Human-AI Collaboration Increases Skill Tagging Speed but Degrades Accuracy
Cheng Ren, Zachary Pardos, Zhi Li

TL;DR
This study investigates human-AI collaboration in educational skill tagging, showing that AI assistance significantly speeds up the process but reduces accuracy and recall, highlighting trade-offs in collaborative AI applications.
Contribution
It provides empirical data on the effects of AI assistance in educational content tagging, revealing speed gains and accuracy trade-offs in a real-world task.
Findings
AI assistance reduced tagging time by 50%
AI collaboration decreased accuracy by 35%
Humans still exercised discernment with AI recommendations
Abstract
AI approaches are progressing besting humans at game-related tasks (e.g. chess). The next stage is expected to be Human-AI collaboration; however, the research on this subject has been mixed and is in need of additional data points. We add to this nascent literature by studying Human-AI collaboration on a common administrative educational task. Education is a special domain in its relation to AI and has been slow to adopt AI approaches in practice, concerned with the educational enterprise losing its humanistic touch and because standard of quality is demanded because of the impact on a person's career and developmental trajectory. In this study (N = 22), we design an experiment to explore the effect of Human-AI collaboration on the task of tagging educational content with skills from the US common core taxonomy. Our results show that the experiment group (with AI recommendations) saved…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education
