Transforming online learning research: Leveraging GPT large language models for automated content analysis of cognitive presence

The last two decades of online learning research vastly flourished by examining discussion board text data through content analysis based on constructs like cognitive presence (CP) with the Practical Inquiry Model (PIM). The PIM sets a footprint for how cognitive development unfolds in collaborative inquiry in online learning experiences. Ironically, content analysis is a resource-intensive endeavor in terms of time and expertise, making researchers look for ways to automate text classification through ensemble machine-learning algorithms. We leveraged large language models (LLMs) through OpenAI's Generative Pre-Trained Transformer (GPT) models in the public API to automate the content analysis of students' text data based on PIM indicators and assess the reliability and efficiency of automated content analysis compared to human analysis. Using the seven steps of the Large Language Model Content Analysis (LACA) approach, we proposed an AI-adapted CP codebook leveraging prompt engineering techniques (i.e., role, chain-of-thought, one-shot, few-shot) for the automated content analysis of CP. We found that a fine-tuned model with a one-shot prompt achieved moderate interrater reliability with researchers. The models were more reliable when classifying students' discussion board text in the Integration phase of the PIM. A cost comparison showed an obvious cost advantage of LACA approaches in online learning research in terms of efficiency. Nevertheless, practitioners still need considerable data literacy skills to deploy LACA at a scale. We offer theoretical suggestions for simplifying the CP codebook and improving the IRR with LLM. Implications for practice are discussed, and future research that includes instructional advice is recommended.