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AI for Special Education: Opportunities, Best Practices, and Ethical Considerations

AI is reshaping special education-automating paperwork and unlocking new ways to personalize learning—but only with the right safeguards for privacy, accuracy, and equity.

AI is reshaping special education-automating paperwork and unlocking new ways to personalize learning—but only with the right safeguards for privacy, accuracy, and equity.

Artificial Intelligence (AI) is making rapid inroads into classrooms, and special education is no exception. Educators and researchers are increasingly exploring how generative AI tools can streamline paperwork, personalize learning, and support students with disabilities in new ways. With over 7.5 million public school students (15% of enrollment) having Individualized Education Plans (IEPs), this creates a labor-intensive process for special educators. Special education teachers face mounting challenges managing growing caseloads, as federal guidance becomes less clear, concerns surrounding compliance intensify, and student achievement gaps continue to widen despite record spending on intervention support during and following ESSER.

Experts believe that AI can help teachers draft higher-quality IEPs more efficiently, thereby easing burnout and freeing up time for direct student support. However, because every learner is unique, relying too heavily on AI that cannot distinguish between strong and weak learning design can introduce significant risks in special education. Since it’s so easy to instantly receive a response that “looks good” to a passive or less knowledgeable audience, AI could accelerate complacency and even lead to less careful attention to individual student needs.

In this article, we’ll explore how AI can assist special educators – from crafting more effective IEP goals to enhancing parent communication – and discuss best practices and safeguards (such as grounding AI responses) to ensure educators use these tools responsibly and effectively.

Streamlining IEP Development with AI Tools

One of the most promising applications of AI in special education is helping teachers create high-quality IEP documents more efficiently. IEPs are detailed documents that set individualized academic and behavioral goals and describe the services and instruction a student will receive. Crafting these plans is vital, but it is also a time-consuming process. Final IEPs can run 30 pages or more, reflecting extensive progress monitoring and compliance details. Generative AI can serve as a tireless assistant in this process. AI-driven writing tools can quickly generate drafts of Present Levels of Academic Achievement and Functional Performance (PLAAFP) summaries or suggest language for measurable goals and objectives based on teacher input.

Early evidence suggests AI-assisted IEP writing can enhance the quality of goals. In one preliminary study, special education teachers who used ChatGPT to assist in writing IEP goals for preschoolers with autism produced goals that were rated significantly higher in quality (average scores of ~9.1 out of 10) than those written without AI (scores ranging from ~5.5 to 9.2). AI was able to draw on a vast repository of knowledge (e.g., examples of age-appropriate, standards-based goals) to provide teachers with well-structured draft goals.

AI can also offer fresh ideas: it might suggest a novel instructional approach or an objective that the teacher hadn’t considered, expanding the team’s repertoire. “AI can find new ideas for student goals and instructional approaches that the educator might not have otherwise considered,” notes Andrea Harkins-Brown of Johns Hopkins University’s Center for Technology in Education. This creative brainstorming aspect can be valuable, especially for veteran teachers who may be writing their hundredth IEP and want to avoid cookie-cutter goals.

However, human expertise and oversight remain essential. Educators widely agree that AI is “a collaborator, not a replacement” for teacher judgment. AI might draft a present level statement or propose a goal, but it’s the teacher (in consultation with the IEP team) who must vet it, personalize it, and ensure it’s appropriate. “Only a human can ensure the IEP substantively matches the student’s needs and addresses them in detailed, tangible ways,” cautions Tessie Bailey of the American Institutes for Research. In practice, this means any AI-generated content should be carefully reviewed, edited, or discarded as needed. Harkins-Brown emphasizes that all AI outputs must be scrutinized with a critical eye and verified for accuracy and compliance. For example, if an AI tool suggests measuring progress over 3 months, the teacher might adjust it to a 6-month timeframe based on the school’s reporting schedule. The IEP team must also ensure the AI’s suggestions align with IDEA regulations and state standards (general-purpose AI tools might not automatically reference legal requirements, such as including parent input or transition plans).

