Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale
Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and instructional processes. However, traditional qualitative analysis remains a labor-intensive bottleneck, severely limiting the scale at which this research can be conducted. We present Sandpiper, a mixed-initiative system designed to serve as a bridge between high-volume conversational data and human qualitative expertise. By tightly coupling interactive researcher dashboards with agentic Large Language Model (LLM) engines, the platform enables scalable analysis without sacrificing methodological rigor. Sandpiper ...
| Authors | Daryl Hedley, Doug Pietrzak, Jorge Dias et al. |
|---|---|
| Primary category | cs.HC |
| Published | 9 March 2026 |
| Last updated at source | 5 April 2026 |
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