Chatbot could be designed in several ways to enhance phenomenon-based learning.
Firstly, chatbot design with personalised guidance, tailored to the specific contents, resources, and prompts to the learner.
Another method could be structural learning either individually or group based. For individually, chatbots with prompts could promote critical thinking of the phenomenon or in group based prompts could support collaborative learning.
Overall, chatbots can support and encourage learning through inquiry and dialogue that prompt students to ask questions, reflect on findings, and connect concepts across disciplines while maintaining engagement and student-centred learning (different learning styles/approaches required by students).
Chatbots that are driven by advanced machine learning models like GPT-4 or Claude can make phenomenon based learning more personalized by responding in real time to each student's question. They can connect ideas from different fields using NLP and knowledge graphs, which helps people understand things in a more complete way. For instance, a bot driven by GPT-4 can pretend to have conversations with historical or scientific figures, which adds context and keeps people interested. ML methods such as BERT and T5 can help the bot figure out what students are saying and use that information to guide their inquiries.
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