The Comprehensive District AI Platform

Outline

fill me in

Outcomes

After this session, participants will be able to:

- Identify how a unified district AI platform supports teachers, students, and administrators across the full instructional cycle — from lesson design to leadership decisions
- Evaluate AI-generated lesson plans, differentiated materials, assessments, and feedback workflows against their own instructional standards and district priorities
- Assess the governance and visibility tools available to district leaders, including teacher usage data, student AI interaction monitoring, and administrator communication features
- Apply a framework for district-wide AI adoption grounded in research — using district documents, curricula, and policy uploads to customize platform behavior from day one
- Distinguish between AI tools that add to educator workload and infrastructure designed to reduce overhead while keeping professional judgment at the center of every decision

Supporting research

Liu, A., & Sun, M. (2025). From voices to validity: Leveraging large language models (llms) for textual analysis of policy stakeholder interviews. AERA Open, 11, 23328584251374595.

Sarkar, S., Liu, A., Shapiro, R. B., & Sun, M. (2025). Collaborative and Adaptive Learning: Designing Ai Educational Systems With and for Educators. In Rajala, A., Cortez, A., Hofmann, R., Jornet, A., Lotz-Sisitka, H., & Markauskaite, L. (Eds.), Proceedings of the 19th International Conference of the Learning Sciences – ICLS 2025 (pp. 3150-3152). International Society of the Learning Sciences.

Tian, Z. V., Esbenshade, L., Liu, A., Sarkar, S., Zhang, Z., He, K., & Sun, M. (2025). Rubric Generation in Colleague AI: Transforming Assessment in Education. Social Innovations Journal, 30.

Tian, Z., Liu, A., Esbenshade, L., Sarkar, S., Zhang, Z., He, K., & Sun, M. (2025, October). Implementation Considerations for Automated AI Grading of Student Work. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers (pp. 9-20).

Tian, Z. V., Sun, M., Liu, A., Sarkar, S., & Liu, J. (2025). Instructional improvement: leveraging computer-assisted textual analysis to generate insights from educational artifacts. In Research Handbook on Classroom Observation (pp. 190-207). Edward Elgar Publishing.

Liu, A., Esbenshade, L., Sarkar, S., Tian, V., Zhang, Z., He, K., & Sun, M. (2025). How K-12 Educators Use AI: LLM-Assisted Qualitative Analysis at Scale. arXiv preprint arXiv:2507.17985.

Liu, A., Esbenshade, L., Sarkar, S., Tian, V., Zhang, Z., He, K., & Sun, M. (2025). Decoding Instructional Dialogue: Human-AI Collaborative Analysis of Teacher Use of AI Tool at Scale. arXiv preprint arXiv:2507.17985.

Liu, A., Esbenshade, L., Sun, M., Sarkar, S., He, J., Tian, V., & Zhang, Z. (2025). Adapting to Educate: Conversational AI’s Role in Mathematics Education Across Different Educational Contexts. arXiv preprint arXiv:2503.02999.

Liu, A., Sarkar, S., Esbenshade, L., Tian, V., He, J., Zhang, Z., & Sun, M. (2025). From practice to nudge: A hybrid intelligence framework for instructional decision support. Manuscript accepted at the HHAI 2025 Workshop on Designing a Research Agenda for Responsible AI-Supported Behaviour Change.

Sarkar, S., Sun, M., Liu, A., Tian, Z., Esbenshade, L., He, J., & Zhang, Z. (2025). Connecting feedback to choice: Understanding educator preferences in GenAI vs. human-created lesson plans in K–12 education – A comparative analysis. arXiv. https://arxiv.org/abs/2504.05449