TIES TIPS Foundations of Inclusion

Introduction

Collaboration is a fundamental component of effective instruction in inclusive classrooms (McLeskey et al., 2022). When general and special educators plan together, students with disabilities are more likely to experience

• Higher engagement in grade-level content alongside their peers, and
• More interaction with general education teachers (Kuntz & Carter, 2021).

Collaboration requires time. Finding time to collaborate is one of the most persistent challenges inclusive teams face. Co-planning meetings are often short, infrequent, or squeezed out by other demands. When collaboration time is scarce, student engagement may be impacted - for example, when a lesson gets taught without agreed-upon supports, a student with extensive support needs ends up observing rather than participating.

Generative artificial intelligence (GenAI) tools are widely available to educators. These tools can produce written text, summaries, and plans in response to a prompt from the user. In collaboration, GenAI is most useful for reducing the time-consuming preparation that happens before a meeting, especially when idea generation and initial drafting often use up the first 15 minutes of a team meeting. That frees teams to spend their limited time together on using professional judgment about the plan that only people who truly know the students and have the content expertise can do well.

This TIP introduces collaborative teams to what these GenAI tools can do, what they cannot do, and how to use them in ways that strengthen inclusive practice.

What Can AI Tools Do?

GenAI tools produce content in response to a written prompt. Tools like ChatGPT, Gemini, Claude AI, and Microsoft Copilot are examples of large language models that are available to many educators. These tools draw on patterns from large amounts of information pulled from across the internet to produce responses that can sound natural and specific to a topic. In addition, AI-powered tools like Ludia (askludia.com ) or Goblin (https://goblin.tools/ ) pull from several large language models, but are trained to do a very specific task. For example, they can assist with specific tasks like generating to-do lists or providing ideas for lessons based on Universal Design for Learning guidelines.

None of these tools knows specific students the way teachers understand their students, and all GenAI output should be evaluated by the team with knowledge and expertise of their students and instructional practices. GenAI tools generate responses based on patterns in their training data. That means GenAI responses can sound fluent and confident even when they are incomplete, generic, or totally wrong. The U.S. Department of Education describes GenAI tools as a form of intelligence augmentation, technology that extends what educators can do, not technology that replaces what educators know (2023).

Many school districts now provide "Enterprise" versions of GenAI tools that are more secure than the free versions. Use these for greater protection of student confidentiality and shielding of information.

There are several areas where GenAI can save teams time. When preparing for a co-planning meeting, one or both teachers often need to organize or draft materials in advance. This might include

• an outline of an upcoming unit,
• a list of barriers a student might face, or
• possible accommodation options.

GenAI can generate these drafts quickly when given clear information about the content and the student's needs. Teams using planning tools like the 5-15-45 can use GenAI to fill in initial responses to the guiding questions. The team then reviews and refines those responses together. This part matters most. GenAI does not replace teacher expertise.

If the first GenAI response isn’t useful or is incomplete, adjust your prompt and try again. Small changes in wording can lead to very different results. For example, if you initially asked how a nonspeaking student could participate in a lesson on photosynthesis, and the results were not helpful, you could try a more descriptive prompt, like:

Vague Prompt	Specific Prompt (De-Identified)
"How can a nonspeaking student participate in a lesson on photosynthesis?"	"I am a 5th-grade general education teacher teaching a diverse, inclusive class where all students learn based on my state's 5th-grade science standards. I am planning a lesson on photosynthesis. Suggest three ways a student who uses a 20-location AAC device could actively participate in the 'Observe and Record' portion of the lab."

With GenAI, teams can also leave planning meetings with initial AI-developed drafts of tasks that need to be completed - notes, parent updates, language translations for multilingual learners, or a progress summary for an upcoming IEP meeting. Educators then review and finalize rather than write from a blank page. Research has found that educators value GenAI tools in part because they reduce the load of routine compliance tasks, and IEP documentation is one of the clearest examples of where that load adds up (Waterfield et al., 2025).

Team Expertise Matters More Than AI Fluency

The general educator's content knowledge and the special educator's knowledge of specially designed instruction and inclusive practice must both shape any GenAI output before it is used. GenAI output is only as useful as the professional judgment applied to it. Collaborative teams bring knowledge that no GenAI tool has: knowledge of the student, the classroom, the curriculum, and the family's priorities. GenAI should be treated as a first draft, not a finished product.

