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Related Experiment Video

Updated: Jul 9, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Trust in AI: scale development and validation for online distance learners.

Ayşin Gaye Üstün¹, Mehmet Yavuz², Bünyami Kayalı³

¹Department of Computer Technology, Sinop University, Sinop, 57000, Türkiye. aysingaye.ustun@gmail.com.

|May 7, 2026

Summary

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Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

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A new scale measures student trust in artificial intelligence (AI) within online education. This validated 21-item instrument offers insights into how students perceive and interact with AI learning tools.

Area of Science:

Educational Technology
Artificial Intelligence in Education
Psychometrics

Background:

Student trust in artificial intelligence (AI) is crucial for AI-supported learning environments.
Existing instruments for measuring student trust in AI in education are limited.
A validated scale is needed to assess trust in AI within online distance education.

Purpose of the Study:

To develop and validate a multidimensional scale for measuring student trust in AI systems in online distance education.
To provide a reliable instrument for research on student-AI interactions in higher education.

Main Methods:

Sequential multi-stage scale development: literature review, expert evaluation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA).
Data collected from 837 distance learning students.

Keywords:

Artificial intelligence Higher education Online distance education Scale development Technology acceptance Trust

Related Experiment Videos

Last Updated: Jul 9, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Psychometric properties assessed, including internal consistency, model fit, and measurement invariance.

Main Results:

A 21-item, five-factor scale was developed, explaining 63.20% of the variance.
CFA confirmed good model fit (χ²/df = 2.17, CFI = .95, TLI = .94, RMSEA = .06).
The scale demonstrated high internal consistency (α = .94, ω = .94) and metric invariance across gender.

Conclusions:

The developed scale is a reliable instrument for assessing student trust in AI in online learning.
Trust in AI is a distinct perception influencing student engagement with AI learning tools.
Findings have practical implications for designing and implementing AI-supported learning in higher education.