Bidirectional Human–AI Collaboration for Equitable Student Performance Prediction via Deep Uncertainty Learning

Abstract

This paper studies a bidirectional human-AI collaborative student performance prediction problem to enhance equitable online education, aligning with the United Nations' Sustainable Development Goal (SDG) of ensuring inclusive and equitable quality education for all. The goal is to leverage collaborative intelligence to generate accurate and fair student outcome predictions from behavioral data, ensuring equitable estimation for underrepresented populations. Current fair AI solutions often fail to mitigate demographic bias in the absence of student demographic data, while human-AI collaborative approaches frequently overlook human cognitive biases, leading to inaccurate predictions. We develop CollabDebias, a novel bidirectional human-AI collaborative framework that utilizes the complementary strengths of AI and humans to mitigate the AI demographic bias and human cognitive bias. To address AI demographic bias, we propose an uncertainty learning-based bias identification method and a reliability-aware human-AI integration approach. To reduce human cognitive bias, we design uncertainty-aware visualization of AI decision area and attention mechanism. Experimental results on an online course demonstrate CollabDebias's effectiveness in improving student performance prediction accuracy and fairness.