Announcing the 2026 STFM Project Fund Recipients

STFM has selected two research projects to receive funding through the STFM Project Fund starting in 2026.

April 13, 2026—STFM has selected two research projects to receive funding through the STFM Project Fund starting in 2026. This program encourages STFM Collaboratives and Special Project Teams to plan, develop, implement, evaluate, and disseminate findings from education-related scholarly projects. Each year, STFM selects projects to receive funding through the fund. The two projects receiving funding starting in 2026 are:

  • Using Artificial Intelligence Large Language Model Chatbots to Optimize Creation and Utilization of Individualized Learning Plans in Family Medicine Residency, led by Tana Chongsuwat, MD, MPH, McGaw Medical Center of Northwestern University/Erie-Humboldt Park Program
  • Building a Pathway for Family Physicians, Residents, and Medical Students to Improve Scholarship, led by Lauren Harriett, DO, MBA, University of Chicago Department of Family Medicine

Each project is receiving $10,000 for 2 years through the STFM Project Fund. Thank you to the researchers for their work on these projects and congratulations to them for receiving this funding. Learn more about each project below.

Project: Using Artificial Intelligence Large Language Model Chatbots to Optimize Creation and Utilization of Individualized Learning Plans in Family Medicine Residency

Individualized Learning Plans (ILPs) are required by the ACGME, yet their effectiveness varies due to inconsistent faculty readiness and challenges incorporating meaningful feedback within competency-based medical education. This project addresses these gaps by evaluating AI-assisted tools to support goal writing and progress tracking in family medicine residency ILPs.Through a mixed-methods randomized controlled trial, the study will compare usual ILP processes with AI-supported approaches to assess satisfaction, goal quality, and perceived support. By reducing administrative burden and enhancing coaching conversations, this work aims to strengthen self-directed learning and prepare residents for lifelong professional development.

Project: Building a Pathway for Family Physicians, Residents, and Medical Students to Improve Scholarship

  • Principal Investigator: Lauren Harriett, DO, MBA, University of Chicago Department of Family Medicine
  • STFM Collaborative(s): Learning Network Leaders Collaborative
  • Award: $10,000 for 2 years

Aligned with the STFM Strategic Plan and ABFM and ACGME recommendations, the Family Medicine Learning Network Leaders Collaborative will create a new pathway for medical students to engage in faculty-mentored scholarly activity within a family medicine learning network.Supporting the National Research Strategy for Family Medicine, the initiative will recruit three medical students from different schools to participate in mentored projects, identify and assess family medicine scholarship champions nationwide, and disseminate findings and resources through conferences, publications, and online platforms. These efforts will strengthen early scholarly engagement, expand the culture of inquiry, build research capacity, and increase peer-reviewed scholarship addressing family medicine priorities.

More About the STFM Project Fund

The STFM Project Fund is coordinated by the STFM Foundation Executive Committee and funded through the STFM Foundation. STFM looks to fund projects that benefit members, STFM, and the discipline of family medicine. The project should also provide students, residents, and new faculty an opportunity to participate in a significant way in a scholarly project that exposes them to STFM.Requirements for funding eligibility are below:

  • The project must be a collaborative effort among two or more STFM members.
  • A student, resident, or new faculty (7 years or less in faculty role) must have a significant role in the project either as a co-principal investigator, project leader, or as a primary recipient of the project activities.
  • The project must be recommended by an STFM Collaborative or Special Project Team.
  • Funds must be disbursed to an entity, not an individual.
  • Projects should attempt to conduct their survey through CERA, if appropriate.
  • Funds may not be used for salary support.

Learn more about the STFM Project Fund, including funding details, selection criteria, and how to submit your project for funding.

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AI Chatbot Tips

Tips for Using STFM's AI Assistant

STFM's AI Assistant is designed to help you find information and answers about Family Medicine education. While it's a powerful tool, getting the best results depends on how you phrase your questions. Here's how to make the most of your interactions:

1. Avoid Ambiguous Language

Be Clear and Specific: Use precise terms and avoid vague words like "it" or "that" without clear references.

Example:

Instead of: "Can you help me with that?"
Try: "Can you help me update our Family Medicine clerkship curriculum?"
Why this is important: Ambiguous language can confuse the AI, leading to irrelevant or unclear responses. Clear references help the chatbot understand exactly what you're asking.

2. Use Specific Terms

Identify the Subject Clearly: Clearly state the subject or area you need information about.

Example:

Instead of: "What resources does STFM provide?"
Try: "I'm a new program coordinator for a Family Medicine clerkship. What STFM resources are available to help me design or update clerkship curricula?"
Why this is better: Providing details about your role ("program coordinator") and your goal ("design or update clerkship curricula") gives the chatbot enough context to offer more targeted information.

3. Don't Assume the AI Knows Everything

Provide Necessary Details:The STFM AI Assistant has been trained on STFM's business and resources. The AI can only use the information you provide or that it has been trained on.

Example:

Instead of: "How can I improve my program?"
Try: "As a program coordinator for a Family Medicine clerkship, what resources does STFM provide to help me improve student engagement and learning outcomes?"
Why this is important: Including relevant details helps the AI understand your specific situation, leading to more accurate and useful responses.

4. Reset if You Change Topics

Clear Chat History When Switching Topics:

If you move to a completely new topic and the chatbot doesn't recognize the change, click the Clear Chat History button and restate your question.
Note: Clearing your chat history removes all previous context from the chatbot's memory.
Why this is important: Resetting ensures the AI does not carry over irrelevant information, which could lead to confusion or inaccurate answers.

5. Provide Enough Context

Include Background Information: The more context you provide, the better the chatbot can understand and respond to your question.

Example:

Instead of: "What are the best practices?"
Try: "In the context of Family Medicine education, what are the best practices for integrating clinical simulations into the curriculum?"
Why this is important: Specific goals, constraints, or preferences allow the AI to tailor its responses to your unique needs.

6. Ask One Question at a Time

Break Down Complex Queries: If you have multiple questions, ask them separately.

Example:

Instead of: "What are the requirements for faculty development, how do I register for conferences, and what grants are available?"
Try: Start with "What are the faculty development requirements for Family Medicine educators?" Then follow up with your other questions after receiving the response.
Why this is important: This approach ensures each question gets full attention and a complete answer.

Examples of Good vs. Bad Prompts

Bad Prompt

"What type of membership is best for me?"

Why it's bad: The AI Chat Assistant has no information about your background or needs.

Good Prompt

"I'm the chair of the Department of Family Medicine at a major university, and I plan to retire next year. I'd like to stay involved with Family Medicine education. What type of membership is best for me?"

Why it's good: The AI Chat Assistant knows your role, your future plans, and your interest in staying involved, enabling it to provide more relevant advice.

Double Check Important Information

While the AI Chat Assistant is a helpful tool, it can still produce inaccurate or incomplete responses. Always verify critical information with reliable sources or colleagues before taking action.

Technical Limitations

The Chat Assistant:

  • Cannot access external websites or open links
  • Cannot process or view images
  • Cannot make changes to STFM systems or process transactions
  • Cannot access real-time information (like your STFM Member Profile information)

STFM AI Assistant
Disclaimer: The STFM Assistant can make mistakes. Check important information.