The advancement of VR technology through the increase in its processing power and decrease in its cost and form factor induced the research and market interest away from the gaming industry and towards education and training. In this paper, we argue and present evidence from vast research that VR is an excellent tool in engineering education. Through our review, we deduced that VR has positive cognitive and pedagogical benefits in engineering education, which ultimately improves the students’ understanding of the subjects, performance and grades, and education experience. In addition, the benefits extend to the university/institution in terms of reduced liability, infrastructure, and cost through the use of VR as a replacement to physical laboratories. There are added benefits of equal educational experience for the students with special needs as well as distance learning students who have no access to physical labs. Furthermore, recent reviews identified that VR applications for education currently lack learning theories and objectives integration in their design. Hence, we have selected the constructivist and variation learning theories as they are currently successfully implemented in engineering education, and strong evidence shows suitability of implementation in VR for education.

This study investigates date palm fiber (DPF) and sheep wool hybrid polyester composites with fiber loadings of 0%, 10%, 20%, and 30% by weight, fabricated by compression molding, to develop a sustainable and reliable material system. Experimental data from prior work were modeled using Weibull analysis for reliability evaluation and response surface methodology (RSM) for multi-objective optimization. Weibull statistics fitted a two-parameter distribution to tensile strength and fracture toughness, extracting shape (η) and scale ($\beta$) parameters to quantify variability and failure probability. The analysis showed that 20% hybrid content achieved the highest scale values ($\beta$ = 28.85 MPa for tensile strength and $\beta$ = 15.03 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ for fracture toughness) and comparatively low scatter (η = 10.39 and 9.2, respectively), indicating superior reliability. RSM quadratic models were developed for tensile strength, fracture toughness, thermal conductivity, acoustic attenuation, and water absorption, and were combined using desirability functions. The RSM optimization was found at 18.97% fiber content with a desirability index of 0.673, predicting 25.89 MPa tensile strength, 14.23 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ fracture toughness, 0.08 W/m·K thermal conductivity, 20.49 dB acoustic attenuation, and 5.11% water absorption. Overlaying Weibull cumulative distribution functions with RSM desirability surfaces linked probabilistic reliability zones (90–95% survival) to the deterministic optimization peak. This integration establishes a unified framework for designing natural fiber composites by embedding reliability into multi-property optimization.