This student-facing AI policy outlines clear guidelines for responsible, ethical, and transparent AI use across academic settings, emphasizing academic integrity, digital literacy, and data privacy.
This internal policy outlines clear guidelines for AI use by staff, including permissible applications, student data protections, responsible oversight, and required professional learning. It establishes expectations for ethical, transparent, and inclusive use of AI across instructional and operational contexts.
This report summarizes findings from a staff-wide survey on how educators within the charter network are using AI, what support they need, and how aligned they feel AI use is with instructional priorities. The results informed the network’s next steps for piloting, policy, and PD.
This strategy highlights how schools can lay the groundwork for effective, equity-driven AI integration by building on existing strengths in professional learning, data use, and continuous improvement. By rooting AI in instructional culture and aligning it to real student needs, schools can explore new tools with clarity, purpose, and long-term sustainability.
This foundational article by Jon Saphier and Matthew King outlines 12 key cultural norms that drive sustained instructional improvement. The Eliot team cites this research as a guiding force in their AI strategy, intentionally sequencing their innovation work after building strong data practices, peer collaboration routines, and shared norms around experimentation and growth.
This professional development session supported educators in deepening their understanding of AI tools and integrating them into data-informed planning. It reflects Eliot’s commitment to building system wide instructional capacity, ensuring AI supports—not replaces—educator expertise in improving student learning.
This schoolwide Google Sheet tracks student progress in writing across informational, argumentative, and narrative genres. By visualizing individual growth against personalized goals and color-coding progress, the tracker helps teachers pinpoint student needs, measure intervention impact, and recognize growth over time—including outcomes related to AI-supported instructional shifts.
This internal slide deck outlines Eliot’s process for mid-year data analysis, from identifying schoolwide trends to targeting subgroup needs. It reflects how the school uses structured data reviews to inform instructional shifts—such as customizing small group lessons using AI tools—and maintain a strong continuous improvement cycle tied to real student outcomes.
This Padlet board captured collaborative learning from Eliot’s AI-focused text club, where educators explored foundational questions about AI’s role in teaching and learning. The forum offered a supportive, low-pressure space for teachers to develop shared understanding, raise implementation questions, and connect AI to instructional goals, laying the cultural groundwork for future innovation.
