Learning Paths by Role
Learning Paths by Role
Not sure where to start? Find your role below and follow a curated sequence of workshops and self-paced topics.
Everyone begins with Core 1: Foundations & Capabilities and Core 2: Workflow Enhancement — they cover the prompting basics and Drexel data rules that everything else builds on. After that, your path depends on what you do.
Pick the role closest to yours, then work through the recommended sequence. Each entry links to either a session page or a self-paced track topic. You don’t have to do them all — start with the ones marked start here and add from there.
Admin & Operations Staff
You manage schedules, budgets, reports, events, accreditation documents, and communications. AI helps most with drafting, reformatting, and summarizing — the recurring tasks that eat your week.
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Summarize, rewrite, and make tables with ChatGPT Edu and Copilot Chat; save reusable prompts |
| 3 | Copilot for Email in Outlook | Self-paced | Summarize threads, draft replies, and check tone where your email already lives |
| 4 | Data Security & Classification | Self-paced | You handle student records, budgets, and HR data — classification matters |
| 5 | Verifying AI Output | Self-paced | Catch errors before they reach a report or accreditation document |
| 6 | Core 3: Reusable Assistants | Core session | Reusable prompts and shared templates for your team |
| 7 | Design & Visual Communication | Self-paced | Flyers, event materials, and accessible visuals |
| 8 | Responsible AI | Self-paced | Disclosure norms and accountability for AI-assisted work |
| 9 | Grant Administration with AI | Self-paced | Deadline tracking, compliance checks, and budget formatting |
Cores 1–3 plus Copilot for Email, Data Security, and Verifying Output cover 80% of what admin staff need. The rest is useful but not urgent.
Research Staff & Coordinators
You manage data, run analyses, coordinate IRB submissions, track grant deadlines, and draft reports. AI accelerates the data pipeline and the writing that surrounds it. One hard rule first: no study data in commercial/consumer AI tools — PII and HIPAA data never — 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).
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Summarizing, drafting, and formatting in your daily tools |
| 3 | Data Cleaning | Self-paced | Diagnose and fix messy data faster |
| 4 | Data Security & Classification | Self-paced | You work with identifiable data — know the boundaries |
| 5 | De-identification & Synthetic Data | Self-paced | Make data safe for tools or sharing |
| 6 | Coding with AI | Self-paced | Generate, debug, and translate code (R, Stata, Python) |
| 7 | Statistical Interpretation | Self-paced | Translate models across languages and interrogate output |
| 8 | Verifying AI Output | Self-paced | Catch fabricated citations and wrong statistics |
| 9 | Core 3: Reusable Assistants | Core session | Reusable prompts for recurring research tasks |
| 10 | Literature Review | Self-paced | Search strategy and evidence synthesis |
Start with Cores 1–2, then Data Cleaning and Data Security. Add Coding with AI and Statistical Interpretation once you’re comfortable.
Teaching Faculty
You design courses, write syllabi, create assignments, advise students, and communicate findings to multiple audiences. AI helps with content creation, assessment design, and plain-language translation.
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Drafting, summarizing, and formatting for teaching tasks |
| 3 | Science Communication | Self-paced | Translate findings for students and the public |
| 4 | Verifying AI Output | Self-paced | Model verification habits for your students |
| 5 | AI for Curriculum Development | Elective | Assignment design and AI policies for your courses |
| 6 | Writing Your Course AI Policy | Self-paced | A clear, enforceable policy for your syllabus |
| 7 | Assignment & Assessment Design | Self-paced | Assignments that work with or despite AI |
| 8 | Setting Up Your Course with AI | Self-paced | Groups from a survey and bulk Canvas updates, with student records de-identified first |
| 9 | Responsible AI | Self-paced | Disclosure norms — for you and your students |
| 10 | AI Bias & Health Equity Audit | Self-paced | Teach students to test AI across groups |
| 11 | Multilingual Materials | Self-paced | Translate and adapt materials for diverse learners |
Cores 1–2, then Science Communication and Verifying Output. Before fall semester, complete the Curriculum Development elective and the Course AI Policy topic.
Research Faculty (PI / Co-I)
You design studies, write grants, publish papers, and lead research teams. AI helps most with brainstorming, literature synthesis, and critiquing your own drafts — but the stakes of errors are high. As PI you also set the rule for your team: what study data, if any, may touch an AI tool, within the IRB protocol, DUAs, and each source’s terms of use (see the Research Data callout on Tools & Access).
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Drafting and summarizing in your daily tools |
| 3 | Literature Review | Self-paced | Search, synthesize, and verify — not fabricate |
| 4 | Verifying AI Output | Self-paced | Catch hallucinated citations before they reach a manuscript |
| 5 | Grant Proposal Development | Self-paced | Draft aims, abstracts, and reviews of your own work |
| 6 | Manuscript Preparation & Peer Review | Self-paced | Tighten manuscripts and structure reviewer responses |
| 7 | Statistical Interpretation | Self-paced | Translate models and interrogate output |
| 8 | AI Bias & Health Equity Audit | Self-paced | Test for disparities in AI-assisted analyses |
| 9 | Responsible AI | Self-paced | Disclosure, accountability, and governance frameworks |
| 10 | Coding with AI | Self-paced | Generate and debug analysis code |
Cores 1–2, then Literature Review and Verifying Output. Add Grant Proposal Development and Manuscript Prep when you’re writing.
Practice & Community-Facing Roles
You work with communities, run programs, create outreach materials, or do practice-based work. AI helps with communication, evaluation, and materials — but cultural competence and community trust are non-negotiable.