Best Practices for AI-Assisted IEP Writing

What does using AI as an “IEP assistant” look like in practice? One approach is using structured prompts to guide the AI. For instance, the nonprofit AI for Education provides a prompt template to generate IEP goals that are Specific, Measurable, Attainable, Relevant, and Time-bound (SMART). The prompt asks the AI to act as an “expert special needs teacher” and write a goal for a student, given the grade level, subject area, a brief description of the skill or topic, and the desired timeframe. By filling in those details (e.g., “a 2nd-grade student learning capitalization rules, with 3 months to achieve the goal”), teachers can obtain a draft goal that is already in a SMART format and then adjust it as needed. The AI for Education prompt guide also suggests follow-up questions to refine the output, such as asking the chatbot for lesson plan ideas to practice that specific skill, or ways to integrate the goal into other subjects. This iterative prompting is a best practice that treats AI as an on-demand brainstorming partner. Teachers can even use AI to adjust goals over time: for instance, if a goal is met quickly or proves too difficult, one can prompt the AI to modify the goal or generate next-step objectives to ensure continuous progress.

Additionally, some educational AI platforms are designed with IEP development in mind. At Lessi, we begin with a pre-made system prompt that serves as the foundation for all other user input fields. Here’s one example system prompt we use in the Lessi AI Classroom:

Example Lessi AI System Prompt

You are a special education IEP assistant. Your role is to help educators develop comprehensive, individualized education plans for K–12 students with disabilities. Use a friendly, collaborative tone and maintain rigor by basing your guidance on evidence-based best practices.

  • Gather key student information (e.g., name, age, grade, disability category, and evaluation results) to understand the diagnostic profile and factors qualifying the student for services.
  • Start with the student’s strengths and current performance. Summarize Present Levels of Academic Achievement and Functional Performance (PLAAFP) using data from assessments and observations, highlighting what the student can do and areas of need.
  • Develop measurable annual goals aligned with the identified needs. Ensure each goal is SMART (Specific, Measurable, Achievable, Relevant, Time-bound) and linked to state standards or curriculum when appropriate. Clearly connect each goal to baseline data from the present levels.
  • Recommend special education services, related services, and accommodations/modifications tailored to the student’s disability and needs. Include details like frequency, duration, and setting of services. Ensure these align with the least restrictive environment (LRE) principle so the student can participate in general education as much as possible.
  • Include a plan for progress monitoring: describe how the student’s progress toward each goal will be measured and reported (e.g., weekly probes, monthly reports to parents).
  • Ensure the IEP is compliant with legal requirements (IDEA) and reflects the best practices recommended by the Council for Exceptional Children (CEC) and CASE. For example, document parent/guardian input and concerns, and include transition planning if the student is age 16 or older.
  • Tailor the IEP content to the student’s specific diagnosis, needs, and learning profile. For example, if the student has Autism Spectrum Disorder, consider supports for communication and social skills (e.g., visual schedules, speech therapy) and consider sensory accommodations. If the student has a reading need, include evidence-based reading interventions, appropriate accommodations (such as audiobooks or extra time), and goals targeting reading skills. Adjust your recommendations based on any other qualifying factors (such as English language learner status or health issues) to ensure the plan is truly individualized.
  • Maintain a positive, supportive tone throughout your assistance. Use teacher-friendly language and explain any technical terms or acronyms (like FBA, PLAAFP, etc.) so that all IEP team members, including parents, can understand. Encourage collaboration and celebrate the student’s progress and potential, while remaining honest and precise about their needs.

When prompts contain mentions of specific organizations or practices, AI search prioritizes the most relevant grounding documents related to those terms. Educators are then able to customize these prompts and document guidance based on their specific approach and key priorities. In another article, we’ll discuss the role of agents and how they fundamentally change how we need to think about AI in education… already.

