Table 1_Determinants of university students' intention to use generative AI tools for pers

386 valid questionnaires collected from university students via convenience sampling. The dataset analyzes determinants of intention to use generative AI tools for personalized English learning, employing an extended UTAUT2 model. Structural equation modeling was performed using SmartPLS 4.0 software.

Use Cases

• Modeling technology acceptance factors based on performance expectancy, effort expectancy, hedonic motivation, and habit.
• Analyzing mediating effects of flow experience on behavioral intention.
• Examining moderating effects of personal innovativeness on the relationship between hedonic motivation and intention.
• Testing structural equation models for educational technology adoption.

Strengths

• Dataset includes 386 valid survey responses.
• Analysis employs a validated theoretical framework (UTAUT2) extended for generative AI context.
• Results provide practical implications for students, instructors, and tool designers.

Limitations

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

Provenance

Source

Li Wang

Collection Method

Survey conducted among university students using a convenience sampling method.

Freshness

Last updated 2026-05-05 05:29:01; freshness should be verified.