Sustainability has emerged as a prominent concern globally, extending its influence into various domains, including education. It is recognized as of utmost importance to address global environmental challenges. However, there is a critical gap in the perception of innovative teaching strategies, i.e., interdisciplinary collaboration, experiential learning, and targeted approaches, to improve sustainability literacy and its applications. This review analyzes existing environmental and sustainability education frameworks and approaches to determine desired learning outcomes and challenges associated with sustainability education. Also, it explores and identifies concepts, theories, and assumptions found within the literature review, promoting sustainability integration within engineering education. The review was conducted to facilitate the development and improvement of sustainability education within the Architectural Engineering discipline, a field known for emphasizing educational innovation and technical excellence. By synthesizing existing ideas related to sustainability and sustainable development, this work aims to guide curriculum designers and educators in fostering sustainability competencies among engineering students within the built environment.

Interlocking Compressed Earth Blocks (ICEBs) offer a sustainable alternative to conventional fired-clay bricks but remain hindered by inconsistent geometric designs and limited standardization. This study develops a stakeholder-weighted Analytic Hierarchy Process (AHP) framework to evaluate and select the most suitable ICEB geometry for sustainable and seismic-ready construction in developing regions. Five evaluation criteria—size, weight, interlocking effectiveness, reinforcement/grout provision, and handling ergonomics—were prioritized based on expert input from masons, engineers, architects, and researchers. The synthesized results ranked the HiLo-Tec-type geometry highest, followed by Thai-Rhino, Auram, and Hydraform designs. Unit weight (0.289) and reinforcement capacity (0.261) emerged as dominant decision factors. Sensitivity analysis confirmed the robustness of rankings under varying weight perturbations. The AHP framework identifies the top-ranked geometry, whose structural performance was examined experimentally through a full-scale cyclic test on a grouted double-wythe ICEB wall, revealing enhanced ductility and residual strength compared with traditional brick masonry. The proposed framework demonstrates that selected ICEB geometry can balance ergonomic and structural performance while meeting seismic resilience demands. Beyond geometry selection, the model provides a replicable decision-support tool adaptable for regional material innovations in sustainable construction.

Despite growing emphasis on sustainability across engineering disciplines, empirical evidence on how structured interventions shape students’ sustainability knowledge and understanding remains limited. This study introduces and evaluates a set of purposefully designed sustainability modules integrated into an architectural engineering design studio. It addresses a persistent gap in student knowledge due to insufficient and non-coherent integration of sustainability topics. Noting the increased necessity to tackle complex sustainability challenges by systems thinking and applied design, the modules were designed to support learning across five progressive steps: foundational concepts, specialization and peer teaching, hands-on assessment, project-based integration, and reflective synthesis and future implementation. Using a mixed-methods approach, the modules were tested through pre- and post-intervention assessment, supported by statistical analysis (t = 41.92, p < 0.001) and qualitative feedback from students and instructors. The findings suggest significant improvements in students’ sustainability knowledge and in-depth engagement when incorporating active and collaborative learning strategies. Similarly, project-based learning and peer learning exercises were seen as most effective in fostering comprehension and applied understanding. The iterative approach of refining module’s content and delivery based on students’ feedback—such as incorporating reflective prompts in later sessions—improved conceptual clarity and strengthened student learning needs and relevance to the study topic. By addressing the gaps in knowledge and curriculum integration, this exploratory study offers a transformative framework to integrate sustainability into engineering curricula and highlights the importance of pedagogical strategies in promoting sustainability competencies within engineering.

Day-ahead photovoltaic (PV) forecasting must resolve the coexistence of a smooth diurnal generation envelope and shorter-scale fluctuations while providing uncertainty information suitable for operational decision making. This paper develops a multi-resolution Wavelet–CNN–LSTM framework for 24 h ahead PV power forecasting and evaluates it using a five-year PVGIS-derived dataset for a 334.116 MW Rabigh case study. The forecasting information set is restricted to the preceding 24 h of PV power and meteorological observations, together with deterministic target-day temporal variables. A validation study selects a Haar level-2 decomposition of PV history into $A_2$, $D_2$, and $D_1$ components, which are processed through parallel convolutional branches before recurrent fusion. The resulting network contains 87,032 trainable parameters. On the 2023 temporal evaluation period, the proposed model achieves an RMSE of 17.228 MW, $R^2=0.9641$, and a daylight RMSE of 24.636 MW, corresponding to 25.69% forecast skill relative to 24 h seasonal persistence. A 7-day moving-block bootstrap confirms clear RMSE reductions relative to persistence and XGBoost, while the intervals for LSTM and CNN–LSTM include zero, indicating statistically comparable performance among the strongest recurrent models. Probabilistic forecasts obtained from 100-pass Monte Carlo dropout yield daylight PICP = 0.9279, MPIW = 84.06 MW, and Winkler score = 122.08 for nominal 90% intervals; an independent conformal calibration set requires no additional widening of the neural interval. The results show that multi-resolution representation can improve squared-error performance without sacrificing model compactness, while calibrated uncertainty provides a complementary measure of forecast reliability.