Developing a Chatbot for Soft Skills Education: Comparing ReLU-LSTM and GloAT-Transformer Models

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Recently, soft skills, like communication, teamwork, and problem-solving, have become more important in higher education. In Moroccan universities, fostering these skills is essential to better prepare students for the demands of the job market and modern work environments. However, traditional educational methods often lack interactive and personalized approaches to develop these competencies. This led us to develop a conversational AI system to support soft skills learning through intent classification using Natural Language Processing (NLP) and Neural Networks. Two deep learning models were developed and evaluated; a ReLU-Enhanced LSTM, optimized for stable sequential processing, and a GloAT-Transformer model leveraging global self-attention to capture contextual relationships.

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