Hubei University of Technology Study Links External Support to Faculty AI-TPACK

A cross-sectional study published in Frontiers in Education on September 3, 2026 surveyed 593 university faculty members in China to examine how external support relates to self-reported AI-TPACK. The study used exploratory and confirmatory factor analyses and structural equation modeling, with 5,000 bootstrap resamples and 95% confidence intervals for indirect effects. External support was positively associated with self-reported AI-TPACK (β = .238), and perceived ease of use and perceived usefulness played important roles in the indirect associations.

The study offers a testable analytical framework for building university faculty AI teaching capacity: institutional training, hardware and software resources, evaluation and incentive mechanisms, and organizational climate may be statistically connected to self-reported AI-TPACK through faculty judgments about ease of use and usefulness. For teacher-training designers and university administrators, this suggests that providing tools or one-off training alone may not be sufficient, and faculty perceptions of whether tools are easy and useful deserve a place in support plans. The study uses cross-sectional self-report data, so it cannot establish causality or actual teaching-capacity gains, and reverse or reciprocal relationships remain plausible. University AI training programs could incorporate improving perceived ease of use and perceived usefulness as design elements, rather than focusing only on tool provision. The study uses self-report scales rather than performance assessments, so findings reflect faculty perceptions of AI-integrative competence only.