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Reconstructing Teacher-Student Relationship in AI-Based Assessment Systems: Authority Transfer or Collaborative Evolution?
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DOI：10.3837/tiis.2026.04.025.png)
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Abstract
En 中文
This study empirically examined the impact of AI-based assessment systems on teacher-student relationship using data from 462 university students. Drawing on prior research and theoretical foundations, three latent variables were measured: AI system use, perceived teacher authority, and perceived teacher-student collaboration. The results of structural equation modeling revealed that the use of AI assessment systems had a significant negative effect on students' perceptions of teacher authority (beta =-0.222, p < .001), suggesting that AI weakens teachers' traditional evaluative authority. Conversely, system use exerted a significant positive effect on students' perceptions of teacher-student collaboration (beta= 0.431, p < .001), indicating that AI fosters more collaborative interactions. Notably, the relationship between perceived teacher authority and collaboration was found to be nonsignificant (beta = 0.049, p = .371). These findings demonstrate that AI technologies simultaneously diminish teacher authority while enhancing collaborative interactions, suggesting that "authority transfer" and "collaborative evolution" are not mutually exclusive but coexisting mechanisms. This study contributes to a deeper understanding of how educational relationships are being fundamentally restructured in the digital era and provides important implications for the design and implementation of future AI-based educational systems.
Keywords:
AI-based assessment system
Teacher-student relationship
Authority transfer
Collaborative evolution
Empirical study
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