Applying AI-Based Adaptive Assessment to Map and Enhance Science Literacy Skills in Generation Z: A Systematic Literature Review

Science literacy is a critical 21st-century competency, yet student achievement in this area presents significant challenges globally. This gap is exacerbated by conventional assessment methods that are misaligned with the learning characteristics of the digital-native Generation Z, who expect personalized, interactive, and instantaneous feedback. This study aims to analyze and synthesize the current research landscape on the application of AI-based adaptive assessment to map and enhance the science literacy skills of Generation Z. Employing a Systematic Literature Review (SLR) guided by the PRISMA framework, this study identifies existing theoretical models, platforms, evidence of effectiveness, and implementation challenges. The findings indicate that AI-based adaptive assessment platforms, such as Inq-ITS and ALEKS, effectively measure various science sub-skills and improve learning outcomes. Key features including personalization, interactivity, and immediate feedback closely align with the preferences of Generation Z, thereby enhancing student motivation and engagement. Nevertheless, implementation faces significant challenges related to infrastructure, teachers' pedagogical readiness, and crucial ethical considerations, including data privacy and algorithmic bias. This study concludes that AI-adaptive assessment holds transformative potential, yet its effective and equitable adoption requires addressing existing challenges and future research gaps.