Understanding generative artificial intelligence (GenAI) impact on the sustainability of civil engineering education and practice: A survey study on learning process

• GenAI enhances perception on problem-solving in civil engineering education. • Teamwork perception remains minimally impacted by GenAI use across contexts. • Students report greater motivation gains; professionals value collaboration. • Hybrid learning supports sustainable education aligned with SDGs 4 and 8. • Ethical GenAI use requires policy support and cross-contextual research. The rapid advancement of generative AI (GenAI) has significantly influenced educational and professional environments, offering personalized support and enhanced productivity. While GenAI is known for improving learning efficiency, its impact on the learning process among civil engineering students and professionals remains underexplored. This study surveyed both groups to assess their perceptions of GenAI adoption, focusing on its influence on subjects’ perceptions of efficiency, problem-solving, motivation, teamwork, and future potential. Quantitative (Likert scale) and qualitative (thematic) analysis were used to interpret the findings. Results showed that 75% of participants viewed GenAI positively for enhancing efficiency, with perception of problem-solving identified as the most improved skill (70%). However, its perceived effect on teamwork and collaboration was limited. Students perceived stronger benefits in problem-solving and motivation perceptions, while professionals reported more consistent collaboration advantages. Concerns included over-reliance and information accuracy, reinforcing the need for ethical and critical use. This study underscores the importance of hybrid learning, combining conventional and GenAI-assisted methods, to support sustainable education and workplace adaptability in line with Sustainable Development Goals 4 (quality education) and 8 (decent work and economic growth). To ensure responsible GenAI integration, policies must encourage its use as a supplement rather than a replacement for traditional methods. Findings offer insights into how GenAI can be ethically and effectively implemented across learning and work settings. Future cross-context studies are encouraged to further explore how learners and professionals engage with GenAI, particularly in higher education and engineering practice, ensuring long-term value, collaboration, and critical skill development.