Sarah Alaghbari

Papers

Total Citations

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About

Sarah Alaghbari’s research lies at the intersection of human-computer interaction, gamification, and machine learning, with a focus on making data annotation more engaging and efficient. Her most cited work, “A User-Centered Approach to Gamify the Manual Creation of Training Data for Machine Learning” (2021, 7 citations), proposes a novel framework that applies game design elements—such as points, levels, and challenges—to the tedious task of labeling data for supervised learning. By prioritizing user experience, she demonstrates how gamification can improve both the quality and quantity of training data, a critical bottleneck in AI development. Her companion study, “Achiever or Explorer?” (2020, 7 citations), further explores how different player personality types respond to gamified annotation tasks, offering tailored strategies to sustain motivation and accuracy. Together, these contributions address a pressing need in computer vision and beyond, where high-quality labeled datasets are essential. Alaghbari’s work is notable for its practical, user-centered lens, bridging the gap between AI scalability and human engagement. Her research has been recognized for its potential to transform data preparation workflows, making her a rising voice in the field of human-AI collaboration.