Exploring Effective Strategies for Building a Customised GPT Agent for Coding Classroom Dialogues (2506.07194v1)

Abstract: This study investigates effective strategies for developing a customised GPT agent to code classroom dialogue. While classroom dialogue is widely recognised as a crucial element of education, its analysis remains challenging due to the need for a nuanced understanding of dialogic functions and the labour-intensive nature of manual transcript coding. Recent advancements in LLMs offer promising avenues for automating this process. However, existing studies predominantly focus on training large-scale models or evaluating pre-trained models with fixed codebooks, which are often not applicable or replicable for dialogue researchers working with small datasets or customised coding schemes. Using GPT-4's MyGPT agent as a case, this study evaluates its baseline performance in coding classroom dialogue with a human codebook and examines how performance varies with different example inputs through a variable control method. Through a design-based research approach, it identifies a set of practical strategies, based on MyGPT's unique features, for configuring effective agents with limited data. The findings suggest that, despite some limitations, a MyGPT agent developed with these strategies can serve as a useful coding assistant by generating coding suggestions.

• The paper’s main contribution is the design-based customization of a GPT-4 agent using structured prompts to automate classroom dialogue coding.
• The study found that increasing training data up to a threshold significantly enhances accuracy, with diminishing returns beyond 120 examples due to token limits.
• Optimized strategies like segmented decision trees and modular prompts improve agent performance in applying the CDAS framework for dialogue analysis.

Analyzing Strategies for Customizing GPT Agents in Coding Classroom Dialogues

The paper "Exploring Effective Strategies for Building a Customised GPT Agent for Coding Classroom Dialogues" explores the potential of customizing a GPT-4-based MyGPT agent for coding classroom dialogues. This research addresses the challenges inherent in the manual coding of classroom dialogues—a vital component of educational research—by leveraging automated strategies with LLMs.

Classroom dialogue analysis often relies on structured coding schemes, such as the Scheme for Educational Dialogue Analysis (SEDA) and its successor, the Cambridge Dialogue Analysis Scheme (CDAS). These frameworks offer valuable insights into the dynamics of classroom exchanges. However, coding via these frameworks involves manual labor, susceptibility to human error, and extensive training, posing barriers to its systematic application.

The paper presents a design-based approach to developing a customized GPT agent capable of utilizing the CDAS framework, aiming at bridging the gap between resource-intensive AI applications and practical viability in educational research. The research poses three primary questions regarding the performance of a MyGPT agent configured with CDAS, the effects of training data size, and potential strategy optimizations for effective model building.

Study Methodology and Key Outcomes

The authors used a controlled variable approach to evaluate the MyGPT agent's coding efficacy, manipulating the amount and nature of the example data used for training. Performance metrics were based on confusion matrix data assessing true and false positives/negatives.

1. Baseline Performance: The baseline assessment demonstrated limited accuracy, with code "Reasoning" (RE) showing the highest precision at 67.2%, but most other categories showed much lower precision, indicating a need for enhanced fine-tuning.
2. Impact of Data Size: Experimentation with different training data sizes revealed that while increasing data size from 12 to 120 examples resulted in notable performance improvements, further increases to 500 examples provided diminishing returns. This suggests token limits inherently constrain the MyGPT agent’s efficiency once a certain threshold of training complexity is reached.
3. Optimized Strategies: The authors provided evidence that segmented instructions, such as decision trees and modular prompts, significantly improved agent performance. These strategies mitigated cognitive load and facilitated structured processing, aligning with cognitive psychology principles for data chunking and hierarchical decision-making.

Theoretical and Practical Implications

The implications of this research are promising for those engaged in classroom dialogue analysis who may lack significant datasets or technical resources. The paper indicates the feasibility of creating effective coding assistants using resource-efficient strategies rather than relying on expensive, large-scale AI infrastructures. It emphasizes the importance of structuring prompts and instructions to exploit LLM capacities efficiently, which could democratize access to AI-driven dialogue analysis tools for educators and researchers globally.

Future Directions

The paper opens several avenues for future research, particularly in exploring additional prompt-engineering strategies and extending the tested framework to other dialogue analysis schemas differing from CDAS. The insights on data contextualization and decision tree integration could enhance GPT applications in domains beyond educational dialogues, calling for broader investigations into model training and instruction optimization techniques across various linguistic processing tasks.

In conclusion, while significant constraints exist due to technical limitations and contextual specificity, the paper contributes meaningfully to ongoing discourse on LLM customization and accessibility. It underlines a strategic paradigm shift from data-heavy training towards nuanced, cognitive-aware AI configurations to bridge existing resource gaps in qualitative research practices.