Survey on AI Feedback in English Language Learning from 255 University Students

255 university-level English as a Foreign Language learners provided survey data on their engagement with AI-generated feedback. The data was analyzed using Partial Least Squares Structural Equation Modelling to examine relationships among trust, ethics, engagement, clarity, perceived usefulness, and future readiness. Author Amani A. Binjwair published the dataset under a CC-BY-4.0 license on figshare in April 2026.

Use Cases

• Modeling learner trust in AI-generated feedback based on survey constructs.
• Analyzing the impact of ethical concerns on engagement with AI tools in education.
• Investigating the relationship between feedback clarity and perceived usefulness in formative assessment.
• Studying how prior AI experience and academic maturity influence engagement patterns.

Strengths

• Data from 255 university-level EFL learners provides a defined sample size.
• Analysis uses a learner-centered conceptual model based on technology acceptance and feedback literacy frameworks.
• Published under a permissive CC-BY-4.0 license.

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 institutional bias inherent to the survey's collection.

Provenance

Source

Amani A. Binjwair via figshare.

Collection Method

Survey data collected from university-level EFL learners enrolled in higher education institutions.

Time Range

Survey collection date is not specified.

Freshness

Last updated 2026-04-16 05:16:27; freshness should be verified.

Geography

Geographic coverage of the surveyed institutions is not specified.