Impact of educational agents on student’s learning outcomes: a meta-analysis

TL;DR

This study uses a meta-analysis to show that educational agents positively impact student learning outcomes, especially in cognitive areas like creative thinking and academic performance.

Contribution

The study provides a comprehensive meta-analysis of educational agents' impact on learning outcomes, identifying specific cognitive and non-cognitive effects and moderating factors.

Findings

Educational agents significantly enhance cognitive abilities like creative thinking and academic performance.

Non-cognitive abilities such as learning motivation and attitude are also significantly improved.

The effects are strongest for chatbots, in universities, and in engineering technology disciplines.

Abstract

With the deep integration of artificial intelligence technology in the field of education, educational agents as an intelligent teaching tool possessing interactive and personalised characteristics have drawn increasing attention for their impact on learning outcomes.

This study employs a meta-analysis methodology to systematically synthesise 52 empirical investigations published in internationally authoritative journals between 2015 and 2025. It examines the overall effect of educational agents on student learning outcomes, their specific manifestations at cognitive and non-cognitive levels, and the influence of moderating variables such as types of agents, subjects, sample size, and academic level.

Findings indicate that educational agents exert a significant positive influence on student learning outcomes. Regarding cognitive abilities, they demonstrate moderate to substantial enhancement effects on creative thinking, academic performance, and communication skills, while their impact on spatial ability and problem-solving skills falls below statistical significance. Regarding non-cognitive abilities, learning motivation and learning attitude showed significant enhancement, whereas the effects on learning engagement and learning interest were smaller and non-significant. Moderation analyses indicated that the impact of educational agents was particularly pronounced among chatbots, universities, small-scale settings, and engineering technology disciplines.

This study reveals limitations in educational agents’ cultivation of complex abilities and personalised adaptation, providing empirical evidence for their precise application and optimised design.

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