Using AI to Improve School Needs Assessments

Advances in artificial intelligence (AI) and automation are changing how researchers conduct qualitative research. These tools can speed up time‑consuming early tasks—like transcription and initial coding—so researchers with deep K-12 expertise and methodological training can focus on the relationship‑building, interpretation, and judgment that cannot be automated.

This work highlights new technical tools developed for the Illinois Needs Assessment project, a statewide initiative led by the Illinois State Board of Education in partnership with AIR. The project supports schools identified for intensive or comprehensive improvement by providing in‑depth, evidence‑based needs assessments.
 

The Data Collection Process

Data collection involves surveying teachers and school leaders and two-day site visits that involve focus groups, interviews, and classroom observations. Afterward, AIR researchers bring together qualitative data, school administrative records, and survey results to build a detailed picture of each school’s strengths, effective practices, and areas for growth. These findings are documented in a Needs Assessment Summary Report, which helps school and district leaders plan improvements and allocate resources.
 

How the Tools Work

To produce detailed reports quickly while still providing meaningful feedback, AIR’s Data Science Team developed three connected tools:

• An audio pipeline that uses automatic speech recognition and generative AI to transcribe interviews and focus groups, identify speakers, and categorize key themes, supporting the early stages of qualitative coding.
• A report generation pipeline that combines Airtable automations with AWS infrastructure to create customizable school reports.
• An automated report assembly process that pulls together relevant data into a customizable needs assessment report.

By automating early steps in qualitative analysis and report production, these tools make the process more efficient, consistent, and scalable. Most importantly, they free up researchers’ time to focus on what matters most: working closely with school and district leaders, analyzing data thoughtfully, and providing clear, actionable feedback grounded in evidence‑based improvement frameworks.