Prompt Engineering for Course Development: Wait, Confirm, Execute
General description of the project
This project explores how faculty members in higher education approach course development alongside artificial intelligence (AI). The study investigates faculty perceptions, comfort levels, and practical applications of AI tools in creating instructional materials. It also introduces a structured model for AI-assisted course design, called “Prompt: Wait, Confirm, Execute”, which guides faculty in engaging with AI tools more intentionally and effectively.
By leveraging freely available or institutionally licensed AI tools, instructors can minimize design constraints, accelerated the development of new courses from scratch, and enhanced existing learning materials for greater clarity, engagement, and accessibility. The model’s focus on prompt refinement and confirmation reduces wasted time and output revisions, maximizing productivity with minimal cost. This initiative inform institutional strategies for faculty development and digital literacy. Data collected help decision-makers identify training needs, improve support services for instructors, and integrate AI responsibly into academic policies. The project also promotes evidence-based decisions on curriculum innovation, emphasizing transparency and ethical AI use in teaching and learning.
The “Prompt: Wait, Confirm, Execute” model provides a practical, reflective framework for faculty to use AI tools efficiently and responsibly. It promotes deliberate interaction rather than automatic generation, helping educators stay in control of the creative process.
1. Prompt – Clearly define your goal, context, and expectations for the task. When crafting your initial prompt, instruct the AI to wait for further direction before generating a response. This creates a “pause” that allows you to refine the setup, provide data, or add clarifying details.
2. Wait – Use this pause to review and think critically about your input and intended outcomes. This step encourages reflection and ensures that you guide the process and maintain alignment with academic objectives.
3. Confirm and Execute – Once you are confident in your setup, give the AI permission to proceed by giving curating the an output carefully for accuracy, relevance, tone, and ethical alignment.
This structured approach ensures that AI supports academic expertise rather than replacing it. By encouraging intentional engagement, the model helps make AI use both sustainable and cost-effective across disciplines.
Technologies
This project integrated a combination of freely available AI tools and institutionally licensed technologies. Specifically, tools such as ChatGPT (OpenAI) and Microsoft Copilot (Microsoft 365 integration) were used to support a range of design and development tasks.
ChatGPT (Free Version): Served as an accessible, low-cost creative partner for ideation, drafting learning outcomes, generating formative assessments, and refining instructional language. Faculty leveraged the “Prompt: Wait, Confirm, Execute” framework to maintain control over the creative process, ensuring AI-generated content was aligned with pedagogical goals and disciplinary standards.
Microsoft Copilot: Used within institutionally approved environments (Word, PowerPoint, Excel, and Teams) to streamline document creation, summarize discussions, and improve accessibility of materials. Its integration with the university’s secure Microsoft ecosystem ensured compliance with data privacy and academic integrity policies.
The combined use of these technologies resulted in measurable gains in speed, quality, and consistency across the project lifecycle:
1. Speed: Course prototypes and materials that previously required several weeks to design could be developed in a fraction of the time through iterative AI-assisted drafting and revision.
2. Quality: AI-assisted editing and feedback improved clarity, tone, and accessibility of instructional content, reducing faculty revision time and enhancing learner engagement.
3. Sustainability: The use of freely available and institutionally supported AI tools minimized financial barriers and enabled long-term scalability across departments.
4. Pedagogical Alignment: The reflective prompting framework ensured that AI served as an assistive tool to maintain academic rigor and ethical use standards.
Explain project results
The project demonstrates a scalable, ethical, and cost-effective approach to integrating artificial intelligence into academic workflows. By adopting the “Prompt: Wait, Confirm, Execute” model and using approved AI tools such as Microsoft Copilot and ChatGPT, the institution can:
1. Enhance efficiency and productivity: Faculty can design or revise courses more quickly.
2. Support consistent quality standards: AI-assisted design ensures that instructional materials meet accessibility, clarity, and formatting standards across departments.
3. Encourage innovation and digital literacy: Faculty and staff gain confidence using emerging technologies responsibly.
4. Promote cost-effectiveness: By leveraging freely available and licensed AI tools, the institution reduces external design costs and maximizes return on existing software investments.
As for students, they directly benefit from the enhanced quality of AI-supported learning materials. This approach can improve learning experiences, increase accessibility, and enable faster curriculum updates that helps the institution advance its mission of promoting a high quality education.
Why it should be considered best practice?
This project presents a best practice in responsible and effective application of artificial intelligence in higher education. It combines innovation, accessibility, and ethical oversight within a sustainable framework that can be replicated across disciplines and institutions. “Prompt: Wait, Confirm, Execute” model introduces a replicable structure that promotes thoughtful and intentional AI use. By requiring faculty to pause, review, and confirm before implementation, the model reinforces human oversight, academic integrity, and ethical alignment.
Highlights of your proposed presentation
The project began by analyzing faculty pre-survey feedback, which provided valuable insights into instructors’ perceptions, concerns, and readiness to engage with AI tools in teaching and course design. This feedback directly informed the creation of the “Prompt: Wait, Confirm, Execute” model. Several faculty mentioned wanting more professional development, and by building on this model, the next phase will explore replicating and refining the model across courses and departments, collecting both quantitative and qualitative data to measure impact on teaching efficiency, material quality, and faculty satisfaction.
The Evaluation Committee will evaluate submitted proposals based on the following criteria. Each area will be rated on a scale from 1 to 5 (1= non-satisfactory; 5 =outstanding), for a maximum of 45 points.