AITAQS four-dimensional framework of AI tutoring acceptance quality

Product or Programme · All products and programmes
Evidence · 5 claims (5 for) · 1 study (1 design), q3 · 0 of 1 report an effect size · 5 claims rest on one study

Description

A learner-perceived self-report assessment framework for higher-education students that measures AI tutoring acceptance quality across feedback quality, instructional effectiveness, interaction experience, and user trust.

Components

Claims

• Bifactor-ESEM shows the highest fit among CFA, ESEM, bifactor-CFA and bifactor-ESEM models, supporting AITAQS as one general factor plus four specific dimensions. [+M]
• Principal component analysis of the AITAQS extracts four factors accounting for 65.421% of variance, with item loadings from 0.64 to 0.85 matching the four hypothesized dimensions. [+M]
• AITAQS dimension reliabilities fall within 0.721–0.745 Cronbach's α, exceeding acceptability thresholds, with all CR values above 0.7 though some AVE values fall below 0.5. [+W]
• All AITAQS items load above 0.50 on their target factor with cross-loadings below 0.30, in the hypothesized assignment (X1–X3 Feedback Quality through X10–X12 User Trust). [+W]
• The AITAQS item pool shows sampling adequacy for factor analysis, with KMO = 0.758 and a significant Bartlett test (χ² = 438.75, df = 66, p < 0.001). [+W]

Related Products and Programmes

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Key Sources

• Zhang, Z., Katayoun, M., Tan, L., & Wei, G. (2026). Development and Validation of a Multidimensional Scale for AI Tutoring Acceptance in Higher Education. npj Science of Learning. https://doi.org/10.1038/s41539-026-00431-9

Page metadata

Field	Value
type	product
id	aitaqs-four-dimensional-framework-of-ai-tutoring-acceptance-quality
title	AITAQS four-dimensional framework of AI tutoring acceptance quality
description	A learner-perceived self-report assessment framework for higher-education students that measures AI tutoring acceptance quality across feedback quality, instructional effectiveness, interaction experience, and user trust.
status	review
product_kind	assessment
generated	by process:settle-candidates on 2026-10-26
trust	unverified — no one has confirmed this page's content

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