Prioritize tutor communication skill and a calibration readiness mechanism when replicating the co-creation lab¶

Strategy · All strategies
Evidence · no claims cited

Description¶

For institutions seeking to replicate the LearnAI model, the article recommends prioritizing tutor selection and pedagogy over rare technical expertise. Both interviewed tutors identified communication and teaching as harder to develop than technical proficiency, and the article concludes that "Replication efforts should thus prioritize recruiting tutors who can explain and adapt, then add training on current tools and a readiness mechanism before independent facilitation." The model's feasibility is described as depending less on rare technical expertise than on tutor selection, pedagogy, and quality control.

Design Implications¶

Context¶

Requirements¶

• Recruit tutors who can explain and adapt
• Provide training on current tools
• Use a readiness mechanism (observation and co-facilitation) before independent facilitation

Constraints¶

• Replication guidance derives from interviews with only 2 of 4 tutors at a single institution

Target Learners¶

• Mixed-ability university learners served by peer-tutored co-creation labs

Target Learning Goals¶

• AI-supported problem solving, prompting as specification, and independent continuation of AI workflows

Affordances¶

• Learnai Two Layer Just In Time Co Creation Framework

Related Strategies¶

Examples¶

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Key Sources¶

• Weihao Qu, Ling Zheng, Chris Buzaid, and Daniel Crawford. 2026. LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University. https://arxiv.org/abs/2608.19164