Large Language Model-Based Student Intent Classification for Intelligent Tutoring Systems
Malshan Keerthichandra, Tharoosha Vihidun, Supun Lakshan, Indika N. Perera · 2024
Intent classification is a foundational element in natural language processing, enabling conversational systems to accurately interpret user intent. In educational contexts, effective intent classification within Intelligent Tutoring Systems (ITS) can significantly enhance personalized student interactions. This paper presents the intent classification module for the Learner-Aware AI (LAAI) tutor, a dialogue-based ITS designed to recognize and respond to diverse student behaviors, such as valid answers, questions, expressions of boredom, and requests for clarification. We introduce LAAIIntentD, a custom data set specifically designed for this task, containing 1,244 labeled training records and 278 evaluation records. Leveraging this dataset, we fine-tuned a large language model (LLM) LAAI-intent-classifier using Low-Rank Adaptation (LoRA) techniques to create a lightweight yet powerful intent classifier. Our fine-tuned model achieves better overall Recall (0.86), Precision (0.85), and F1-Score (0.83) compared to GPT-based methods. GPT models with CoT and Few-Shot prompting improve Recall but sacrifice F1 scores. This highlights our model's efficiency in balancing accuracy and scalability for ITS applications.