Generative AI Prompt Engineering for Educators
Dr. Jiyeon Park
About Dr. Jiyeon Park
Dr. Jiyeon Park is an assistant professor at the Eastern Kentucky University.
She is teaching instructional and assistive technology courses for pre-service special and general education teachers. Her research interests involve academic interventions for students with disabilities, as well as the effective use of instructional and assistive technologies in special education. In particular, her research focuses on leveraging emerging technologies, such as artificial intelligence, to enhance learning outcomes for students with disabilities.
The problem highlighted in this brief
Generative artificial intelligence (GenAI) has great potential to facilitate individualized learning, provide immediate feedback, and reduce workload. However, the rapid evolution of AI technology and the lack of frameworks that focus on leveraging GenAI in education settings leave many educators unprepared. Dr. Park addressed the importance of developing a prompt engineering framework that guides educators to use GenAI effectively.
Why does this topic matter to teacher preparation?
It is important to prepare educators to effectively use GenAI in educational settings due to its immense possibilities to provide personalized instruction based on diverse learning needs. Several studies indicated the necessity of prompt engineering skills for educators to support students with disabilities (Goldman et al., 2024; Mosher et al., 2024; Waterfield et al., 2024). With prompt engineering training, educators can leverage GenAI to create accessible and equitable learning environments, which are critical issues in special education.
About This Brief
This brief will introduce the prompt engineering framework that guides designing and optimizing input prompts for GenAI. Dr. Park introduces the IDEA framework, a step-by-step guide for using prompt engineering to maximize the effectiveness of GenAI in education. She provides insights on integrating the framework for teacher preparation programs as well as implications for future research.
Research and Practice Context
Generative AI and Prompt Engineering for Educators
Special education and general education teachers who teach students with disabilities often face significant workloads developing Individualized Education Programs (IEPs), and personalized lesson plans and resources (Goldman et al., 2024; Mosher et al., 2024; Waterfield et al., 2024). GenAI has a huge potential to support teachers by generating personalized materials. However, ineffective prompt inputs for GenAI often lead to undesired output (Dwivedi et al., 2023; Ekin, 2023; Zhou et al., 2023). Teaching how to write effective prompts can be a stepping stone for pre-service and in-service teachers in effectively leveraging GenAI to support students with disabilities.
The following are key insights shared by Dr. Jiyeon Park on this research. The interview focused on seven questions about GenAI and prompt engineering in teacher preparation and recommendations for teacher educators to incorporate these ideas.
Conversation with Dr. Jiyeon Park
Q1: Can you introduce what generative AI and prompt engineering are?
GenAI creates images, videos, or texts based on user input, also referred to as “prompts.” Although prompts involve everyday human language, we need to learn how to write effective prompts as the quality of AI output depends on the quality of input. Learning prompt engineering including crafting, refining, and optimizing prompts helps us interact with AI effectively (Ekin, 2023).
Dr. Park: “Generative AI refers to artificial intelligence systems using machine learning techniques to create original, personalized, and contextually relevant outcomes, such as images, videos, or text, based on user input. ... Prompt engineering is the process of crafting, refining, and optimizing prompts to obtain high-quality outcomes from AI models.”
Q2: What issues are you trying to address through your research and work?
Educators face significant challenges in accessing resources that help them effectively use AI tools in special education. Dr. Park emphasizes the rapid evolution of AI technology and highlights the necessity of AI literacy for educators to keep pace with advancements. Her research focuses on addressing this gap by developing practical resources and actionable strategies, such as the IDEA framework, specifically tailored to educators. Unlike existing frameworks, which often lack a pedagogical focus, the IDEA framework equips teachers to leverage AI in ways that enhance learning for diverse learners and support evidence-based practices.
Dr. Park: “AI tools enable teachers to create customized curricular resources for students with disabilities while also offering substantial support to manage the heavy workload. However, resources for special education teachers to use AI tools are limited. … To address this, I adopted and built on existing models to develop the IDEA framework specifically designed for educators. ”
Q3: Can you walk us through how generative AI and prompt engineering can support educators who teach students with disabilities?
Dr. Park introduces the IDEA framework, a step-by-step guide for using prompt engineering to maximize the effectiveness of GenAI in education. First, the framework emphasizes including essential PARTS like defining and specifying the educator's role (Persona), goals (Aim), target audience (Recipient), stylistic parameters (Theme), and desired output format (Structure) (Google for Educators, 2024). In addition, the framework also stresses the importance of CLEAR prompts which stands for Concise, Logical, Explicit, Adaptive, and Restrictive prompts to ensure AI outputs are aligned with educational needs (Lo, 2023). Finally, Dr. Park highlights the need for iterative REFINEment of prompts following these steps: Rephrase the keywords, Experiment with context and examples, Feedback loop, Inquiry questions, Navigate by iterations, and Evaluate and verify outputs. The iterative refinement ensures the ethical and effective use of GenAI in special education by addressing potential biases or inaccuracies in AI tools.
