An innovative tool for education: An adversarial dialogue system embedded with humor, empathy and culture

Artificial intelligence (AI) dialogue systems are gaining popularity in education for enhancing user experience and achieving learning outcomes. However, there is a limited study on AI dialogue systems can dynamically generate humorous interactions and empathetic responses to learners' emotions while considering their cultural diversity. This work-in-progress paper presents an interactive adversarial learning system, the MACHE-Bot, based on Wasserstein's generative adversarial network (WGAN) that uses user input to determine whether the generated responses entail humor, empathy, and culture in learning. The significance of the MACHE-Bot is: (1) it can trigger humorous and empathetic interactions and consider learners' cultural varieties, (2) it is able to capture the subtleties of user emotion, (3) it is capable of expressing multimodal humorous responses by recalling prior context and external knowledge, and (4) it is embedded with a corpus of cultural contexts, semantic properties, and language editions to enhance learning experience and outcomes. This study is useful because the ability of the MACHE-Bot to generate responses that are tailored to the user's emotions and cultural background can greatly enhance the learning experience and lead to better learning outcomes. Additionally, the use of a WGAN and the incorporation of external knowledge and cultural context makes the MACHE-Bot a unique and innovative system that has the potential to improve the overall effectiveness of AI-powered educational systems.