Transforming Academic Excellence:
Leveraging AI for Enhanced Productivity in Teaching and Research
Faculty Research Workshop — University of South Carolina, College of Nursing
Max Topaz, PhD, RN, MA, FAAN, FIAHSI, FACMI
Elizabeth Standish Gill Associate Professor of Nursing | Columbia University & VNS Health
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Follow along below as we move through each session. For questions after the workshop, connect with me on LinkedIn.
What You Told ARIA
Before this workshop, participants completed a brief voice interview with ARIA — an AI Research Interview Assistant. Here's a summary of what we heard and the themes we'll explore together today.
Ethics & Appropriate Use Unanimous
All three participants raised ethics as a top concern — where are the lines for faculty and students?
"Ethical use of AI in research would be my only thought."
— Participant C
AI-Integrated Assignment Design
Redesigning assignments so students develop professional judgment while using AI — multi-step processes that mirror real-world decision-making.
"I would like to redesign some assignments so that they would require development of judgment for students when they're using AI… in a multi-step process require students to submit prompts, and then evaluate the feedback in ways that parallel professional decision-making."
— Participant A
AI for Qualitative Research
Hands-on guidance for using AI to analyze qualitative data from completed studies.
"I would love to learn how to evaluate qualitative data that I have just gotten from a completed survey."
— Participant B
AI for Patient Care & Clinical Applications
Moving beyond LLMs and machine learning — what else can AI do to directly improve patient outcomes?
"Are there other ways that we can use AI for patients to improve patient care?"
— Participant C
AI Disclosure & Transparency Policies
Faculty and students both need clarity on when and how to disclose AI use — institutional frameworks are needed.
"I think we need to talk about the clarity of AI use, our disclosure and the students' disclosures as well."
— Participant B
Discussion Starting Point
Everyone we spoke with is already using AI in teaching. The shared questions are: How do we use it responsibly? How do we help students develop judgment? And how do we move from personal use into research and clinical applications? Let's explore these together today.
Interviews conducted by ARIA (AI Research Interview Assistant) — a voice-based AI interviewer built for this workshop
Registration & Welcome
Grab a coffee and settle in. ☕
Before We Begin
Take 3–5 minutes to chat with ARIA, our AI research assistant. She'll ask you a few quick questions about your experience with AI and what you'd like to learn today — this helps me tailor the workshop to your needs.
Start ARIA InterviewIntroductions & Workshop Overview
Overview of the day's structure, participant introductions, and workshop goals.
Keynote Session
Based on pre-workshop interviews, two keynote options are available — one research-focused, one teaching-focused. Select a tab below to preview each.
AI Advances in Health Research & Grant Funding
Current landscape of AI in healthcare research, emerging funding opportunities, and how AI is reshaping competitive grant proposals.
Interactive Q&A
Open discussion: your research priorities, questions, and concerns about AI in grant-funded research.
Break
Grab a coffee and recharge before the hands-on session. ☕
Hands-On Workshop: AI Tools for Research & Grant Writing
Live demonstrations and participant engagement with AI tools for literature synthesis, grant proposal development, and research productivity.
💻 Participants should have laptops ready for interactive portions.
Unifying Case Study
Dr. James Chen is an assistant professor studying how neighborhood-level social determinants — food access, transportation, housing quality — affect hospital readmission in older adults with heart failure. He's resubmitting an R01 to NINR after his first submission scored well on Significance but was criticized for weak Innovation and Approach. Today we follow James as he uses AI tools to strengthen every section of his resubmission.
Deep Research & Literature Synthesis
AI-powered literature review, evidence synthesis, and gap identification for grant proposals.
James needs to update his literature review and find recent studies that distinguish his approach from existing SDOH-readmission research.
Teaching Point
Deep Research replaces hours of literature searching — but you MUST verify every citation. Hallucination rates for AI-generated references run 10–20%. Always cross-check in PubMed before putting a citation in your grant.
AI for Grant Writing — Custom Claude Skills
Live demonstration: how to define granular formatting guidelines, structure requirements, and writing patterns from your successful grants as reusable AI "skills" — then use them to draft Specific Aims, Research Strategy sections, and reviewer response letters.
