Mashiur Khan
Student,
Vanderbilt University
Feedback Loops and Outcomes: Building an AI Assistant to Support Educational Evaluation in Nursing
Wednesday, October 29, 2025 | 3:15PM–4:00PM CT | EDUCAUSE Commons, Enterprise Central, Poster Area
Session Type:
Poster Session
Delivery Format:
Poster
Educators and researchers are exploring methods for interacting with AI-based tools, endeavoring to integrate these approaches into educational practice in meaningful ways. Future-FLO at Vanderbilt School of Nursing is a research lab focusing on the development of AI Assistants to support Feedback Loops and Outcomes (FLO). Focused on applying large language models for evaluation in nursing education, the lab’s current research includes development of a Retrieval Augmented Generation (RAG) chatbot that evaluates health equity content and course materials within nursing and public health education. Using a rubric based on comprehensive review of the health equity literature, the assistant is capable of assessing presentations and providing constructive feedback. This guidance supports instructors in refining teaching materials in clearer and more sustainable ways. As the chatbot was developed, researchers and developers found that 1) it is important to choose a task that the AI Assistant can do 2) creating a context knowledge base for the chatbot is crucial and 3) effective communication between the AI and educators is key to producing accurate and useful results. For the AI-based assistant to work well, prompts and instructions must be clear and focused. When educators work with AI-based tools, they need to clearly explain what they want to achieve and the results they are expecting. It is possible to improve outputs over time and align them to education evaluation priorities.
