Trust, Readiness & AI Competence in Education
Teacher Readiness for AI: Competence, Trust and Concerns
An interactive look at how K-12 teachers' AI teaching competence, trust in AI and concerns about AI compared before and after a two-day professional development program. Choose a question, examine the evidence, then build your own version through vibe coding.
Each one answers with real study results, as group summaries.
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Findings
Key findings
Higher skills and trust, lasting concerns.
Teachers moved toward confident adoption
Group averages with 95% confidence intervals. Hollow = before, filled = after.
Skill and worry are different things
Correlation runs from −1 to 1; 0 means unrelated. Hollow = before, filled = after.
Some concerns eased; others did not
% of teachers agreeing with each concern. Hollow = before, filled = after.
What this means
For PD designers
Pair hands-on practice with time for sensemaking about transparency, autonomy and privacy, and measure trust and concerns as well as skills.
For school leaders
Clear policies on data use and human oversight help turn new skills into confident, responsible practice.
For EdTech builders
Explain how decisions are made, keep teachers in control and be open about data. Teachers trust tools they can understand.
Before and after are different groups of teachers and there was no comparison group, so differences cannot be attributed to the workshop alone.
Prompt Lab
Your prompt journal
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Ask TRACE
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Chat about the study's findings, no sign-in needed. Attach a document or spreadsheet to ask how it relates.
About
About the study and this dashboard
Read with care
Privacy
Downloads
Synthetic practice data · Dimensions · Subscales · Items · Groups
Build your own Ask TRACE app
Open Google AI Studio → Build, attach the synthetic data and the item summary,
and paste the prompt from the TRACE Studio Kit (ask_trace_app_kit.md). AI Studio builds a chat app you can share with colleagues.