Teachers' post-workshop platform engagement fell into three behavioral profiles, with most teachers showing minimal creation and browsing¶

Claim · All claims
Evidence · 1 study · design r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Hierarchical clustering identified three engagement profiles: High-Creation-High-Browsing (10.6%, n=23), Low-Creation-High-Browsing (33.5%, n=73), and Low-Creation-Low-Browsing (56.0%, n=122). → Haiyang Xin 2026

Evidence¶

Haiyang Xin 2026¶

Haiyang Xin, Qiannan Niu, Shuang Li, Yimeng Sun, Ching Sing Chai, Lingyun Huang, Gaowei Chen. (2026). An Activity-Theoretical Approach to Teacher Professional Development in Pedagogical AI Agent Design. https://arxiv.org/abs/2605.12934 doi:10.48550/arxiv.2605.12934

q2 · i? · design · r3

Hierarchical clustering (Ward's linkage, squared Euclidean distance) on seven standardized behavioral variables from 218 teachers' eight-week platform logs. "Silhouette coefficient analysis (0.62) confirmed optimal three-cluster solution"; the Low-Creation-Low-Browsing group held 56.0% (n=122).

"Silhouette coefficient analysis (0.62) confirmed optimal three-cluster solution: High-Creation-High-Browsing (10.6%, n=23) demonstrated sustained active creation wit h frequent browsing"

Discussion¶

Related Claims¶

• 87% of K-12 teachers ceased meaningful AI agent creation within three weeks of completing a two-day professional development workshop — a broader claim this one bears on
• Exploratory K-means clustering identifies four heterogeneous student AI-user profiles differing in experience, competence, usefulness, trust, and control, but the solution's optimality and stability are not established — related
• K-means clustering of K–8 students' platform trace data yields seven distinct engagement profiles — related
• Interviewed teachers attributed disengagement to technical, institutional, and social-relational environmental constraints rather than capability deficits — related
• Teachers designing multi-agent workflows fall into three behavioral archetypes: Systematic Optimizers, Prolific Creators, and Passive Observers — related
• Policy packages fall into four archetypes defined by which design element is missing — related
• In an eight-week teacher workshop, the AI technology creation process was iterative and non-linear, with Understand AI and Reflect on Ethical Application phases surrounding the design cycle — related
• A cluster of highly active learners driving the learning communities included equally many low-hierarchy as high-hierarchy members — related
• Markov transition analysis shows the three clusters differ in workflow dynamics: iterative optimization loops, rapid prototyping flows, and browsing-anchored exploration — related
• K-Means++ clustering of self-reported behavioral proxies yields six distinct, stable learner profiles (k = 6, Silhouette = 0.62) — related