This effective online practice was developed in response to a growing need on campus to support graduate students in becoming AI literate and AI fluent without undermining instructional quality, accessibility, or professional judgment. As generative AI tools rapidly entered educational contexts, many graduate students expressed uncertainty about how to use these tools ethically and productively in instructional design work. At the same time, faculty faced legitimate concerns that AI could encourage surface-level thinking or replace essential human decision-making.
This practice addresses that need by embedding AI use within a carefully scaffolded sequence of online learning experiences that emphasize critical evaluation, ethical responsibility, and inclusive instructional design. Rather than positioning AI as a shortcut or productivity tool, the course design frames AI as a resource that must be actively guided, evaluated, and constrained by the human designer.

Description of the Practice
The practice is implemented in a graduate-level online instructional design course (ADE 608) and centers on a multi-phase, persona-based learning sequence that builds AI literacy and fluency through targeted learning activities for the duration of the semester.
Phase 1: Self-Persona Development
Students begin by constructing a learner persona of themselves. AI tools may be used optionally to assist in drafting the persona. Students then critically evaluate the AI-supported output for accuracy, clarity, and alignment with their lived learning experiences. This phase is intentionally low-risk and metacognitive, allowing students to experiment with AI while developing awareness of how AI represents learner characteristics, preferences, and challenges.
Phase 2: Learner Persona Construction for Instructional Design
Students then construct three learner personas representing potential participants/students/clients in their instructional design microteaching project. These personas must address learner background, goals, motivations, and any disabilities or access needs. Students again use AI as a support tool, but they are required to act as the skilled human in the loop by reviewing, correcting, and refining AI-generated content to ensure that the personas are accurate, ethical, and plausible representations of real learners.
Phase 3: Iteration and Prompt Engineering
Iteration is explicitly required and assessed. Students refine both their personas and their prompts, learning that high-quality AI outputs depend on clear intent, constraints, and instructional reasoning. Custom prompts developed by the instructor guide students to think critically about how they communicate with AI systems and how prompt engineering functions as a metacognitive and professional skill rather than a technical trick.
Phase 4: Convergent Persona-Informed Instructional Design
In the culminating phase, students design a single learning experience for their microteaching project and explain how it serves all three diverse learner personas simultaneously. Rather than creating separate lessons, students must justify how their instructional choices, including lessons, videos, activities, and artifacts, are scaffolded, accessible, and differentiated within a shared learning experience. This phase reinforces that instructors serve diverse learners in the same instructional space and that inclusive design strengthens learning for all students.

Why the Practice Is Effective
This practice is effective because it treats AI literacy and fluency as developmental competencies rather than isolated skills. Students move from awareness to application through intentional scaffolding, increasing complexity, and ethical responsibility. AI use is transparent and optional, reinforcing learner autonomy and modeling responsible instructional practice.
Assessment focuses on instructional reasoning, justification, and reflection rather than AI-generated output. Students are evaluated on how well they explain design decisions, address learner needs, and revise their work and iterate based on critical review. This approach directly counters common concerns that AI diminishes rigor or replaces human expertise.
Accessibility and disability considerations are embedded into persona construction and instructional justification, reinforcing that inclusive design begins at the planning stage rather than as a post-production accommodation.

What Was Gained or Achieved
Through this practice, graduate students demonstrate increased confidence in evaluating AI outputs, greater awareness of AI limitations and biases, and stronger instructional decision-making skills. Students consistently report a clearer understanding of their role as designers responsible for instructional quality, accessibility, and ethical practice. The course design also provides faculty with a replicable structure for integrating AI into online instruction without compromising academic integrity or learning outcomes.

Why Others Should Consider This Practice
This practice is tool-agnostic, adaptable, and transferable across disciplines and programs. It provides a practical model for institutions seeking to integrate AI into online graduate education in a way that is ethical, inclusive, and pedagogically sound. By embedding AI use within a coherent instructional arc, the practice supports sustainable, faculty-driven innovation rather than reactive or tool-centered adoption.

https://www.section508.gov/develop/sample-personas/
https://digitalpromise.org/2024/06/18/ai-literacy-a-framework-to-understand-evaluate-and-use-emerging-technology/
https://online.hbs.edu/blog/post/what-is-design-thinking
https://www.nist.gov/itl/ai-risk-management-framework