What question did this study set out to answer?

The central aim is to evaluate the effectiveness of an integrated WEFE engineering education framework that employs optimization and emerging technologies.

March 23, 2026Open Access

Emerging WEFE Technologies in Engineering Education: Integration of Optimization, Explainable AI, Digital Twins, and Virtual Reality

Key Points

The central aim is to evaluate the effectiveness of an integrated WEFE engineering education framework that employs optimization and emerging technologies.
Developed a framework combining risk-aware optimization, explainable AI, digital twins, and virtual reality.
Generated a synthetic WEFE scenario library with 10,200 operating conditions and 20,000 intervention policies.
Conducted benchmark experiments to assess the optimization core's impact on resource deficits and tail risk.
Optimization reduces water and food deficits by 25–45% and decreases WEFE loss tail risk by 15–30%.
Surrogate policies exhibit 86–92% fidelity to the optimizer, with performance gaps below 5%.
Real-time VR interactions achieve frame rates above 60 fps for over 500 concurrent evaluations per session.

Abstract

This study presents an integrated Water-Energy-Food-Ecosystem (WEFE) engineering education framework that combines risk-aware optimization, machine learning surrogates, explainable artificial intelligence (XAI), digital twin, and virtual reality (VR). A synthetic WEFE scenario library of 10,200 operating conditions and 20,000 intervention policies was generated to support solver-based labeling, XAI training, and immersive VR labs without using real learner data. Benchmark experiments show that the optimization core reduces aggregate water and food deficits by 25–45% while lowering WEFE loss tail risk (CVaR) by 15–30% across baseline, stress, and extreme regimes. Surrogate policies achieve 86–92% fidelity to the optimizer, with objective-performance gaps below 5%, enabling real-time VR interaction at frame rates above 60 fps for more than 500 concurrent “what-if” evaluations per session. The framework is instantiated across 6 program streams at the Hijjawi Faculty for Engineering Technology, with 12–16 contact hours per course allocated to WEFE-XR labs aligned with ABET-related outcomes in systems design, sustainability, data-driven decision making, and ethical responsibility. Analytics from more than 1,000 synthetic scenario playbacks indicate consistent selection of risk-aware strategies, with simulated policy trajectories showing, on average, 18–25% lower tail risk than naive baselines. By quantitatively evaluating the technical performance of each layer, the study shows that integrated WEFE technologies can be embedded in engineering curricula as a reproducible and measurable instructional infrastructure rather than as a standalone enrichment tool.

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