AI evidence extraction

Students’ behavioral intentions toward generative AI in education: Task-technology fit and moral obligations

Yann‐Jy Yang, Wang Chih-Chien, Yi-Hsuan Chen · Journal of Education and e-Learning Research · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.44

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.20448/jeelr.v12i4.7862

Methodology & findings

Study design

Cross-sectional online survey with Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis

Sample

N = 136, 1 group

Primary method

Partial Least Squares Structural Equation Modeling (PLS-SEM)

Main result

The study found that "perceived technology characteristics and self-efficacy significantly enhance task-technology fit, positively affecting behavioral intention" while "moral obligation shaped by perceived teacher attitudes negatively influences students' intention to use AI tools for coursework." The research demonstrates that "The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypotheses and explains a substantial proportion of the variance in behavioral intention."

Reports effect sizes.

Research paradigm

Positivist/Quantitative

Author conclusions

The authors conclude that "These findings provide theoretical insights into how technological and ethical considerations jointly influence AI adoption in education" and that "The study also offers practical suggestions for educators and institutions aiming to guide the responsible use of generative AI in learning environments. This study contributes a novel framework for understanding responsible AI use in higher education."

Risk of bias

Self-report survey methodology (social desirability bias); Single-country sample (Taiwanese students, limited generalizability); Cross-sectional design (cannot establish causality); Convenience sampling approach (selection bias); Selection bias: Sample limited to 136 Taiwanese college students, not representative of broader student populations; Geographic limitation: Single country context may limit generalizability; Self-report bias: Data collected through online survey, subject to response bias; Potential confounders: Cultural differences not controlled for; institutional policies on AI use not mentioned; Selection bias: Sample limited to Taiwanese college students only; Cross-sectional design: Cannot establish causality; Self-reported data: Subject to social desirability bias; Single country sample: Limited generalizability

Open questions raised

Future research directions are not explicitly stated in the abstract. The authors position their framework as novel, suggesting gaps in existing literature regarding the integration of task-technology fit and moral obligation perspectives in understanding AI adoption in education.
The abstract does not explicitly state specific gaps or future research directions identified by the authors.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 74%

Explore related topics

Responsible AI Use AI Governance Survey