Design AI literacy tools with transparency in development and establish district policies for vetting AI tools¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Drawing on educator concerns about bias, inaccuracies, and data privacy, the report recommends that AI tools for reading be designed with English Learners at the forefront. It states this "includes ensuring transparency in AI development, as well as establishing robust district policies for vetting AI tools to ensure ethical and culturally responsive adoption." In practice this means districts maintain procedures evaluating AI tools on multiple components such as student data privacy, and developers make training data and design purposes clear to educators, echoing participants' questions about how tools were developed and what data trained them.

Design Implications¶

Context¶

Requirements¶

• District policies or procedures where AI tools are vetted and evaluated on multiple components, such as student data privacy

Constraints¶

• Educators noted vetting questions arise where AI-generated reading material must fit school approval requirements for reading materials

Target Learners¶

• English Learners in elementary grades
• K-8 students

Target Learning Goals¶

• Early literacy and reading instruction

Related Strategies¶

• Ground AI integration in higher education on core principles of human agency, academic freedom, transparency, ethics, inclusivity, and critical thinking
• Mitigating Racial Bias in Edtech Products
• Use this Toolkit: Mitigating Racial Bias in Edtech
• AI in Education Toolkit for Racial Equity
• Systemic, human-centred approach treating digital data use as a school-and-teacher system, with AI integrated into professional development
• Ask ethics, bias, and equity questions before adopting an AI system
• Establish AI-specific evaluation and accountability systems using independent audits, role-based dashboards, and privacy and equity safeguards
• Audit AI vocal-assessment systems for bias, transparency, privacy, and recording-context robustness before evaluative use
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making

Examples¶

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Key Sources¶

• Stephenson, S. P., Leones, T., Kamdar, D., Prado, Y., & Ddamulira, J. (2025). A National Context of Generative AI for Reading to Support English Learners. Digital Promise. https://digitalpromise.dspacedirect.org/items/73c883c0-4652-499d-aeb2-74f773749c60. doi:10.51388/20.500.12265/258