Blooms Analyzer - Project for Pavatharini
Title: Automated Bloom’s Taxonomy Analyzer for University Question Paper Design using LLaMA 3.2 Abstract: In outcome-based education (OBE), it is essential that university question papers are aligned with cognitive learning objectives to ensure holistic student development. Bloom’s Taxonomy, with its six hierarchical levels (K1–K6), provides a widely accepted framework for classifying educational objectives and assessing cognitive rigor. However, manual classification of exam questions into Bloom levels is subjective, time-consuming, and prone to inconsistencies. This research presents an AI-powered Bloom’s Taxonomy Analyzer that leverages the Ollama LLaMA 3.2 large language model to automatically classify university examination questions according to the cognitive levels defined in Bloom’s framework. The proposed system uses prompt engineering techniques to guide the LLaMA 3.2 model in identifying the intent, action verbs, and cognitive complexity of each question. The analyzer accept...