AI evidence extraction

A Framework for Approaching AI Education in Educator Preparation Programs

Nancye Blair Black, Stacy George, Amy Eguchi, Jennifer Camille Dempsey, Elizabeth Langran, Lucretia Fraga et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

15.79

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1609/aaai.v38i21.30351

Methodology & findings

Study design

Narrative literature review combined with framework development based on Faculty Fellowship collaboration.

Main result

The paper articulates "a framework of seven critical strategies with the potential to address the urgent need EPPs have in preparing preservice teachers to effectively integrate AI-powered instructional tools and to teach this new area of content knowledge in PreK-12 classrooms." The framework demonstrates that "support for preservice teachers' critical examination and application of AI, including a focus on the issues of equity, ethics, and culturally responsive teaching, is essential to their later success in PreK-12 classrooms."

Reports effect sizes.

Research paradigm

Interpretivist/Constructivist

Author conclusions

The authors conclude that "a framework of seven critical strategies" with "potential to address the urgent need EPPs have in preparing preservice teachers to effectively integrate AI-powered instructional tools" is necessary, and they provide "Recommendations for further research and learning...to promote community-wide initiatives for supporting the integration of AI in education through Educator Preparation Programs and beyond."

Risk of bias

Not reported in abstract. Potential bias considerations include: selection bias in Faculty Fellows chosen for the collaboration, lack of empirical validation of the proposed framework, and possible institutional funding bias from ISTE.

Open questions raised

The paper identifies the gap that "while many organizations have developed professional learning opportunities for inservice educators, a gap remains for resources specifically designed for those facilitating and enrolled in Educator Preparation Programs (EPPs)." The authors provide recommendations for further research to promote community-wide initiatives for supporting AI integration in education.
Gap in resources for Educator Preparation Programs (EPPs) regarding AI integration; need for support in preservice teachers' critical examination of AI with focus on equity, ethics, and culturally responsive teaching; need for community-wide initiatives to support AI integration in education beyond EPPs.
The paper identifies that a "gap remains for resources specifically designed for those facilitating and enrolled in Educator Preparation Programs (EPPs)" compared to professional learning opportunities available for inservice educators. The authors also identify emerging needs for integrating AI education in EPPs and recommend further research and community-wide initiatives for supporting AI integration in education.

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