When LLMs fall short in Deductive Coding: Model Comparison and Human AI Collaboration Workflow Design
Zijian Li; Luzhen Tang; Mengyu Xia; Xinyu Li; Naping Chen; Dragan Gašević; Yizhou Fan · 2025 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
With generative artificial intelligence driving the growth of dialogic data in education, automated coding is a promising direction for learning analytics to improve efficiency. This surge highlights the need to understand the nuances of student-AI interactions, especially those rare yet crucial. However, automated coding may struggle to capture these rare codes due to imbalanced data, while human coding remains time-consuming and labour-intensive. The current study examined the potential of large language models (LLMs) to approximate or replace humans in deductive, theory-driven coding, while
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Metadata source: arXiv