The U.S. Department of Education (2023) recommends keeping a human in the loop when GenAI is used in educational settings. For instructional teams, this means reviewing every GenAI suggestion together. One practical approach is for one team member to create the GenAI draft and share it with the full team before the meeting. The team then uses their time together to confirm, adjust, and finalize rather than generate content on the spot. This is faster than drafting from scratch and maintains shared ownership of the plan.

Bias and Equity in AI Output

Using GenAI for teaching-related work raises important equity concerns. GenAI tools are trained on large amounts of text that may perpetuate limited expectations of students with disabilities, multilingual learners, or students from culturally and linguistically diverse backgrounds (Imada, 2024). Ableist thinking may also be evident when the focus is on the student’s disability and what they cannot do, rather than on their strengths and how barriers to learning can be eliminated. As a result, output can reflect dominant norms rather than the full range of student experiences. The U.S. Department of Education (2023) has warned that GenAI trained on non-representative data may produce unfair or exclusionary results. Generic GenAI-generated language may sound professional, but it can miss what makes a student's plan individual and equitable.

Teams should actively check for the following:

A quick bias check during team review can catch generic or one-size-fits-all language before it ends up in a plan. If a suggested goal or accommodation could apply to almost any student or seems to be written from a medical perspective, it probably needs revision. For example, a suggested goal might sound appropriate but be written in a way that emphasizes deficits rather than active participation or strengths.

Ask your GenAI tool to develop output using a 'strengths-based, person-first perspective.'

Protecting Student Privacy

Student privacy is a key concern. IEPs contain sensitive personal and educational information protected under the Family Educational Rights and Privacy Act (FERPA) and the Individuals with Disabilities Education Act (IDEA). Many GenAI tools use cloud-based systems that may store or share data in ways that are not fully transparent. Entering a student's name or other identifying details into a general-purpose GenAI tool may violate FERPA and expose the school to legal risk (Coleman & Waterfield, 2026).

There is a straightforward way to reduce this risk. Use descriptive, de-identified language in any GenAI prompt. Instead of using a student's name and label, describe the student in general terms — for example, "a 6th-grade student with an intellectual disability who uses AAC and is working on science vocabulary." This gives the GenAI tool enough context to produce a useful draft without exposing student data. Teams should also check with their district about which GenAI tools have been reviewed and approved for use, and specific policies or guidelines about what information can be shared when using GenAI for lesson planning.

Never upload an actual IEP file or type a student's name, birthday, address, school, or district into a general-purpose GenAI tool

Try It as a Team

Pick one upcoming instructional activity that usually requires a lot of writing time, such as completing a unit planning template, drafting a progress summary, or a family update. Follow these steps together.

• Step 1: Identify the Task and Gather Key Information. Begin planning based on the grade-level general standards and the curricular unit being taught in the general education classroom. This would include the unit topic, key concepts, essential vocabulary, the student's relevant IEP goals, and any supports or accommodations that are already in place.
• Step 2: Idea Generation. Assign one team member to write the prompt. Use de-identified language. Do not include the student's name, school, or other identifying information. Describe what you need in specific terms — for example, "Suggest three ways to embed a vocabulary goal into a 7th-grade science unit on ecosystems for a student who uses picture-based communication. The essential vocabulary terms for this unit are: ______________________. Generate possible visuals for each of these vocabulary terms."
• Step 3: Collaboration and Human Review - Share the AI output with the full team at least the day before your next meeting, and use your meeting time to revise together. Read it and revise with these questions in mind:
  • Does this reflect our students and high learning expectations for all learners?
  • Is anything generic, biased, or missing?
  • Is it legally and instructionally sound?
• Step 4: Finalization and Implementation - Implement the final plans. Adjust as needed based on what is working and what is not during the lessons. Reflect on what can be learned from how the lesson went and the evidence of student learning. Make sure to note how the recommendations worked and how the team might adjust in the future. Save the unit plan and accompanying adapted lessons in a shared electronic repository for future reference.

Download Tip 32: Quick Reference Card

Summary

GenAI tools hold real promise for helping collaborative teams work more efficiently by reducing a major barrier to collaboration: lack of time. These tools can reduce the time spent on drafting, organizing, and documenting, leaving more room for the professional conversation that leads to strong, inclusive plans. At the same time, using these tools works best when teams bring clear expectations to them. GenAI drafts require human review. Output must be checked for bias and individualization. Student data must be protected. When teams keep these responsibilities in mind, GenAI becomes a useful starting point rather than a shortcut. The expertise that makes inclusive education powerful stays with the team, where knowledge of the student, the classroom, and the family can actually shape the plan.