Methods & Study Design Track
Self-paced track · 4 topics · moderate → advanced
AI support for the research methods public-health staff use most — designing instruments, evaluating programs, working with geospatial data, and protecting participants through de-identification and synthetic data. Start from Core 1; keep data rules central, since this track touches real study data.
Difficulty levels
Novice getting started — no prior experience needed. Moderate comfortable with some AI use. Advanced experienced users going deep.
Topics
| # | Topic | Level | You’ll be able to… |
|---|---|---|---|
| 1 | Survey & Instrument Design | Moderate | Draft items and scales, then pressure-test them |
| 2 | Program Evaluation | Moderate | Logic models, indicators, and evaluation plans, drafted with AI |
| 3 | De-identification & Synthetic Data | Moderate | Make data safe to share or teach with — and know the limits |
| 4 | Geospatial / GIS & Built Environment | Advanced | Mapping, spatial joins, and imagery for environmental health |
Not sure where to start?
Answer a few questions to find your starting point in this track.
Use public or non-sensitive data for every activity, and match any tool to Drexel’s data classification. See Tools & Access and Drexel’s AI Tools page. Study data is stricter: never in commercial/consumer AI tools, and even aggregate or de-identified study data needs the PI’s approval and must respect the IRB protocol, DUA, and the data’s terms of use — see the Research Data callout on Tools & Access.
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