Dr. Park: “The IDEA framework is a practical strategy for educators. I stands for Include essential components, D stands for Develop prompts using clear language, E stands for Evaluate outcomes and refine prompts, and A stands for Apply accountability.”
Q4: How would you integrate generative AI and prompt engineering into your teacher preparation program?
Dr. Park integrates GenAI and prompt engineering into teacher preparation by embedding these technologies within existing courses like instructional technology. Through hands-on activities such as creating lesson plans, practicing IEP writing, and developing instructional materials, pre-service teachers gain practical experience with AI tools. Dr. Park also highlights how GenAI can support reading comprehension by simplifying complex texts for diverse learners and engaging them with materials at their reading level. These approaches not only model effective AI use but also prepare educators to leverage these technologies to address the learner variabilities in their future classrooms.
Dr. Park: “We could offer courses focusing on AI or introduce AI within existing coursework for pre-service teachers. Faculty can also use AI tools for hands-on activities like creating lesson plans, practicing IEP writing, or developing instructional materials for diverse learners.”
Q5: How can we better prepare educators for using or developing these technologies?
Dr. Park emphasizes the importance of equipping educators with foundational knowledge and practical skills to effectively utilize AI in teaching. She advocates for comprehensive training that includes understanding AI capabilities and limitations, engaging in hands-on practice, and learning strategies for ethical and reasonable use. These preparations support educators in leveraging AI to create lesson plans, develop instructional materials, and utilize accessibility features to support diverse learners, particularly students with disabilities.
Dr. Park: “Educators should be prepared by focusing on fundamental knowledge and practical skills. Training should include basic knowledge of AI, hands-on practice, and strategies for reasonable use.”
Q6: What implications do you see for future research, and what are some questions we might be asking?
Q7: Are there any resources and tools you suggest for those who want to learn more about your work?
Dr. Park highlights the need for more evidence-based studies to validate the effective use of AI in special education. While AI holds promise for creating equitable and accessible learning environments, there are gaps in research and practice on its integration into evidence-based practices. She also emphasizes the importance of addressing ethical considerations, such as data privacy, bias, and overreliance on technology. Dr. Park calls for collaboration among educators, AI developers, programmers, and policymakers to ensure AI tools are both reliable and beneficial for students with disabilities.
Dr. Park recommends several online resources for educators interested in learning more about prompt engineering. Tools like Prompt Professor, Prompt Engineer, and Prompt Perfect are designed to assist in crafting and refining prompts for higher-quality outputs from AI models. These resources provide practical support for integrating AI into teaching practices and improving its effectiveness in diverse educational settings.
If you are interested in learning more about prompt engineering for educators with details and examples, please read the publication written by Dr. Park and Dr. Choo.
Resources and References
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., & Wright, R. (2023). “So what if ChatGPT wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71(1), 102642.
Ekin, S. (2023). Prompt engineering for ChatGPT: A quick guide to techniques, tips, and best practices. TechRxiv.
Goldman, S. R., Taylor, J., Carreon, A., & Smith, S. J. (2024). Using AI to support special education teacher workload. Journal of Special Education Technology, 39(3), 434-447.
Google for Educators. (2024). Generative AI for educators. Google LLC. https://skillshop.exceedlms.com/ student/path/1176018.
Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4), 102720.
Mosher, M., Dieker, L., & Hines, R. (2024). The past, present, and future use of artificial intelligence in teacher education. Journal of Special Education Preparation, 4(2), 6-17.
Park, J., & Choo, S. (2024). Generative AI prompt engineering for educators: Practical strategies. Journal of Special Education Technology, 01626434241298954.
Waterfield, D. A., Watson, L., & Day, J. (2024). Applying artificial intelligence in special education: Exploring availability and functionality of AI platforms for special educators. Journal of Special Education Technology, 39(3), 448–454.
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2023). Large language models are human-level prompt engineers. arXiv: 211.01910.
Suggested Citation
Seung, Y., Park, J., & the CIDDL Team. (2025). Generative AI Prompt Engineering for Educators. The Center for Innovation, Design, and Digital Learning.
This work is licensed under a Creative Commons Attribution 4.0 International License.