James wants to draft a Specific Aims page using an AI skill trained on successful grant formatting patterns.
Teaching Point
The power here isn't generic AI writing — it's that I've encoded the exact patterns from my own funded grants as a reusable "skill." Paragraph structure, gap framing, significance-to-innovation transitions, even sentence-level patterns. You can build these for your own writing style. This makes it YOUR tool, not a template.
Claude Code — Building Tools from Text
Live demonstration: how Claude Code lets you describe what you need in plain English and builds functional tools, websites, and applications directly from your terminal. This workshop website was built the same way.
Teaching Point
You don't need to know how to code. Claude Code turns plain language into working software — from data processing scripts to full websites. The barrier to building custom research tools just dropped to zero.
Research Image Generation
Creating figures, conceptual diagrams, and visual abstracts for grants and publications.
James needs a conceptual framework figure for his R01 — the kind that usually takes hours in PowerPoint.
Teaching Point
You get a starting point in 30 seconds instead of staring at a blank PowerPoint. It won't be submission-ready — you'll refine the layout and verify the scientific accuracy — but the conceptual structure comes together immediately.
Presentation & Slide Creation
Rapidly building polished research presentations and conference slides from outlines.
James needs to present his R01 concept at an internal faculty review meeting next week.
Teaching Point
Gamma builds a first draft in 60 seconds. Perfect for practice talks, lab meetings, and internal reviews — then refine from there instead of starting from scratch.
Learning & Training Materials
Generating study guides, audio overviews, and training materials from your research papers.
James needs to train his research team — two RAs and a community health worker — on the ML methods in his study.
Teaching Point
NotebookLM generates training materials FROM your own sources — audio your team can listen to on a commute, quizzes for RA onboarding. Because content comes from your uploaded papers rather than the model's general knowledge, hallucination risk is significantly lower.
Ethics, Bias & Governance in AI-Enabled Research
Addressing reviewer concerns: bias mitigation, privacy, data governance, and responsible AI integration in grant proposals.
James knows reviewers will raise bias concerns about his ML model. He uses AI to draft mitigation language for his Approach section.
Teaching Point
This is where I show that AI can also be misused. This output looks polished enough that someone could submit it without understanding the statistics. For an experienced researcher like James, it's a drafting accelerator — he knows what SHAP values are and can verify the content. The tool doesn't replace expertise; it speeds up writing about concepts you already understand. That's the academic integrity line.
AI Tools Demo Recording
Afternoon Orientation
Grant Clinic overview, consultation sign-up, and logistics for the afternoon session.
Lunch
On your own. 🍽
AI Integration Lab (Full Group + Breakouts)
Hands-on working session where you apply the morning's AI tools to YOUR work. Choose the track that fits your focus.
Track A: Grant Proposals
For faculty actively writing, revising, or planning grant submissions
Share Your Project
10 minGo around the room. Each person gets 60 seconds:
- ● What's your research topic? (1–2 sentences)
- ● Who's your target funder? (NIH, AHRQ, foundation, internal pilot...)
- ● What's the #1 thing holding your proposal back right now?
Surface Your Questions
10 minWhat do you most need help with? We'll group these together.
Aims & Significance
Methods & Approach
Data & Feasibility
Ethics & Governance
Build with AI
40 minOpen an AI tool. Pick one section of your proposal and work on it right now. For each upgrade you try, think about:
1. What did AI produce?
2. What would a reviewer flag?
3. How do you mitigate that concern?
Report Back
5 min prepPrepare to share with the full group:
- ● Your top AI-enabled upgrade
- ● The biggest risk it introduces and how you'd address it
Report-Out & Cross-Pollination (15 min)
Both tracks reconvene. Each group shares their top upgrades, redesigns, and the risks they identified. The best insights often come from hearing what the other track discovered.
Come Prepared
Bring a laptop, a 1–2 sentence project or course description, and if available, a draft Specific Aims page, concept summary, or current assignment you'd like to redesign.
Individual Consultations & Office Hours
Optional 30–45 minute slots for personalized guidance on AI integration in your grant proposals. Drop-in welcome if slots are available.
Quick Reference
All the tools from today's workshop