Exploring the Impact of AI-Based Adaptive Learning Assessment on Academic Well-Being in Vocational Education: A Mini Review Dwi Fitria Al Husaeni, Amay Suherman*, Budi Mulyanti*, Ade Gafar Abdullah, Lala Septem Riza, Eki Nugraha
Universitas Pendidikan Indonesia
Abstract
In the digital era, vocational education faces challenges in ensuring accurate, fair, and adaptive assessment for learners. AI-Based Adaptive Learning Assessment (AI-ALA) has emerged as an innovative solution to improve learning effectiveness through assessment personalization, real-time feedback, and adjustment of material difficulty levels based on individual abilities. This study aims to explore the impact of AI-ALA on learners^ academic well-being, including psychological, motivational, and social aspects in a vocational learning environment. The method used in this study is a mini review by reviewing 26 English-language journal articles from the Scopus database that are relevant to this topic. The results of the study indicate that AI-ALA contributes to increased learning motivation, reduced academic anxiety, and increased trust in a more transparent assessment system. However, the implementation of AI-ALA still faces challenges in infrastructure readiness, educator skills, and potential bias in assessment algorithms. Therefore, an optimization strategy is needed through increased educator training, strengthening technological infrastructure, and clearer regulations regarding the use of AI in academic assessment. The implications of this study indicate that the appropriate implementation of AI-ALA can not only improve learning effectiveness but also support students^ academic well-being holistically in more inclusive and sustainable vocational education.
