AI-Driven Adaptive Assessment for Detecting Individual Learning Needs in Secondary Education

Abstract

The rapid growth of artificial intelligence (AI) has encouraged its application in education, particularly in adaptive assessment designed to meet diverse student needs at the secondary school level. Traditional assessments are often rigid and less responsive, creating challenges in early detection of learning difficulties. This study seeks to analyze the role of AI-based adaptive assessments in identifying students’ individual learning needs. A systematic literature review was conducted on fourteen peer-reviewed articles published between 2019 and 2023. The findings reveal that adaptive systems are able to adjust questions and materials in real time, provide accurate feedback, and support teachers with data-driven insights for targeted interventions. These systems also enhance personalization, student motivation, and efficiency in the learning process. Nevertheless, challenges remain, including algorithmic bias, data privacy concerns, and limited infrastructure, particularly in developing countries. The study concludes that successful implementation requires ethical safeguards, adequate resources, and strong policy support to ensure that AI-based adaptive assessments can create a more inclusive, equitable, and effective educational environment.