The supplementary material includes a GitHub repository with two subfolders—‘aiPipeline-SDSS2025’ and ‘autoreportsPipeline-SDSS2025’—corresponding to the Audio Pipeline and the Report Generation Pipeline + Automated Report described in the manuscript. While the original implementations relied on secure cloud infrastructure, the materials provide insight into system architecture, key processing steps, and expected outputs. Included are mock data, configuration examples, prompts, crosswalks, and selected code, enabling users to review and execute sample scripts to understand each pipeline stage. The supplementary materials also include an additional R Markdown (RMD) file that demonstrates report generation using synthetic school-level data, illustrating how qualitative and quantitative inputs are combined within the automated reporting workflow. Due to reliance on internal systems and proprietary authentication, some components (e.g., secure dataset access, organizational credentials, private APIs) are non-functional outside production. Code exposing security-sensitive logic or deployment details has been removed, but the materials still convey the overall design and practical implementation. Additional documentation in each folder guides navigation of outputs. See
Advances in AI and automation are reshaping qualitative research workflows, making processes more efficient, accurate, consistent, and scalable. This paper presents innovations developed for the Illinois Needs Assessment project, a statewide initiative led by the Illinois State Board of Education and the American Institutes for Research to conduct comprehensive needs assessments for schools that need intensive or comprehensive support. To address the scale and tight timeline requirements of the project, the team designed three interconnected pipelines that work together to produce a finalized report. The first, an Audio Pipeline, uses Whisper and generative AI to automate transcription, text-based speaker role attribution, thematic coding, and insight generation from focus groups and interviews. The second, a Report Generation Pipeline, integrates Airtable automations with AWS infrastructure to produce customized school reports that merge AI-generated findings with survey data, school performance metrics, and contextual comparisons. Third, the Needs Assessment Summary Report automates the assembly of all quantitative and qualitative inputs into a polished, customizable deliverable that combines efficiency with expert review. Together, these pipelines replace ad hoc manual workflows with reproducible, consistent systems that enhance data quality, reduce error, and broaden access for non-technical users. The integrated design demonstrates how automation and generative AI can reduce manual burdens, shorten delivery timelines, and support timely, data-informed, and human-centered decision-making in education.