The Language of AI: E22 - IEPs and AI

Fellow Educators,

I am NOT an expert in gradeschool or highschool education plans but I do know that many of you are inundated with modifying plans for students in need. I know that AI can help with this process when done correctly and safely. When considering how artificial intelligence might assist with Individual Education Plans (IEPs) in Ontario's educational system, it helps to approach the topic conceptually rather than from the perspective of an IEP expert or classroom teacher. Let's look how we might think through this process, focusing on the conceptual framework rather than specific implementation details.

Understanding IEPs as Information Systems

At their core, IEPs are structured information systems designed to capture a student's learning profile and educational needs. They represent a synthesis of observations, assessments, goals, and strategies. This fundamental structure makes them potentially compatible with AI assistance in several ways.

Think of an IEP as having three main conceptual components:

1. Input information - observations, assessments, and data about the student

2. Analytical components - the interpretation and synthesis of that information

3. Output components - goals, strategies, accommodations, and plans derived from that analysis

AI can potentially assist with each of these components in different ways.

The AI Thinking Framework for IEPs

Stage 1: Information Organization and Pattern Recognition

AI excels at organizing large amounts of information and recognizing patterns within that data. When thinking about using AI for IEPs, you might consider:

• How could AI help sort through assessment results, observations, and previous documentation to identify patterns in learning needs?

• Could AI assist in comparing a student's profile against typical developmental patterns to highlight areas of strength and need?

• Might AI suggest connections between seemingly disparate pieces of information about a student?

For example, rather than manually reviewing all previous assessment data, you might ask an AI: "What patterns emerge when comparing this student's reading fluency assessments over the past three terms with their comprehension scores?"

Stage 2: From Patterns to Insights

Once patterns are identified, AI can help generate potential insights by comparing those patterns against known educational frameworks and approaches:

• How might AI connect identified learning needs with research-based interventions?

• Could AI suggest potential underlying factors that connect several observed behaviors or challenges?

• Might AI help translate technical assessment language into more accessible descriptions?

For instance, you could ask an AI: "Based on these assessment results showing strengths in visual processing but challenges with auditory processing, what might this suggest about potential learning approaches for this student?"

Stage 3: From Insights to Actionable Strategies

The most valuable aspect of AI assistance might be in connecting identified needs to specific, actionable strategies:

• How could AI generate a range of possible accommodations or modifications based on specific learning profiles?

• Might AI suggest differentiated approaches to teaching the same concept based on a student's learning preferences?

• Could AI help scaffold goals into manageable, measurable steps?

You might ask: "What are five different ways to modify this Grade 6 science curriculum expectation for a student working at a Grade 3 reading level but with grade-appropriate conceptual understanding?"

Practical Thinking Scenarios

Here are a few conceptual scenarios that illustrate how this thinking framework might apply (always protect the information used and nothing identifiable to the student):

Scenario 1: Generating Initial Hypotheses

Imagine you're beginning the IEP process for a student you don't know well. You have some assessment data and observations, but you're not sure how to integrate them into a coherent picture.

AI Thinking Approach:

1. Input the available information without attempting to draw conclusions

2. Ask the AI to suggest several possible interpretations of the data

3. Use these suggestions as starting points for your own professional consideration

4. Refine your understanding through additional observation and collaboration

Sample Thinking Prompt: "I have these pieces of information about a student: [list observations and assessment results]. What are 3-4 different ways these might be interpreted, and what additional information would help clarify which interpretation is most accurate?"

Scenario 2: Translating Between Different Frameworks

Education involves many different frameworks and terminologies - curriculum expectations, assessment frameworks, diagnostic categories, etc. Translating between these can be challenging.

AI Thinking Approach:

1. Identify the frameworks you're working with

2. Ask the AI to suggest connections between specific elements

3. Use these suggestions to build bridges between different aspects of the student's educational experience

Sample Thinking Prompt: "How might this set of 'challenges with working memory' potentially impact a student's engagement with these specific Grade 8 mathematics expectations? What approaches might address these challenges while maintaining academic rigor?"

Scenario 3: Developing a Range of Possible Approaches

For any identified need, there are typically multiple possible interventions or approaches. AI can help you consider a broader range of options.

AI Thinking Approach:

1. Clearly describe the specific learning need or challenge

2. Ask the AI to generate multiple distinct approaches, including their potential advantages and limitations

3. Use these suggestions as a starting point for your own creative thinking about the student's needs

Sample Thinking Prompt: "For a student who struggles with initiating and organizing written assignments, what are five fundamentally different approaches to supporting this need? For each approach, what would be one potential advantage and one potential limitation?"

Ethical Considerations in Your Thinking Framework

When conceptualizing AI's role in IEP development, several important ethical considerations should shape your thinking:

1. Maintaining Human Judgment at the Center

Always position AI as an idea generator and information organizer, not a decision-maker. The final judgment about what goes into an IEP should always remain with the educational team, including parents and, when appropriate, the student.

Thinking Question: "How can I use AI-generated suggestions as a starting point while ensuring my professional judgment remains central to the process?"

2. Avoiding Over-Standardization

One risk of AI assistance is potential over-standardization of IEPs. Each student is unique, and their educational plan should reflect that uniqueness.

Thinking Question: "How can I use AI to enhance, rather than reduce, the individualization of this educational plan?"

3. Addressing Potential Biases

AI systems may reflect biases present in their training data, including biases related to cultural backgrounds, socioeconomic factors, or disability categories.

Thinking Question: "What assumptions might be embedded in this AI-generated content, and how can I critically evaluate these suggestions to ensure they're appropriate for this specific student's context?"

A Thinking Partnership

The most productive way to conceptualize AI's role in IEP development is as a thinking partner - one that can help organize information, suggest connections, and generate possibilities, but that always defers to human judgment for final decisions.

This thinking partnership works best when you:

1. Ask open-ended questions that generate multiple possibilities rather than single answers

2. Provide context that helps the AI understand the specific educational situation

3. Maintain critical engagement with AI suggestions, evaluating them against your knowledge of the student and educational best practices

4. Use AI to expand your thinking, not replace it

Approaching AI as a tool for enhancing your thinking process rather than as an expert system providing definitive answers, you can leverage its capabilities while maintaining the human-centered, collaborative nature of effective educational planning.

The goal isn't to have AI write IEPs, but rather to have AI help educators think more deeply, creatively, and comprehensively about how to best support each student's unique educational journey.

Thanks for taking the time to be part of a positive change in education compared to simply burying your heads in the sand.

Cheers,
Matthew