Aligning Instruction and Interventions to IEP Goals

Writing a strong IEP is just the beginning – the real challenge is implementing it through effective instruction and supports. AI can also assist here by helping educators connect day-to-day teaching practices to each student’s IEP goals. Teachers have reported success using AI to produce custom learning resources on the fly. Matthew Marino, a special education technology researcher, provides an example: when he needed a graphic organizer to help a student compare plant and animal cells, he prompted an AI tool, and “within 15 seconds, I had a beautiful graphic with everything I wanted for the student.” Finding or creating such a resource manually might have taken him 15 minutes or more, a time that is often unavailable in a busy classroom. By offloading that task to AI, he could spend those minutes working directly with the student – a clear win for instructional alignment and efficiency.

AI can also help ensure that interventions remain closely tied to the student’s goals and data. In practice, special educators should frequently monitor each student’s progress toward IEP objectives and adjust instruction accordingly. However, in reality, numerous programs generate data, and few of these programs coordinate their analytics or support with one another. Some districts have experimented with using AI to analyze large sets of student data (e.g., IEP progress reports, assessment results). In one case, administrators utilized a GPT-based “data analyst” tool to aggregate and summarize IEP service data from 100 case managers – a process that typically took three months of manual data entry. However, with AI, the task was completed in just three days, even generating narrative reports with charts to inform staffing decisions. On a smaller scale, a teacher could use AI to quickly identify patterns in a student’s performance (for instance, by feeding in anonymized quiz scores or behavior logs) and get insight like, “Student performs better on reading comprehension questions when given a graphic organizer”. Armed with that information (which the teacher might have eventually seen herself, but AI can surface sooner), the educator can adjust the instructional plan, perhaps introducing graphic organizers regularly or trying a different approach if something isn’t working.

AI-enhanced assistive technology.

Modern AI tools now support communication and accessibility more effectively than earlier software. For example, traditional speech-to-text applications often failed to accurately transcribe input from students with complex communication needs or atypical speech patterns. AI-driven systems like Voiceitt can learn an individual student’s speech over time, deciphering words that others might not understand, and translate it into clear text or voice output. Similarly, AI-powered text-to-speech readers (such as Microsoft’s Immersive Reader) can read text aloud, adjust speed, highlight words, and even explain terms to the listener.

Enhancing Communication and Collaboration with AI

Effective special education isn’t just about what happens in the classroom — it’s also a collaborative endeavor involving teachers, parents, administrators, specialists, and paraprofessionals. AI can streamline many of these interactions:

  • Plain-language summaries: AI tools can automatically translate an IEP or progress report into clear, everyday summaries for families.
  • Translation services: AI translation tools enable educators to produce accurate versions of key documents in languages such as Spanish, Somali, or any language needed.
  • Just-in-time guides for paraprofessionals: A one-page reference guide generated by AI can help paraprofessionals implement new behavior support plans or accommodations correctly.
  • Meeting preparation and follow-up: AI can draft meeting agendas for IEP meetings (ensuring all required topics are listed) or summarize meeting notes afterward for distribution. AI for Education’s prompt library even includes a “Summarize Meeting Notes” template.

Ethical and Privacy Considerations for AI in Special Education

With all the excitement surrounding AI, it’s essential to address two of the most significant concerns: privacy and accuracy.

Privacy and Data Security

Special education records and IEP details are highly sensitive. They include personally identifiable information (PII) and details about a child’s disability, services, and medical and developmental history – data protected by laws such as FERPA and IDEA. Using a mainstream AI chatbot (like the free version of ChatGPT) for IEP work poses a serious risk because these platforms “absorb all of the data [they receive]” into their models. Entering real student information into a public AI service could even violate confidentiality laws or district policies. Educators must therefore never input student names, identification numbers, or any uniquely identifiable data into an AI tool that uses student and user data to train or refine its models. Some teachers, eager to try AI, have attempted workarounds, such as using fake names for students in their AI prompts. While pseudonyms might conceal identity to a degree, CEC’s experts warn that this does not eliminate the privacy risk – the AI provider could still pick up fragments of real data or context. The safest route is to use AI platforms that guarantee data privacy (e.g., tools that run locally or under a district-controlled account, or those specifically designed with education data protections).

