Table 4 R² and construct reliability and validity.

	AISE	CT	ESC	GUC
R²	Moderate explanatory power	Weak explanatory power	Moderate explanatory power
Q²	High predictive relevance	Medium predictive relevance	High predictive relevance	Medium predictive relevance
SRMR	Accepted
NFI	Good fit
GOF	High fit

The coefficient of determination was R²; construct reliability and validity included Cronbach’s alpha, composite reliability, and AVE; and the measure of predictive relevance was Q2. R² ≥ 0.75: Strong explanatory power; 0.50 ≤ R² < 0.75: Moderate explanatory power; 0.25 ≤ R² < 0.50: Weak explanatory power; R² < 0.25: Very weak explanatory power. Q² ≥ 0: Minimum threshold (indicates predictive relevance); Q² > 0.35: High predictive relevance; 0.15 < Q² ≤ 0.35: Medium predictive relevance; Q² ≤ 0.15: Low predictive relevance. GOF ≥ 0.10: Poor fit; GOF ≥ 0.25: Medium fit; GOF ≥ 0.36: High fit. AISE = Artificial Intelligence Self-Efficacy; CT = Critical Thinking; ESC = Engineering Students’ Creativity; GUC = Generative Artificial Intelligence Usage Competence. SRMR = Standardized Root Mean Square Residual. A commonly accepted cut-off value is SRMR < 0.08. NFI = Normed Fit Index. NFI value > 0.90 indicates a Good Fit.

Quick links

Search