Computer-based simulations for learning offer affordances for advanced capabilities and expansive possibilities for knowledge construction and skills application. Virtual agents, when powered by artificial intelligence (AI), can be used to scaffold personalized and adaptive learning processes. However, a synthesis or a systematic evaluation of the learning effectiveness of AI-powered virtual agents in computer-based simulations for learning is still lacking. Therefore, this meta-analysis is aimed at evaluating the effects of AI-powered virtual agents in computer-based simulations for learning. The analysis of 49 effect sizes derived from 22 empirical studies suggested a medium positive overall effect, \(\overline{g }=0.43\), SE = 0.08, 95% C.I. [0.27, 0.59], favoring the use of AI-powered virtual agents over the non-AI-powered virtual agent condition in computer-based simulations for learning. Further, moderator analyses revealed that intervention length, AI technologies, and the representation of AI-powered virtual agents significantly explain the heterogeneity of the overall effects. Conversely, other moderators, including education level, domain, the role of AI-powered virtual agents, the modality of AI-powered virtual agents, and learning environment, appeared to be universally effective among the studies of AI-powered virtual agents in computer-based simulations for learning. Overall, this meta-analysis provides systematic and existing evidence supporting the adoption of AI-powered virtual agents in computer-based simulations for learning. The findings also inform about evidence-based design decisions on the moderators analyzed.

基于计算机的学习模拟为知识构建和技能应用提供了高级功能和广泛的可能性。虚拟代理在人工智能 (AI) 的支持下，可用于构建个性化和自适应学习过程。然而，仍然缺乏对基于计算机的学习模拟中人工智能驱动的虚拟代理的学习有效性的综合或系统评估。因此，这项荟萃分析旨在评估人工智能驱动的虚拟代理在基于计算机的学习模拟中的效果。对 22 项实证研究得出的 49 种效应大小的分析表明，总体效果中等，\(\overline{g }=0.43\)，SE = 0.08，95% CI [0.27, 0.59]，有利于使用人工智能驱动的在基于计算机的学习模拟中，虚拟代理优于非人工智能驱动的虚拟代理条件。此外，主持人分析显示，干预长度、人工智能技术和人工智能驱动的虚拟代理的表现显着解释了总体效果的异质性。相反，其他调节因素，包括教育水平、领域、人工智能驱动的虚拟代理的作用、人工智能驱动的虚拟代理的模式和学习环境，在计算机领域人工智能驱动的虚拟代理的研究中似乎普遍有效。基于模拟的学习。总体而言，这项荟萃分析提供了系统和现有的证据，支持在基于计算机的学习模拟中采用人工智能驱动的虚拟代理。研究结果还为所分析的调节器提供了基于证据的设计决策。