Mitigating “Hallucinations” and Ensuring Accuracy

Generative AI sometimes produces information that is convincing-sounding but entirely false or unfounded—a phenomenon dubbed hallucination. In special education, an AI might fabricate a statistic (e.g., “students with dyslexia improve reading by 80 % with XYZ method”) or misquote an IDEA regulation, which could mislead educators or families if taken at face value. To mitigate this, grounding AI responses in reliable sources is key. Whenever possible, use AI tools or modes that can cite sources or work off a provided knowledge base. For instance, instead of asking a chatbot broad questions, one might provide the AI with a trusted article or a list of evidence-based practices and ask it to summarize or adapt those for the student. This extra verification step is critical to safe, accurate use.

Bias and Equity

AI models learn from data that may contain biases, and special education is a context where equity is a paramount concern. One issue, as researcher Matthew Marino notes, is that most AI providers have primarily trained their models on data from neurotypical students and mainstream educational practices (which may not always align with our needs). There’s far less data on students in special education, and there’s no data on the needs of a specific learner. This imbalance could lead to AI tools that don’t perform as well for students with disabilities or that overlook critical student needs. Using AI in special education should never result in lowering expectations or reinforcing stereotypes. All students are capable of growth, and AI suggestions should be filtered to ensure they align with the high expectations and inclusive ethos of special education practice, as well as increasingly, the law.

Equity also means ensuring that the benefits of AI don’t become available only to some. School leaders and policymakers must be aware of the “digital divide.” Wealthier districts might rapidly adopt AI tools, while under-resourced schools struggle to access them, potentially widening outcome gaps. As noted earlier, stakeholders worry about leaving some students and schools behind if AI becomes a new educational advantage. To address this, advocacy is needed so that proven AI tools (especially those that could significantly reduce teacher workload or improve student access) are funded and shared widely, not just in geographic pockets.

The Role of the Teacher and Ethical Use

A recurring theme from experts is that AI should augment—not replace—the human elements of special education. Relationships, professional judgment, and individualized care remain at the heart of effective practice. Teachers must continue to scaffold and motivate students, even if AI helps generate materials or feedback. Similarly, students should learn to use AI responsibly—such as using a spell-checker or grammar suggestion—without bypassing fundamental skill development. Transparency is also crucial: if AI drafts part of an IEP or parent letter, educators should note it and, where appropriate, obtain parental notification or consent when AI tools are used directly with students.

A recurring theme from experts is that AI should augment, not replace, the human elements of special education. Relationships, professional judgment, and individualized care are at the heart of special education. Teachers must continue to scaffold and motivate students, even if an AI is helping to generate materials or provide feedback. Similarly, students should be taught to use AI (when appropriate) as a support for their learning – for example, using a spell-checker or a grammar suggestion from an AI – but not to bypass learning the skills themselves. An ethical approach also involves transparency: if AI is used to draft part of an IEP or a parent letter, it’s wise for educators to be open about it in case it raises questions or concerns. Additionally, districts may consider obtaining parental consent or, at the very least, providing notification when AI tools are directly used with students (such as an AI tutoring program that collects student data), just as they do for other educational technologies.

Conclusion: Embracing Responsible AI for Special Education

AI has the potential to be transformative for special education. This field faces chronic challenges, including burdensome paperwork, severe staffing shortages, and the increasingly complex task of individualizing learning for every student and reporting that activity to families. By lightening the load of administrative tasks, AI can give teachers and specialists more time and energy to focus on delivering high-quality, responsive instruction and support.

As we integrate AI into special education, we must continuously refer to the evidence base and ground AI tools in the right data. It also means building strong safeguards against the risks of using AI with vulnerable populations – guarding against privacy breaches, ensuring accuracy, and promoting equitable access to technology. The hope is that with AI handling some of the heavy lifting, educators can spend more time doing what no AI can: building relationships, inspiring students, and tailoring learning with professional insight and heart. In doing so, we ensure that AI in special education truly lives up to its potential as a force multiplier for good, helping every student reach their goals in a supportive, well-informed, and ethical educational environment.

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