When the scaffold is withdrawn: Motivational trajectories and differences in AI-supported inquiry learning

Sustaining motivation is central to science education. Whether AI-supported inquiry produces durable motivational gains remains unclear, as few studies examine what happens after scaffolding ends. This longitudinal mixed-methods study ( N = 131) compared traditional inquiry, technology-supported inquiry, and AI-augmented inquiry (ATSI) across intervention and withdrawal phases, assessing four motivation dimensions at four time points. Open-ended attributions and contrastive case analysis provided explanatory depth. Both technology-supported conditions outperformed traditional inquiry in intrinsic motivation during intervention, with ATSI producing the largest growth but also the sharpest post-withdrawal decline. Self-determination declined most in ATSI after scaffolding removal, while self-efficacy and grade motivation did not differ significantly across conditions. ATSI students continued to attribute their motivational changes to technological support even after withdrawal. Case analyses suggested that motivational sustainability depended on how students positioned AI-augmented support within their own practices. These findings indicate that AI-supported motivational gains are dimension-specific and withdrawal-vulnerable, suggesting that durable benefits require scaffolding designs that gradually shift regulatory ownership from tool to learner. Educational relevance statement Although artificial intelligence (AI) shows promise for science education, its impact on student motivation after technology removal remains underexplored. Our findings reveal that while AI-supported inquiry boosts motivation during use, dimension-specific declines follow once students return to traditional classrooms, accompanied by students attributing their motivation to technological support. To counteract this dependency, pedagogical designers should guide learners to recognize AI as a collaborative partner while progressively assuming ownership of the regulatory functions the scaffold provides. By building this transfer into scaffold design, educators can support the persistence of self-directed motivation after the scaffold is withdrawn.

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Publication Details

Journal
Learning and Individual Differences
Published
2026-09-14
DOI
https://doi.org/10.1016/j.lindif.2026.102994
Primary Topic
Science Education and Pedagogy
Type
article
Field-Weighted Citation Impact
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article

When the scaffold is withdrawn: Motivational trajectories and differences in AI-supported inquiry learning

Gaowei Chen, Wei Jia, Fan Chen, Bo Xiong
Learning and Individual Differences
Science Education and Pedagogy
article

When the scaffold is withdrawn: Motivational trajectories and differences in AI-supported inquiry learning

Gaowei Chen, Wei Jia, Fan Chen, Bo Xiong
article en

Abstract

Sustaining motivation is central to science education. Whether AI-supported inquiry produces durable motivational gains remains unclear, as few studies examine what happens after scaffolding ends. This longitudinal mixed-methods study ( N = 131) compared traditional inquiry, technology-supported inquiry, and AI-augmented inquiry (ATSI) across intervention and withdrawal phases, assessing four motivation dimensions at four time points. Open-ended attributions and contrastive case analysis provided explanatory depth. Both technology-supported conditions outperformed traditional inquiry in intrinsic motivation during intervention, with ATSI producing the largest growth but also the sharpest post-withdrawal decline. Self-determination declined most in ATSI after scaffolding removal, while self-efficacy and grade motivation did not differ significantly across conditions. ATSI students continued to attribute their motivational changes to technological support even after withdrawal. Case analyses suggested that motivational sustainability depended on how students positioned AI-augmented support within their own practices. These findings indicate that AI-supported motivational gains are dimension-specific and withdrawal-vulnerable, suggesting that durable benefits require scaffolding designs that gradually shift regulatory ownership from tool to learner. Educational relevance statement Although artificial intelligence (AI) shows promise for science education, its impact on student motivation after technology removal remains underexplored. Our findings reveal that while AI-supported inquiry boosts motivation during use, dimension-specific declines follow once students return to traditional classrooms, accompanied by students attributing their motivation to technological support. To counteract this dependency, pedagogical designers should guide learners to recognize AI as a collaborative partner while progressively assuming ownership of the regulatory functions the scaffold provides. By building this transfer into scaffold design, educators can support the persistence of self-directed motivation after the scaffold is withdrawn.

Learning and Individual DifferencesVol. 132
Chongqing University of Education (CN), University of Hong Kong (HK)
Quality Education
Openalex Percentile: Top 2%
Science Education and Pedagogy
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