A Systematic Literature Review: Advances and Challenges of Generative Artificial Intelligence in Engineering

Abstract

This systematic literature review examines the advances and challenges of Generative Artificial Intelligence (GenAI) in engineering using the PICOC strategy and strictly following PRISMA 2020 guidelines to ensure transparency, reproducibility, and methodological rigor. The search conducted in Scopus (2023–2026) initially identified 24,166 records, which were filtered through predefined inclusion and exclusion criteria, resulting in a final sample of 98 studies. Findings indicate that GenAI applications are predominantly concentrated in industrial and chemical process optimization (59.7%), followed by generative design and simulation (19.4%), predictive maintenance (6.2%), and telecommunications (5.4%). Heuristic optimizers dominate methodological approaches (90.7%), complemented by advanced generative models (76.7%), Deep Learning (34.1%), and traditional Machine Learning (27.9%). Only 60.5% of the studies report experimental comparisons, revealing gaps in methodological standardization. Reported performance improvements include efficiency gains (54.3%), productivity increases (24%), and enhanced reliability (14%). Validation practices rely primarily on statistical cross-validation (91.5%) and real-world industrial testing (59.7%). However, persistent limitations involve data scarcity (23.3%) and high computational costs (14%). Overall, the evidence confirms that GenAI is emerging as a strategic technology for addressing complex engineering problems, although broader industrial validation frameworks and standardized evaluation metrics remain necessary to ensure scalable and sustainable adoption.