With the increasing integration of artificial intelligence (AI) technologies into educational settings, it becomes important to ensure responsible and effective use of these systems. This thesis addresses two critical challenges within AI-driven educational applications: the effortless integration of different Large Language Models (LLMs) and the mitigation of inappropriate content. An AI assistant chatbot was developed, allowing teachers to design custom chatbots and set rules for them, enhancing students’ learning experiences. Evaluation of LangChain as a framework for LLM integration, alongside various prompt engineering techniques including zero-shot, few-shot, zero-shot chain-of-thought, and prompt chaining, revealed LangChain’s suitability for this task and highlighted prompt chaining as the most effective method for mitigating inappropriate content in this use case. Looking ahead, future research could focus on further exploring prompt engineering capabilities and strategies to ensure uniform learning outcomes for all students, as well as leveraging LangChain to enhance the adaptability and accessibility of educational applications.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-105912 |
| Date | January 2024 |
| Creators | Eriksson, Hanna |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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