Artificial intelligence and its influence on student engagement in higher education
- 1Yangzhou University , ,
- 2Tongda College of Nanjing University of Posts and Telecommunications , ,
Abstract
Empirical evidence on how artificial intelligence (AI) relates to student engagement in culturally diverse higher-education settings remains limited. In Chinese university classrooms, where interaction is often teacher-led and asking questions in class can carry a social cost, AI-driven dialogic tools may offer low-pressure opportunities for active participation. This study examined the association between an AI conversational agent (EduBot) and the behavioural, cognitive, emotional and agentic dimensions of engagement among Chinese undergraduates. A mixed-methods quasi-experimental design involved 555 students in four comprehensive (multi-disciplinary, research-intensive) universities, each contributing one introductory course taught in two parallel sections. Within a 15-week semester, intervention sections (n = 278) used EduBot, a Socratic-dialogue agent embedded in the learning management system, for 12 weeks; control sections (n = 277) used teaching-assistant-moderated forums. We measured engagement at three time points with a 21-item four-dimensional engagement scale adapted from an established measure and supplemented it with system logs, 30 student interviews and instructor focus groups. Linear mixed-effects models accounting for the nesting of students within course sections, together with mixed-design ANOVAs, showed Group × Time interactions for behavioural (η²p = 0.043, post-test d = 0.71), cognitive (η²p = 0.032, d = 0.55), agentic (η²p = 0.054, d = 0.57) and emotional engagement (η²p = 0.012, d = 0.34), all p ≤ 0.001 after Holm adjustment. Within the intervention group, the number of EduBot sessions predicted post-test engagement after adjustment for baseline scores and covariates. Exploratory moderation analyses indicated larger agentic gains among first-year students and larger cognitive gains among students with lower prior GPA. Thematic analysis identified three hypothesised mechanisms, namely a judgment-free interaction space, scaffolded persistence and temporal flexibility. A Socratic-scaffolded AI agent was associated with higher engagement, most clearly for agentic engagement, which participants described as constrained by classroom norms. The findings support culturally attentive AI design and suggest, tentatively, an equity-enhancing potential. Because the design compared an integrated AI package with usual practice across eight sections, the effects cannot be attributed to single design components.
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BibTeX
@article{chen2026artificial,
title = {{Artificial intelligence and its influence on student engagement in higher education}},
author = {Yongsheng Chen and Jing Dong and Guojuan Ji},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1949332},
url = {https://europepmc.org/article/PMC/PMC13716850},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence and its influence on student engagement in higher education
AU - Yongsheng Chen
AU - Jing Dong
AU - Guojuan Ji
PY - 2026
DA - 2026/10/15
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1949332
UR - https://europepmc.org/article/PMC/PMC13716850
AB - Empirical evidence on how artificial intelligence (AI) relates to student engagement in culturally diverse higher-education settings remains limited. In Chinese university classrooms, where interaction is often teacher-led and asking questions in class can carry a social cost, AI-driven dialogic tools may offer low-pressure opportunities for active participation. This study examined the association between an AI conversational agent (EduBot) and the behavioural, cognitive, emotional and agentic dimensions of engagement among Chinese undergraduates. A mixed-methods quasi-experimental design involved 555 students in four comprehensive (multi-disciplinary, research-intensive) universities, each contributing one introductory course taught in two parallel sections. Within a 15-week semester, intervention sections (n = 278) used EduBot, a Socratic-dialogue agent embedded in the learning management system, for 12 weeks; control sections (n = 277) used teaching-assistant-moderated forums. We measured engagement at three time points with a 21-item four-dimensional engagement scale adapted from an established measure and supplemented it with system logs, 30 student interviews and instructor focus groups. Linear mixed-effects models accounting for the nesting of students within course sections, together with mixed-design ANOVAs, showed Group × Time interactions for behavioural (η²p = 0.043, post-test d = 0.71), cognitive (η²p = 0.032, d = 0.55), agentic (η²p = 0.054, d = 0.57) and emotional engagement (η²p = 0.012, d = 0.34), all p ≤ 0.001 after Holm adjustment. Within the intervention group, the number of EduBot sessions predicted post-test engagement after adjustment for baseline scores and covariates. Exploratory moderation analyses indicated larger agentic gains among first-year students and larger cognitive gains among students with lower prior GPA. Thematic analysis identified three hypothesised mechanisms, namely a judgment-free interaction space, scaffolded persistence and temporal flexibility. A Socratic-scaffolded AI agent was associated with higher engagement, most clearly for agentic engagement, which participants described as constrained by classroom norms. The findings support culturally attentive AI design and suggest, tentatively, an equity-enhancing potential. Because the design compared an integrated AI package with usual practice across eight sections, the effects cannot be attributed to single design components.
SN - 1664-1078
ER -