Future Water: Lessons learned and ways forward for transdisciplinary,cross-cultural water education

Increasingly adverse effects of climate change, accelerating urbanization, overtaxing of resources and aging infrastructure pose a wide range of challenges for current and future water managers. The scale and urgency of these challenges require innovative, holistic solutions, often surpassing the scope of traditional civil and environmental engineering education. Nonetheless, few educational initiatives or academic programs exist that enable civil and environmental engineering students to develop water management solutions in a transdisciplinary, international environment. The international web seminar Future Water, now in its second iteration, is designed to bridge this gap by offering students, educators, and researchers from all over the world such an environment.

In Future Water, students develop sustainable water management concepts for predefined settings in small, self-organized teams, supported by a set of formal lectures and input from a faculty advisor. This basic idea is the same for both iterations of the seminar; the status quo in academic education, however, has evolved considerably since the first seminar. When Future Water first took place just before the COVID-19 pandemic, working entirely online was novel to most participants. While virtual collaboration has become commonplace since 2020, the recent, yet widespread adoption of generative artificial intelligence (AI) presents another significant paradigm shift in water education, which had to be considered when conceptualizing the 2025 seminar.

In this work, we detail how the rise of AI tools in engineering education, combined with lessons from the first seminar, impacted the second’s conceptualization, materials and outcomes. For this purpose, data from both seminars - i.e., lecture recordings, meeting transcripts and students’ work products, as well as submitted questionnaires, time sheets and AI prompts - is combined. First, an integrated analysis of these materials is used to reveal commonalities, differences and gaps in student teams’ water management solutions. Second, the resulting insights are correlated with team members’ academic backgrounds, seniority, as well as group dynamics and communication characteristics. Third, the collected prompts are used to quantify the degree to which AI was adopted by civil and environmental engineering students and how it impacted their research, solution development and communication of water management concepts to interdisciplinary audiences.

Finally, we address how the compendium of findings affect the goal of establishing Future Water as a permanent forum for innovative water education beyond the limitations of traditional engineering curricula, national or societal boundaries.