Use an LLM web agent to compare potential lesson designs and get fast, low-cost feedback before deployment¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 design), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article demonstrates how designers could run the web agent on candidate lessons to compare designs before release. The authors show "how designers could use the agent to compare potential lesson designs through case studies", and argue such agents give "fast, low-cost, and helpful insights for new learning experiences". This makes agent-based walkthroughs a practical early-stage evaluation step in learning experience design.

Design Implications¶

Context¶

Requirements¶

• An online lesson prototype the agent can navigate autonomously

Constraints¶

Target Learners¶

• Learning experience designers and instructional designers

Target Learning Goals¶

• Evaluating and iterating on online lesson designs

Claims¶

• The authors argue AI web agents have the potential to accelerate advancements in online learning by giving fast, low-cost insights [+W]

Related Strategies¶

• Evaluate AI systems before deploying them with students

Examples¶

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Key Sources¶

• Wang, S., Mitchell, J., Piech, C. (2025). AI Web Agents Can Effectively Guide Lesson Design and Predict Student Outcomes. In: Cristea, A.I., Walker, E., Lu, Y., Santos, O.C., Isotani, S. (eds) Artificial Intelligence in Education. AIED 2025. Lecture Notes in Computer Science, vol 15878. Springer, Cham. https://doi.org/10.1007/978-3-031-98417-4_21