Resources
Resources
Guides, references, and where to go deeper after the workshops
Interactive Tool: AI Environmental Footprint Explorer
How much energy, water, and carbon does AI use? The AI Environmental Footprint Explorer compares the footprint of a chatbot question, an agentic task, a video clip, and a year of use, with ranges and sources instead of single numbers, and shows where the experts disagree. Figures are updated as new measurements come out; the changelog records what changed and why.
For the activities that go with it, see the Environmental Sustainability topic in the Governance, Ethics & Equity track.
Drexel Tools & Access
- AI at Drexel — the university’s central AI hub (start here).
- AI Tools + data classification — which tools are approved for which data (authoritative).
- ChatGPT Edu — university-licensed; primary tool for the workshops, available to faculty, professional staff, and students. Use the current default model in the Drexel workspace. Request access. See also Tools & Access.
- Microsoft Copilot Chat with Enterprise Data Protection — sign in with your Drexel M365 account; Drexel lists it as usable with High Risk Data when Enterprise Data Protection is active.
- Microsoft 365 Copilot — the paid Copilot inside Word, Excel, PowerPoint, Outlook, and Teams. Drexel does not currently provide this license; use Copilot Chat and paste or upload your content instead.
- Zoom AI Companion — included with Drexel Zoom; meeting summaries, recaps, and action items. Drexel lists it as High Risk Data when not recorded or transcribed and Low Risk Data when recorded or transcribed.
- Adobe Acrobat AI Assistant, PDF Spaces, Creative Cloud, and Express — document and visual tools; Drexel lists them as usable with Moderate Risk Data. Featured in the Documents & Visuals with Adobe elective.
Drexel Policy & Training
- Guidance and Policies — university policy and best practices.
- Guidance for Staff — staff-specific guidance.
- AI Trainings — Drexel workshops and professional development.
- Standing Committee on AI (SCAI) — questions about AI at Drexel.
- For research data: consult your IRB and any data use agreements before entering data into any AI tool.
Quick Reference
PTCF prompt recipe — Persona, Task, Context, Format. Name who the AI should be, what to do, the background it needs, and the shape of the output.
Data self-check — What is the data classification? Does Drexel list this tool and use case for that classification? Could I use a de-identified or synthetic version? If any answer gives you pause, stop and check.
Verify before it counts — AI can state wrong facts and invent citations. Check anything that goes into a real document or decision.
Good First Uses by Role
| If you… | Try AI for… |
|---|---|
| Manage admin, schedules, grants | Drafting emails and reminders, summarizing long threads, cleaning up notes into agendas, reformatting documents |
| Coordinate research | Meeting recaps with action items, drafting project updates, summarizing protocols and reports, first-draft plans |
| Teach and run a lab / faculty | Drafting and revising materials, summarizing literature on public topics, generating practice questions, reformatting slides |
| Study in an MPH, MS, or doctoral program | Understanding dense readings, structuring an argument before you write it, checking your own draft for gaps, practicing methods explanations—within your course AI policy |
Public Health & AI — Further Learning
- ASPPH — AI for Public Health Framework Report — the association’s framework for responsible, ethical AI use in public health research, practice, and education (the five-pillar framework).
- World Health Organization (2021). Ethics and Governance of Artificial Intelligence for Health. Available online — authoritative, freely available guidance; the executive summary (pp. 1–12) is a good starting point.
- Mollick, E. (2024). Co-Intelligence: Living and Working with AI. — accessible introduction to working with AI tools. His blog, One Useful Thing, has regular, practical updates on AI developments.
For Research Staff Who Code and Analyze
The material once planned as a separate research-staff session is now available self-paced, and goes further than an hour would have:
- Data & Analysis track — data cleaning, coding in R/Stata/Python, statistical interpretation, qualitative analysis, dashboards, and image annotation, from novice to advanced.
- Technical & Agentic track — reusable skills, context engineering, agents, loops, local models, RAG, and connecting tools with MCP.
A Data & Code for Research Staff elective is planned to sit on top of these. Email daq26@drexel.edu to suggest what it should cover.
Suggest a Topic
The core sessions repeat every fall and spring, and the elective list keeps growing. If there’s a tool, task, or workflow you’d like covered, email daq26@drexel.edu.