Using generative AI for interview simulations to enhance student research skills in biology education.

Abstract

The Longevity Games Interview Simulator provides an innovative approach to preparing students for real-world research interactions by leveraging the capabilities of large language models (LLMs) like OpenAI's GPT-4o and Claude-3.7. This paper outlines the development and demonstrates the benefits of the simulator, designed to mimic interviews with older adults to enhance students' interviewing skills, empathy, and cultural competence. Key outcomes included preparing students for real-world interactions with interview subjects, improving their ability to identify and properly document protected health information (PHI), gaining experience in asking relevant follow-up questions, and directing conversations to achieve interview goals. The simulator used generative AI models to create realistic interview scenarios based on demographic data from Rochester, NY. Components of the simulator included a student interview-question selection and creation portion, an interview-guide worksheet, a post-simulation quiz on the materials, and a reflective exercise focusing on information gathering and ethical considerations regarding PHI. This tool was designed for the Science of Aging course's CURE (Course-Based Undergraduate Research Experience) to provide students with practical, repeatable interview practice. A small pilot study with senior nursing students indicated that the simulator improved students' confidence, preparedness, and understanding of ethical considerations. This paper also discusses how the simulator has potential for adaptation across educational contexts and encourages educators to develop their own custom interview simulations.