Claims

Q methodology reveals three distinct instructional-designer perception factors toward ChatGPT integration: Pessimistic Evaluators, Optimistic Advocates, and Wary Thinkers

Claim · All claims
Evidence · 1 study · qualitative r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? Q-factor analysis of 19 instructional designers' Q sorts yielded three distinct perception factors toward ChatGPT integration. → Fan Yang · Jill E. Stefaniak 2025 q2 i? Factor 1 (Pessimistic Evaluators) accounted for the highest explained variance at 37%, with 10 participants loading significantly. → Fan Yang · Jill E. Stefaniak 2025 q2 i? Factors 2 (Optimistic Advocates) and 3 (Wary Thinkers) each explained 12% of variance, with three and four participants loading significantly. → Fan Yang · Jill E. Stefaniak 2025

Evidence

Fan Yang · Jill E. Stefaniak 2025

Fan Yang · Jill E. Stefaniak. (2025). An exploration of instructional designers' prioritizations for integrating ChatGPT in design practice. Education Tech Research Dev. https://doi.org/10.1007/s11423-025-10509-7

q2 · i? · qualitative · r2

Q-methodology study in which 19 practicing instructional designers sorted 25 statements about integrating ChatGPT into design practice; principal component analysis with varimax rotation produced a three-factor solution. The article reports that the "three-factor solution, which explained 61% of the variance in the model, was generated from the Q-factor analysis."

"A three-factor solution, which explained 61% of the variance in the model, was generated from the Q-factor analysis."

Discussion

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