, to navigate it alone.

Nick Potkalitsky, Ph.D.

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