AI-Enabled Feedback Management for Enhancing Education - Radha P Sali; Sahil K Patil; Jayesh S Bacchav; Harshal S Patil; Siddesh S Boraste (International Journal of Electronics Automation, 2025)
Institutions are becoming more aware of the importance of student input in improving learning experiences in the current educational environment. However, the intricate and complex patterns found in this feedback are frequently missed by conventional techniques like manual reviews and simple statistics. Our proposal suggests a novel method for analyzing student input and more accurately predicting sentiment by utilizing Long Short-Term Memory (LSTM) algorithms. We can learn more about student experiences and patterns because to LSTM's proficiency with sequential data. By transforming feedback analysis into a thorough, data-driven assessment tool, this novel approach seeks to enhance teaching methods. Furthermore, we use a Generative Pre-trained Transformer (GPT) model to offer dynamic, customized recommendations for students' development. Our technology analyzes comments and provides practical recommendations by merging cutting-edge machine learning algorithms, creating a more encouraging and productive learning environment. This all-encompassing strategy seeks to improve institutional procedures as well as student outcomes
Keywords: Feedback analysis; GPT model; LSTM algorithm; sentiment analysis