Pengembangan Sistem Evaluasi Adaptif Berbasis LLM dalam Smart Tutor Menggunakan Bloom’s Taxonomy - Nicholas Rudy; Hapnes Toba (Journal of Smart Technology and Engineering, 2026)
This research aims to improve adaptive learning systems by developing a Smart Tutor platform enhanced with Large Language Models (LLMs). Traditional systems that rely on keyword matching or cosine similarity often fail to accurately assess student responses due to their lack of semantic understanding. To address this, we propose a semantic-based evaluation system using BERTScore and TF-IDF to analyze student answers more contextually.