Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis
Liu Dong, Xiuxiu Tang, Xiyu Wang
Abstract
Recent developments in artificial intelligence (AI) have revolutionized the field of education as its presence and benefits enable new methods for teaching and learning. Therefore, many empirical studies have been conducted to evaluate the effectiveness of using AI on students' academic achievement. The purpose of this meta-analysis study was to examine the overall effect of AI on students' academic achievement in relation to a set of moderator variables including educational level, role of AI, intervention duration, sample size, learning strategy, subject area, and type of AI. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 29 empirical studies were selected from six databases (Scopus, Web of Science, APA PsycINFO, Education Full Text; Education Source, ERIC, and Social Sciences Full Text). These studies, comprising a sample of 2,657 participants across different educational levels, met the inclusion criteria before being coded, calculated, and analyzed. Overall, we found a significant positive effect of AI on students’ academic performance, with an effect size of 0.924. This study validates the efficacy of AI and provides a critical foundation for future research to identify and leverage specific conditions under which AI can most effectively enhance student learning.