Research map / Large language models / AI and society
Empowering Educators in AI: Insights from Co-Designing an AI Microcredential with and for K-12 Educators
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Reports a co-design study with six K-12 teachers and instructional facilitators that used conjecture mapping and inductive memoing to derive themes and requirements for an Introduction to AI microcredential for educators.
Problem
Current professional development options for teaching AI in K-12 are limited, and teachers' needs for integrating AI into classrooms are not well understood or co-designed with teachers.
Approach
Applies participatory co-design (conjecture mapping, card sorting inspired by Teacher Superpowers, inductive coding and memoing) to build an AI microcredential with K-12 teachers, yielding themes on ethics monitoring, agency under district constraints, unbiased AI knowledge, modernization on their terms, and continuous just-in-time learning.
Key results
- Six participants (4 middle school teachers and 2 instructional facilitators) across three one-hour co-design sessions produced themes including ethical AI integration monitoring, AI agency limited by district constraints, demand for unfiltered AI knowledge, modernization on teachers' terms, and cont
- The conjecture map was revised in green-marked changes (e.g., 'Taken to the Classroom' resources, 'district-level limitations', 'behavioral changes', and a new outcome on unfiltered, unbiased opinions about AI).
- Initial feedback from 8 elementary educators on a Teachable Machine bias activity indicated instruction-clarity issues that the team plans to refine.
Surprising findings
- Teachers' concerns centered on practical classroom impacts of AI (e.g., effects on teaching practice and student learning behaviors) rather than only the commonly discussed privacy and copyright issues, though they noted those too.
Limitations
- Small sample of six participants, with interpretations acknowledged as subjective and alternate readings possible
- Intended as precedent knowledge rather than empirical evidence of teacher AI needs
- Activity feedback is anecdotal and from only eight elementary educators
Experimental setup
Datasets: focus group interview transcripts card sorting activity products
Builds on: Teacher Superpowers activity conjecture mapping co-design
Closest papers by meaning
- Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study (AAAI 2025 · similarity 0.93)
- A Framework for Approaching AI Education in Educator Preparation Programs (AAAI 2024 · similarity 0.90)
- “From Unseen Needs to Classroom Solutions”: Exploring AI Literacy Challenges & Opportunities with Project-Based Learning Toolkit in K-12 Education (AAAI 2025 · similarity 0.90)
- Advancing Research on Equitable AI Education Through a Focus on Implementation: Insights from a Middle School Computer Vision Module Beta-Test (AAAI 2025 · similarity 0.89)
- An Effectiveness Study of Teacher-Led AI Literacy Curriculum in K-12 Classrooms (AAAI 2024 · similarity 0.89)
- Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers (AAAI 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.