University Students' Intention to Use Generative AI Tools for English Learning

386 survey responses from university students analyzed via structural equation modeling to understand technology acceptance. The research, authored by Li Wang, extends the UTAUT2 model to examine determinants like performance expectancy and hedonic motivation. Results indicate flow experience mediates and personal innovativeness moderates the intention to use AI for personalized English learning.

Use Cases

• Modeling technology acceptance factors based on performance expectancy and effort expectancy constructs.
• Investigating the mediating role of flow experience in educational technology engagement.
• Analyzing how personal innovativeness moderates the link between hedonic motivation and behavioral intention.
• Informing the design of user-friendly generative AI tools for language learning based on student survey data.

Strengths

• Analysis is based on 386 valid survey questionnaires.
• Employs structural equation modeling with SmartPLS 4.0 software for robust statistical analysis.
• Extends the established UTAUT2 theoretical model with mediating and moderating variables.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the convenience sampling method used for the survey.

Provenance

Source

Li Wang

Collection Method

Survey conducted among university students using a convenience sampling method.

Freshness

Last updated 2026-05-05 05:29:02; freshness should be verified.