The AI Debate: Finding Our Common Ground in K-12 Special Education

By Richard Capone, CEO of Let’s Go Learn

https://asteriskmag.substack.com/p/common-ground-between-ai-2027-and?publication_id=2291516&post_id=178644398&triggerShare=true&isFreemail=true&r=1pg6hh&triedRedirect=true

I’ve been following the current AI debate closely. It’s fascinating, and a little unnerving, to watch two camps take such divergent views on the technology’s future. One side sees the dawn of a Superintelligence which would be a transformative event akin to the invention of fire, bringing a recursive explosion of capability. The other camp views AI as a “Normal Technology,” a powerful but gradual tool, much like the internet or electricity.

When it comes to K-12, and specifically Special Education, this debate feels particularly real. Is AI going to usher in a sudden, utopian era of perfectly tailored IEPs and instruction? Or is it just another tool that will slowly integrate into our existing, often-strained system?

That’s why I found the recent article, “Common Ground Between AI 2027 and AI as Normal Technology,” so insightful. It forces the two opposing views to find agreement, and in doing so, it provides a realistic blueprint for how we should be moving forward in education right now.

The core takeaway is this: Regardless of whether you believe in a rapid AI “takeoff” or a slow, measured evolution, our near-term actions must be the same.

The authors of the article—who disagree wildly on the long-term future—managed to agree on a dozen key points. I’ve pulled them out here and reflected on what they mean for us in Special Education:

1. Before strong AGI, AI will be a normal technology.
   • SPED Implication: This is exactly how we frame AI at Let’s Go Learn. It is a powerful tool to augment the teacher, not replace them. It’s helping our educators translate diagnostic data into polished PLAAFPs and SMART goals faster than ever, but it is always teacher-led and bottlenecked by the real-world process of implementation.
2. Strong AGI developed and deployed in the near future would not be a normal technology.
3. Most existing benchmarks will likely saturate soon.
   • SPED Implication: This is key. We know AI will master standardized tests and isolated tasks (like solving SWE-Bench). But that doesn’t mean it can seamlessly manage the complex, long-tail (unique use cases) requirements of a diverse special education caseload. High scores don’t equal real-world utility in a human-centric field.
4. AIs may still regularly fail at mundane human tasks; Strong AGI may not arrive this decade.
   • SPED Implication: We expect AIs to still make spectacular, “long-tail” errors, which is why a human review of every output is non-negotiable. You can’t let an algorithm—no matter how good its average performance—book a student to the wrong intervention or write a legally non-compliant IEP without a human check. The teacher-student relationship remains the high-assurance setting.
5. AI will be (at least) as big a deal as the internet.
   • SPED Implication: AI is a General-Purpose Technology (GPT). It will fundamentally redefine personalization. By automating paperwork, it frees up teachers to focus on the human connection and the art of teaching—a profound, long-term shift for education.

Actionable Policy & Safety (What We Must Do Now)

1. AI alignment is unsolved.
   • SPED Implication: We must treat every AI output as potentially misaligned. This means using a “Context AI” approach that grounds the AI’s suggestions in validated, objective student diagnostic data and the teacher’s professional wisdom. We must control the inputs and the outputs. Also unguided access by students probably should be avoided. 
2. AIs must not make important decisions or control critical systems.
   • SPED Implication: AI should assist the teacher in drafting an IEP, but it must not be given the final decision-making power over a student’s educational plan or placement. The teacher and the IEP team hold the ultimate authority.
3. Transparency, auditing, and reporting are beneficial.
   • SPED Implication: Transparency is critical for compliance and trust. The data used to train AI models must be ethically sourced, and the algorithms used to generate documentation should be auditable to ensure fairness and compliance with IDEA.
4. Governments must build capacity to track and understand developments in the AI industry.
   • SPED Implication: Regulatory bodies and state DOEs need to quickly develop the technical expertise to set safe standards for AI tools used in special education.
5. Diffusion of AI into the economy is generally good.
   • SPED Implication: We should embrace AI’s ability to reduce the administrative burden. Our data shows that educators can easily save 4-6 hours a week on documentation and data analysis alone. This time goes back to student support—that is the very definition of “good diffusion” in our industry.
6. A secret intelligence explosion, or anything remotely similar, would be bad, and governments should be on the lookout for it.
   • SPED Implication: The focus must be on openness and safety-by-design. No “black box” solutions that hide how they process sensitive student data. Seek vendors that use AI safely.

Ultimately, the argument for both sides falls apart if you can’t translate the technology into positive action. Whether AI is the new fire or the new internet, the immediate job is to empower the teacher with precision and efficiency.

In Special Education, AI’s power is in its ability to take the wealth of diagnostic data we collect along with context and quickly turn it into actionable, individualized instruction and compliant documentation. It is the bridge between data and deployment.

The human factor, the special education teacher’s wisdom, empathy, and oversight, will always be the most critical piece. AI won’t replace that wisdom, but with the right context and the right diagnostic data, it can make individualized education more achievable than ever before.