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Drafting and formatting for outreach materials |
| 3 | Science Communication | Self-paced | Plain-language materials for community audiences |
| 4 | Multilingual Materials | Self-paced | Translate and culturally adapt, not just swap words |
| 5 | Design & Visual Communication | Self-paced | Flyers, social posts, and accessible visuals |
| 6 | Program Evaluation | Self-paced | Logic models and evaluation plans for your programs |
| 7 | AI Bias & Health Equity Audit | Self-paced | Test AI for disparities before using it with communities |
| 8 | Verifying AI Output | Self-paced | Don’t let AI errors reach communities |
| 9 | Data Security & Classification | Self-paced | Community data is often sensitive — know the rules |
| 10 | Keeping Your Judgment | Self-paced | Community relationships require human judgment, not AI shortcuts |
Cores 1–2, then Science Communication and Multilingual Materials. AI Bias & Equity Audit is essential before deploying any AI-assisted tool in a community context.
Mentoring & Advising
You advise students, mentor junior colleagues, write recommendation letters, and navigate sensitive conversations. AI can help with some of these tasks, but judgment, confidentiality, and the personal relationship are paramount.
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Drafting and summarizing for advising tasks |
| 3 | AI in Mentoring & Advising | Self-paced | Where AI helps and where it has no place |
| 4 | Responsible AI | Self-paced | Disclosure norms — when to tell a student AI helped |
| 5 | Data Security & Classification | Self-paced | Student records are FERPA-protected — know the limits |
| 6 | Verifying AI Output | Self-paced | Don’t pass AI errors along to advisees |
| 7 | Keeping Your Judgment | Self-paced | Mentoring is a human relationship — keep it that way |
| 8 | Writing Your Course AI Policy | Self-paced | Help students understand where AI is and isn’t appropriate |
Cores 1–2, then Mentoring & Advising and Responsible AI. Data Security is essential for anyone handling student records.
Graduate Students
You are writing papers, reading more than anyone can read, learning methods, and — if you’re on a thesis or dissertation — producing work that will carry your name into the literature. ChatGPT Edu became available to Drexel students in August 2026, so the tools are there. What isn’t automatic is knowing where they help, where they quietly cost you the skill you came here to build, and what your instructors, your committee, and eventually journals and funders expect you to disclose. Start with the coursework path; add the thesis path when your own project starts. One rule above all: AI use in any credit-bearing work — courses, the APE, the ILE/capstone — is set by the instructor or advisor responsible for it (Drexel’s guidance keeps that decision at the course level); when in doubt, ask first.
Coursework (MPH / MS)
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | Core 1: Foundations | Core session | Prompting basics and the Drexel data rules — start here |
| 2 | Core 2: Workflow Enhancement | Core session | Summarize readings, rewrite drafts, and make tables with ChatGPT Edu and Copilot Chat; save the prompts that work for your courses |
| 3 | AI in Your Coursework | Self-paced | Read your syllabus AI policies accurately, disclose specifically, and ask before you submit — do this in week one |
| 4 | Verifying AI Output | Self-paced | A fabricated citation in a course paper is your name on it, not the model’s |
| 5 | Keeping Your Judgment | Self-paced | The skills you’re here to build are exactly the ones easiest to hand away — this is the topic to read early, not late |
| 6 | Literature Review | Self-paced | Search strategy and synthesis for course papers and your first real review |
| 7 | Data Analysis Basics | Self-paced | Get unstuck in your methods and biostat courses without letting AI do the reasoning for you |
| 8 | Writing Your Course AI Policy | Self-paced | Written for instructors — read it to understand how your syllabus policies get set, so you can read yours accurately and ask better questions |
| 9 | Data Security & Classification | Self-paced | Practicum and assistantship data is often someone else’s to protect — know before you paste |
Cores 1–2, then Verifying AI Output and Keeping Your Judgment. Read your syllabus AI policy first, every term — it outranks anything on this site.
Thesis, Dissertation & Doctoral Work
Everything in the coursework path, plus:
| Order | Topic | Type | Why it matters for you |
|---|---|---|---|
| 1 | AI for Your Thesis or Dissertation | Self-paced | The orientation page for this path: the “AI critiques, never drafts” rule applied to a thesis, the questions to ask your advisor now, and citation verification before a chapter carries your name |
| 2 | Grant Proposal Development | Self-paced | F31s, dissertation awards, and training-grant sections — and the funder disclosure rules that come with them |
| 3 | Manuscript Preparation & Peer Review | Self-paced | Build the habit now: AI critiques your draft, it never writes it. Learn that ethos here, before a journal or funder policy makes it a formal problem |
| 4 | Qualitative Analysis or Statistical Interpretation | Self-paced | Pick by your methods — coding and thematic work, or interrogating model output you have to defend |
| 5 | Core 3: Reusable Assistants | Core session | Turn the prompts that work into something you and your lab reuse across a multi-year project |
| 6 | Core 4: Verify, Disclose, Decide | Core session | Verification you practice, what funders and journals now require, and how to disclose — go before your first submission, not after |
Manuscript Preparation & Peer Review and Core 4 are the two that protect your record. Do them before you submit anything, and talk with your advisor about what your committee expects — norms vary by field and by person.
Start with Cores 1 and 2 — everyone needs those. Then try the Verifying AI Output topic (it applies to every role) and explore from there. If your role spans multiple paths, pick topics from each that match your actual work.