Generative AI and IEP Goal Development: Implications for Special Education Teacher Preparation
Danielle Waterfield
About Danielle Waterfield
Special Education Ph.D. Candidate at the University of Virginia School of Education & Human Development and a former special education teacher and administrator.
Her research sits at the intersection of evidence-based practice, educational technology, AI policy, and special education teacher preparation and development. Her work explores how generative AI can support teachers while maintaining professional responsibility, ethical safeguards, and individualized instruction for students with disabilities.
The problem highlighted in this brief
Special education teachers are responsible for developing legally compliant, individualized, and measurable Individualized Education Program (IEP) goals. Research has documented variability in IEP goal quality and ongoing challenges related to formal preparation in IEP development (Waterfield et al., 2025). In addition, workload pressures and compliance-related responsibilities remain persistent concerns within the special education workforce. As generative artificial intelligence tools enter educational contexts, empirical questions have emerged regarding whether AI can support IEP goal development while maintaining quality and professional oversight (Waterfield et al., 2025; Coleman & Waterfield, 2026).
Why does this topic matter to teacher preparation?
Teacher preparation programs vary in the extent to which they provide explicit instruction in IEP development, and many special educators report learning compliance-related skills during their initial years in practice (Waterfield et al., 2025). With nearly 15% of U.S. public school students receiving services under the Individuals with Disabilities Education Act (IDEA), the quality of IEP goals has direct implications for access to specially designed instruction and measurable progress. As generative AI tools become more visible in educational settings, frameworks for ethical and responsible integration—such as those outlined by Coleman and Waterfield (2026)—have introduced new considerations for how preparation programs may approach both IEP development and AI literacy within coursework and professional learning structures.
About This Brief
This brief explores how generative AI can support IEP goal development without diminishing quality. Drawing on Danielle Waterfield’s mixed-methods research examining IEP goals written with and without ChatGPT, this brief highlights implications for teacher workload, ethical AI use, professional development, and the future of AI in special education. Teacher educators will gain insight into how AI can function as a support tool rather than a replacement for professional expertise.
Research and Practice Context
Emerging Evidence on AI in IEP Development
Recent research has identified persistent variability in IEP goal quality and significant workload pressures among special education teachers. Generative AI tools such as ChatGPT have demonstrated potential to assist with text generation, resource creation, and compliance-related tasks. However, limited empirical research has examined how AI performs in IEP development specifically, particularly among experienced special educators. Waterfield’s study addressed this gap by comparing teacher-written goals with AI-assisted goals while also exploring teacher perceptions of AI use in practice.
The following are key insights shared by Danielle Waterfield on this research. The interview focused on six questions around AI use in teacher preparation and recommendations for integrating these tools responsibly.
Conversation with Danielle Waterfield
Q1: What are the issues that you are trying to address through your research and work with IEP goal quality?
Waterfield’s work addresses two intersecting issues: variability in IEP goal quality and workload heaviness. Grounded in cognitive load theory and conservation of resources theory, the study examines whether AI can reduce overwhelm without reducing quality.
Waterfield: “We know that making a high-quality IEP goal can still be cumbersome for special education teachers, especially if they haven’t had a lot of formal training in how to really craft a quality IEP goal.”
Q2: How can AI help address those issues?
Participants in her study viewed AI as a potential efficiency tool — not to replace teachers, but to streamline compliance-related tasks such as writing goals and developing resources. The quantitative findings showed no statistically significant difference in quality between teacher-written and AI-assisted goals, suggesting AI can function as a support tool.
Waterfield: “It could eventually kind of expedite their workflows… and ultimately that would give them more face time with their students”
Q3: How might teacher preparation programs integrate AI into current practice?
Waterfield emphasizes AI fluency and prompt crafting as foundational skills. Rather than teaching one tool, preparation programs should focus on transferable AI literacy, particularly effective prompting and evaluation of AI-generated outputs.
Waterfield: “If you can prompt effectively in ChatGPT, you can probably transfer those skills… to other tools with similar interactive components.”
Q4: What would you recommend for programs just starting with AI?
She notes that IEP training itself has historically been inconsistent. Programs should strengthen IEP development instruction first, then layer AI literacy on top, ensuring teachers can evaluate quality before relying on AI tools.
Waterfield: “Teacher preparation programs and professional development opportunities could reflect on what they’re already doing, or maybe not doing, in regards to IEP development.”
Q5: Where is the research headed in this area?
She anticipates expanded research on how AI can enhance teacher retention, streamline workflows, and improve preparation. As AI models evolve, ongoing evaluation of their capabilities will be necessary.
Waterfield: “I kind of see the field moving toward having more research and training around effective AI use for teachers and also for students.”
Q6: What else should teacher preparation programs consider?
Waterfield emphasizes the importance of human oversight or “human in the loop.” Ethical use frameworks, transparency with families, and professional judgment must remain central. AI should function as an enhancement, not a substitute for educator expertise.
Her research suggests that generative AI can support high-quality IEP goal development without compromising standards, but only when teachers remain central in the process. For teacher preparation programs, the implication is clear: strengthen IEP development training, build AI literacy, and anchor both in ethical, student-centered practice. AI may reduce cognitive load and administrative burden, but professional responsibility remains firmly in human hands.
If you are interested in learning more, please read the article IEPs in the Age of AI: Examining IEP Goals Written with and Without ChatGPT in the Journal of Special Education Technology
Resources and References
Coleman, O. F., & Waterfield, D. A. (2026). Ethical AI Use in IEP Development: A Guiding Framework. Journal of Special Education Technology. https://doi.org/10.1177/01626434261419099
Waterfield, D. A., Coleman, O. F., Welker, N. P., Kennedy, M. J., McDonald, S. D., & Cook, B. G. (2025). IEPs in the Age of AI: Examining IEP Goals Written with and Without ChatGPT. Journal of Special Education Technology. https://doi.org/10.1177/01626434251324592
This work is licensed under a Creative Commons Attribution 4.0 International License.