# Exploration and adaptation of experience sampling to understand the impact of artificial intelligence tools on the work of teaching

> **NSF 04002526DB NSF STEM Education** · Stanford University (CA) · $499,715

## Abstract

Despite popular discourse about how generative artificial intelligence (AI) tools might increase efficiencies in education, it is yet to be determined how--or even if--AI tools for teachers are having that envisioned effect. There is currently a baseline assumption that AI should increase teacher efficiency through offloading. However, there is limited research to support such claims, and there is reason to believe that AI could increase workload or cause unexpected shifts in teacher responsibilities due to the unique demands of classroom teaching. Researchers need new methodological approaches to understand generative AI's impact on teacher experience and daily work. By exploring and refining research instrumentation to study how AI affects teaching, this project will inform efforts to improve educational practice and ensure that new technologies support rather than burden teachers. The resulting instrumentation will be made publicly available to support broader investigations into how emerging technologies shape the teaching profession.

The goal of this ECR: Level I project is to examine, adapt, and deploy instrumentation that can help researchers investigate generative AI's impact on teacher work and generate necessary information to guide future decisions. Most immediately, the instrumentation will enable closer scrutiny of assumptions about teacher efficiency with AI. The project centers on experience sampling method (ESM), a technique originating from psychology th

## Key facts

- **NSF award ID:** 2500101
- **Awardee organization:** Stanford University (CA)
- **SAM.gov UEI:** HJD6G4D6TJY5
- **PI:** Victor R Lee
- **Primary program:** 04002526DB NSF STEM Education
- **All programs:** AI Education/Workforce Develop, STEM Learning & Learning Environments
- **Estimated total:** $499,715
- **Funds obligated:** $499,715
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2500101

## Citation

> US National Science Foundation, Award 2500101, Exploration and adaptation of experience sampling to understand the impact of artificial intelligence tools on the work of teaching. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2500101. Licensed CC0.

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