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Artificial intelligence (AI) for supporting second language (L2) writing processes and practices has garnered increasing interest in recent years, establishing AI-mediated L2 writing as a new norm for many multilingual classrooms. As such, the emergence of AI-mediated technologies has challenged L2 writing instructors and their philosophies regarding computer-assisted language learning (CALL) and teaching. Technologies that can combine principled pedagogical practices and the benefits of AI can help to change the landscape of L2 writing instruction while maintaining the integrity of knowledge production that is so important to CALL instructors. To align L2 instructional practices and CALL technologies, we discuss the development of an AI-mediated L2 writing technology that leverages genre-based instruction (GBI) and large language models to provide L2 writers and instructors with tools to enhance English for research publication purposes. Our work reports on the accuracy, precision, and recall of our network classification, which surpass previously reported research in the field of genre-based automated writing evaluation by offering a faster network training approach with higher accuracy of feedback provision and new beginnings for genre-based learning systems. Implications for tool development and GBI are discussed. 

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.