Smart Tutoring Systems Using Interactive Natural Language Understanding with Transformer-Based Models

Due to the growing intricacy in today's educational settings, intelligent adaptive tutor systems need to be developed that can deliver personalized tutoring services on a large scale. This research paper highlights a new idea for designing a Smart Tutoring System (STS) using Interactive Natural Language Understanding (INLU) that is combined with transformer-based deep learning models in order to offer contextualized feedback and adaptability for users. The STS uses a BERT-based language model that is trained on educational data consisting of over 120,000 question-answer pairs pertaining to five academic subjects, which include mathematics, science, language arts, history, and computer programming. For the evaluation of the methodology, a multi-staged approach was adopted, which consists of steps such as intention classification, similarity matching, traversal of the knowledge graph, and feedback generation using reinforcement learning. The experiments performed on the ATIS-Edu benchmark dataset and the own Educational QA dataset show that the model has obtained 94.7% accuracy, 93.8% F1-Score, 94.1% precision, and 93.5% recall scores, which are significantly higher than state-of-the-art baselines such as a GPT-2-based tutoring system (88.4% accuracy) and an LSTM-based NLU (82.1% accuracy). Moreover, the model provides a mean latency reduction of 38% in student response time in comparison to traditional rule-based intelligent tutoring systems. Ablation experiments prove the individual contribution of each component such as the transformer encoder, knowledge graph, and reinforcement feedback towards improving the model’s performance. This reaffirms the viability of transformers in developing educational technology solutions on a large scale and sets a new standard for intelligent tutoring systems in virtual learning environments. Possible directions for future research involve language support and emotional intelligence in edge computing platforms.