Artificial Intelligence as a Science Teacher Assistant: An Analysis of Machine Learning Utilization in Diagnosing Student Misconceptions: A Review

Abstract

Diagnosing these misconceptions in a crowded classroom context is very difficult, time-consuming, and subjective when using conventional methods, which often leads to ineffective teaching interventions. To address the urgent need for accurate and objective diagnosis, this article proposes and analyzes the role of Artificial Intelligence (AI), specifically Machine Learning (ML) technologies such as natural language processing (NLP). ML models can analyze student response data (essays) quickly and consistently, acting as science teacher assistants to strengthen diagnostic capabilities. This study uses a systematic literature review method to analyze and synthesize existing research findings regarding Artificial Intelligence as a Science Teacher's Assistant: An Analysis of the Utilization of Machine Learning in Diagnosing Student Misconceptions. This research aims to analyze and explain Artificial Intelligence as a Science Teacher's Assistant: An Analysis of the Utilization of Machine Learning in Diagnosing Student Misconceptions. The brief objectives of this study are as follows: to analyze the utilization of Machine Learning (ML) models in objectively diagnosing, categorizing, and predicting students' misconceptions in science. The findings of this review study indicate that student misconceptions are a persistent barrier to learning, and conventional (manual, paper-based) diagnostic methods have proven inefficient and subjective for crowded classrooms. This validates the urgent need for technological solutions.