New Curriculum, New Chance -- Retrieval Augmented Generation for Lesson Planning in Ugandan Secondary Schools. Prototype Quality Evaluation
Introduction: Poor educational quality in Secondary Schools is still regarded as one of the major struggles in 21st century Uganda - especially in rural areas. Research identifies several problems, including low quality or absent teacher lesson planning. As the government pushes towards the implementation of a new curriculum, exiting lesson plans become obsolete and the problem is worsened. Using a Retrieval Augmented Generation approach, we developed a prototype that generates customized lesson plans based on the government-accredited textbooks. This helps teachers create lesson plans more efficiently and with better quality, ensuring they are fully aligned the new curriculum and the competence-based learning approach. Methods: The prototype was created using Cohere LLM and Sentence Embeddings, and LangChain Framework - and thereafter made available on a public website. Vector stores were trained for three new curriculum textbooks (ICT, Mathematics, History), all at Secondary 1 Level. Twenty-four lessons plans were generated following a pseudo-random generation protocol, based on the suggested periods in the textbooks. The lesson plans were analyzed regarding their technical quality by three independent raters following the Lesson Plan Analysis Protocol (LPAP) by Ndihokubwayo et al. (2022) that is specifically designed for East Africa and competence-based curriculums. Results: Evaluation of 24 lesson plans using the LPAP resulted in an average quality of between 75 and 80%, corresponding to "very good lesson plan". None of the lesson plans scored below 65%, although one lesson plan could be argued to have been missing the topic. In conclusion, the quality of the generated lesson plans is at least comparable, if not better, than those created by humans, as demonstrated in a study in Rwanda, whereby no lesson plan even reached the benchmark of 50%.
Code (0)
등록된 구현이 없습니다.
Tasks
Retrieval-augmented GenerationSentence EmbeddingsSimilar Papers 제목 키워드 기반
Automatic Large Language Models Creation of Interactive Learning Lessons
We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engineering through a Retrieval-Augmented Gene…
Prompt EngineeringRetrieval-augmented GenerationLessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
The increasing popularity of outdoor recreational activities (such as hiking and biking) has boosted the demand for a conversational AI system to provide informative and personalized suggestion on outdoor trails. Challen…
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support
Clients wishing to implement generative AI in the domain of IT Support and AIOps face two critical issues: domain coverage and model size constraints due to model choice limitations. Clients might choose to not use large…
Answer GenerationLanguage ModelingLanguage ModellingLarge Language Model+3Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction
Incorporating specific knowledge into large language models via retrieval-augmented generation (RAG) is a widespread technique that fuels many of today's industry AI applications. A fundamental problem is to assess if th…
CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning
Retrieval-Augmented Generation (RAG) is an effective method to enhance the capabilities of large language models (LLMs). Existing methods focus on optimizing the retriever or generator in the RAG system by directly utili…
RAGRetrievalRetrieval-augmented Generation