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Abstract Many of our generation’s most pressing environmental science problems are wicked problems, which means they cannot be cleanly isolated and solved with a single ‘correct’ answer (e.g., Rittel 1973; Wirz 2021). The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) seeks to address such problems by developing synergistic approaches with a team of scientists from three disciplines: environmental science (including atmospheric, ocean, and other physical sciences), AI, and social science including risk communication. As part of our work, we developed a novel approach to summer school, held from June 27-30, 2022. The goal of this summer school was to teach a new generation of environmental scientists how to cross disciplines and develop approaches that integrate all three disciplinary perspectives and approaches in order to solve environmental science problems. In addition to a lecture series that focused on the synthesis of AI, environmental science, and risk communication, this year’s summer school included a unique Trust-a-thon component where participants gained hands-on experience applying both risk communication and explainable AI techniques to pre-trained ML models. We had 677 participants from 63 countries register and attend online. Lecture topics included trust and trustworthiness (Day 1), explainability and interpretability (Day 2), data and workflows (Day 3), and uncertainty quantification (Day 4). For the Trust-a-thon we developed challenge problems for three different application domains: (1) severe storms, (2) tropical cyclones, and (3) space weather. Each domain had associated user persona to guide user-centered development.