Episode 78
Quality by Design in the AI Era
Brief description of the episode
Dr. Andrea Gregg, Associate Research Professor and Director of Learning Experience Design at Penn State University, outlines a practical approach to building strong online learning in the AI era. She explains why simple, “don’t make me think” design protects real learning, and why every choice should tie back to outcomes and assessment. She covers what makes micro-credentials credible, how to avoid personalization and dashboard features that mislead instructors, and where AI can help without replacing essential thinking. The episode turns solid learning principles into concrete decisions for higher-ed leaders, LXDs, faculty, and product teams.
Key Takeaways:
- Prioritize usability in courses so learners immediately know where they are and what to do next.
- Tie rigor to learning outcomes by aligning activities and assessments to the skills learners are supposed to demonstrate.
- Treat the product like a course: start with learner analysis, define purpose and outcomes, then build a simple structure and interface.
- Use clear, human-centered messages and labels, avoiding jargon and any cues that quietly lower confidence.
- Give instructors views and tools that help them teach, monitor progress, and grade without friction.
- Require students to do the first round of thinking themselves because predictive AI can be confident and wrong.
- Use AI for explanations, alternate framings, questioning, and have students compare its output to known solutions.
- Write explicit syllabus policies that state when AI can or cannot be used and connect those rules to learning outcomes.
- Build AI literacy by teaching how models predict, where they fail, and the basics of data privacy and ‘garbage in, garbage out.’
- Keep teachers as the decision makers and use AI only to support progress monitoring, not to replace human judgment.
- Start with audience analysis and co-create with the industry so the skills match real workplace needs.
- Make them competency-based by assessing the actual skill through performance, not just multiple-choice checks.
- Issue digital badges with metadata that shows the topics, artifacts, and how the learner proved competence.
- Keep a clear, concise structure for a 10–20 hour experience with frequent interaction, applied tasks, and outcomes-aligned assessment.
- Use employer language in descriptions and rubrics so the value is obvious and not buried in academic wording.
- Treat “completed” clicks and dashboards with caution and look at content analytics and context before interpreting behavior.
- Use mixed methods by pairing low-stakes checks with usage patterns to guide what happens in the next lesson.
- Design visualizations that help learners instead of discouraging them, and avoid stoplight indicators that lower confidence.
- Bring educational psychology and UX into instrumentation so engagement data reflects learning, not just activity.
- Pilot personalization in low-stakes settings first, and let measurement and outcomes decide whether to scale.
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