A Fairness Analysis of Human and AI-Generated Student Reflection Summaries
Bhiman Kumar Baghel, Arun Balajiee Lekshmi Narayanan, Michael Miller Yoder · 2024
This study examines the fairness of human-and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases.Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students.We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others.Our analysis reveals that though humangenerated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students.Pedagogical themes are overrepresented from male reflections in these summaries, while concept-specific topics are underrepresented from female reflections.This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.