A Hybrid BERT-GRU Model for Predicting Language Learning Outcomes of English Learners Using Interactive Artificial Intelligence Instructors

Increasing use of artificial intelligence (AI) in English language learning has created the need for reliable predictive models that can evaluate learner outcomes in adaptive educational settings. This study introduces a hybrid Bidirectional Encoder Representation from Transformer-Gated Recurrent Unit (BERT- GRU) framework that combines BERT-based contextual semantic embedding with GRU-based temporal sequence modeling to predict personalized learning effectiveness. The model is trained on the Adaptive English Learning Dataset, which contains 4,931 classroom interaction sessions and 26 multimodal features, including learner behavior, instructor attributes, and environmental conditions. The dataset captures various learning-related signals like engagement patterns, performance indicators, and instructional context, allowing for thorough modeling of learner progress. The proposed architecture merges deep contextual language representations with sequential learning dynamics to improve prediction reliability and strength. BERT extracts rich contextual embeddings from learner interactions, while GRU captures time dependencies across sequential learning sessions. The main technical contributions of this study include a hybrid embedding, sequence fusion strategy, a structured feature engineering pipeline, and an optimized BERT, GRU architecture for educational outcome prediction. These elements work together to effectively combine semantic and temporal information, enhancing predictive performance in adaptive learning environments. Experimental results show strong performance with an accuracy of 95.17