The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics

Visual learning analytics (VLA) is becoming increasingly adopted in educational technologies and learning analytics dashboards to convey critical insights to students and educators. Yet many students experienced difficulties in comprehending complex VLA due to their limited data visualisation literacy. While conventional scaffolding approaches like data storytelling have shown effectiveness in enhancing students’ comprehension of VLA, these approaches remain difficult to scale and adapt to individual learning needs. Generative AI (GenAI) technologies, especially conversational agents, offer potential solutions by providing personalised and dynamic support to enhance students’ comprehension of VLA. This controlled lab study investigates the effectiveness of GenAI agents, particularly when integrated with scaffolding techniques, in improving students’ comprehension of VLA. A randomised controlled trial was conducted with 117 higher education students to compare the effects of two types of GenAI agents: passive agents , which respond to student queries, and proactive agents , which utilise scaffolding questions, against standalone scaffolding in a VLA comprehension task. The results show that passive agents yield comparable improvements to standalone scaffolding both during and after the intervention. Notably, proactive GenAI agents significantly enhance students’ VLA comprehension compared to both passive agents and standalone scaffolding, with these benefits persisting beyond the intervention. These findings suggest that integrating GenAI agents with scaffolding can have lasting positive effects on students’ comprehension skills and support genuine learning. • Low visualisation literacy limits understanding of visual learning analytics (VLA). • Investigated the effects of GenAI agents with scaffolding on VLA comprehension. • Conducted RCT comparing proactive, passive AI agents, and standalone scaffolding. • Proactive GenAI agents significantly improved VLA comprehension with lasting effects. • GenAI agents offer sustainable improvements in students’ comprehension skills.

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Publication Details

Journal
Computers & Education
Published
2025-04-26
DOI
https://doi.org/10.1016/j.compedu.2025.105322
Citations
25
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
42.34

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article

The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics

Linxuan Zhao, Lixiang Yan, Roberto Martínez‐Maldonado, Riordan Alfredo et al.
25 citations
Computers & Education
Online Learning and Analytics
42.34
article

The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics

Linxuan Zhao, Lixiang Yan, Roberto Martínez‐Maldonado, Riordan Alfredo, Vanessa Echeverría, Yueqiao Jin, Mikaela Elizabeth Milesi, Xinyu Li, Jie Xiang Fan, Dragan Gašević
article en
25 citations

Abstract

Visual learning analytics (VLA) is becoming increasingly adopted in educational technologies and learning analytics dashboards to convey critical insights to students and educators. Yet many students experienced difficulties in comprehending complex VLA due to their limited data visualisation literacy. While conventional scaffolding approaches like data storytelling have shown effectiveness in enhancing students’ comprehension of VLA, these approaches remain difficult to scale and adapt to individual learning needs. Generative AI (GenAI) technologies, especially conversational agents, offer potential solutions by providing personalised and dynamic support to enhance students’ comprehension of VLA. This controlled lab study investigates the effectiveness of GenAI agents, particularly when integrated with scaffolding techniques, in improving students’ comprehension of VLA. A randomised controlled trial was conducted with 117 higher education students to compare the effects of two types of GenAI agents: passive agents , which respond to student queries, and proactive agents , which utilise scaffolding questions, against standalone scaffolding in a VLA comprehension task. The results show that passive agents yield comparable improvements to standalone scaffolding both during and after the intervention. Notably, proactive GenAI agents significantly enhance students’ VLA comprehension compared to both passive agents and standalone scaffolding, with these benefits persisting beyond the intervention. These findings suggest that integrating GenAI agents with scaffolding can have lasting positive effects on students’ comprehension skills and support genuine learning. • Low visualisation literacy limits understanding of visual learning analytics (VLA). • Investigated the effects of GenAI agents with scaffolding on VLA comprehension. • Conducted RCT comparing proactive, passive AI agents, and standalone scaffolding. • Proactive GenAI agents significantly improved VLA comprehension with lasting effects. • GenAI agents offer sustainable improvements in students’ comprehension skills.

Computers & EducationVol. 234
Escuela Superior Politecnica del Litoral (EC), Monash University (AU)
Australian Government, Defense Advanced Research Projects Agency, Advanced Research Projects Agency, Australian Research Council
Quality Education
Openalex Percentile: Top 0%
Online Learning and Analytics
42.34
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