Summary

AI and Special Education: Mitigating Bias and Improving Support for Learners

By Jon Fila | Next Generation Learning Challenges | September 15, 2025 Source: https://www.nextgenlearning.org/articles/ai-and-special-education-m...

Jon Fila’s “AI and Special Education: Mitigating Bias and Improving Support for Learners” offers a balanced, nuanced exploration of how large language models (LLMs) like ChatGPT can enhance special education when used thoughtfully. He challenges both the overhyped narrative of AI as a silver bullet and the fearful view that technology erodes teacher expertise. Instead, Fila presents AI as a strategic assistant—a tool that, when guided by skilled educators, can help close accessibility gaps and improve learning equity for students with disabilities.

A Shift in Assistive Technology

Fila begins by recounting his surprise upon hearing that a school district had allowed a student with an Individualized Education Program (IEP) to use ChatGPT as assistive technology. While the idea was groundbreaking, he realized it was a logical next step in a long history of integrating digital supports—spell checkers, Google Apps, iPads—into the classroom. However, he warns that accessibility has often been an afterthought in past technological rollouts, and LLMs are no exception. Without mindful use, these tools can reproduce or even amplify systemic inequities.

For students with 504 plans or IEPs, Fila sees immense potential if educators use AI to save time, enhance accessibility, and individualize support. But, he emphasizes, this requires professional judgment and ethical guardrails—not automation.

Saving Time Through Smarter Accommodations

Fila recalls rewriting Minnesota’s Manual of Accommodations to adapt it for online learning—an exhaustive process that could now be streamlined using an LLM. Teachers can paste lesson materials alongside a student’s list of accommodations to generate customized suggestions within minutes, then refine them.

He shares a vivid example from a decade earlier: a specialist for the visually impaired who spent 30 hours a week reformatting digital content for one student. AI, he argues, could drastically reduce such burdens, allowing teachers to focus on pedagogy rather than repetitive formatting.

Common accommodations that AI can help generate include:

• Step-based instructions and chunked content

• Reduced assignments focused on mastery

• Immediate feedback and self-quizzing tools

• Readability adjustments with supportive vocabulary

• Visual and structural cues (headings, lists, bolded keywords)

• Simplified, specific directions that minimize cognitive overload

By using AI to manage these logistical tasks, educators can reclaim time for relational and instructional work—where human expertise is irreplaceable.

Embedding Accessibility in Digital Curriculum

Fila stresses that few teachers are trained in digital accessibility standards such as WCAG 2.1. Yet, LLMs can bridge that gap. Teachers can paste lessons into a chatbot and request recommendations for accessibility compliance, or even generate HTML content ready for a learning management system. The AI can suggest descriptive hyperlinks, consistent navigation, and inclusive language—all of which improve usability for diverse learners.

This process, he notes, not only benefits students with disabilities but strengthens universal design for all learners.

Addressing Bias and Promoting Inclusion

LLMs are “bias engines,” Fila cautions—they reflect the data they’re trained on. Educators must therefore craft anti-bias prompts that direct the model toward equity and inclusivity. He offers detailed examples of how teachers can expand their “sphere of inclusion” by explicitly asking the AI to:

• Integrate multiple perspectives across race, gender, culture, and ability

• Use inclusive, anti-racist, and bias-free language

• Reduce cognitive load and promote warm, supportive pedagogy

• Ground recommendations in research-based frameworks like Universal Design for Learning (UDL) and social-emotional learning (SEL)

Testing prompts under different names or identities, he adds, reveals how bias can subtly shape AI responses—and why awareness is essential.

LLMs as Coaching Partners, Not Replacements

Fila advocates treating LLMs as collaborative partners rather than content generators. For students struggling with executive function or writing anxiety, educators can use AI to help them break down tasks, brainstorm prompts, or organize ideas. Teachers remain the decision-makers, but AI provides structure, empathy, and pacing.

He also recommends uploading handouts or frameworks—such as accessibility guides, SEL principles, or demographic notes—to anchor AI outputs in the educator’s professional values and context.

Ethical Guardrails

Fila closes with firm reminders: never input personally identifiable information (PII), use only district-approved platforms, and disable data training when possible. The promise of AI in special education lies not in automating instruction but in amplifying human care, expertise, and equity. When used mindfully, LLMs can become what he calls a “thought partner”—a tool that helps educators make inclusive, informed decisions for every learner.

Original Article

Citation: Fila, J. (2025, September 15). AI and Special Education: Mitigating Bias and Improving Support for Learners. Next Generation Learning Challenges. https://www.nextgenlearning.org/articles/ai-and-special-education-m...

Also found at Mentors.net https://mentors.net/articles-landing-page/

------------------------------

Prepared with the assistance of AI software

OpenAI. (2025). ChatGPT (4) [Large language model]. https://chat.openai.com