Faculty: Suzanne Loui, Environmental Studies
Course: Environmental Modernism
TL;DR
Students choose a historical extreme weather event and use NotebookLM to build a factual timeline, sort research into “factual” and “trauma-informed” categories, and then independently identify whose experiences are missing from the record. They then turn to Gemini to weigh “hard” structural and “soft” nature-based hazard mitigation technologies for that place, before feeding the documents and their own reflections into NotebookLM to generate a podcast that synthesizes the materials into a narrative.
What is being done
Planning for a disaster before it strikes calls for a different set of skills than responding to one after the fact. This assignment in Environmental Modernism asks students to build those forward-looking, anticipatory skills, using AI as a research and brainstorming partner while reserving the ethical judgment calls for themselves.
Students choose a historical extreme weather event (a drought, wildfire, tornado, or flood) in a location of their choosing and log every major AI interaction using a consistent three-part pattern: The Prompt (what they asked the AI to do and why), The AI Artifact (what the AI produced), and The Human Pivot (what they chose to keep, change, or reject).
Source Curation & Timeline (NotebookLM): Students use NotebookLM, an AI research tool that can search, organize, and summarize source documents, to conduct a deep search for eight academic articles or case studies about their event, which the AI sorts into “factual” and “trauma-informed” categories. Independent of AI, they narrow this list to six sources and analyze where the gaps lie, whose voices or topics are missing from the data, since, as the assignment puts it, AI knows what it has been fed but cannot notice an absence of representation the way a person can. Students also use NotebookLM to construct a basic factual timeline of the disaster, then independently explain why certain moments carry lasting weight in the community’s memory of the event.
Mitigation Design (Gemini): Using the instructor’s exact prompt, students query Gemini, a separate AI assistant, to identify hazard mitigation technologies for their chosen event and place, ranging from “hard” structural technologies (like seawalls, levees, or shelters) to “soft” nature-based solutions (like horizontal levees for wetlands restoration or other green infrastructure). Independent of AI, they weigh these options against what the community and landscape have been through, then choose one hard and one soft technology and explain how each would improve human and environmental resilience.
Podcast Synthesis (NotebookLM): In the final step, students feed their complete AI Journey Log, including their own reflections, back into NotebookLM’s Audio Overview feature to generate a podcast that synthesizes the event timeline, community experiences, and mitigation possibilities, ensuring the AI hosts engage with the student’s own human insights rather than simply repeating the facts.
This workflow adds value by treating AI as a research and brainstorming partner while reserving ethical judgment for the student. Students are graded down if they let AI make interpretive calls on their behalf, such as judging how emotionally respectful a source is, since, as the instructor’s rubric puts it, evaluating what counts as “deeply respectful” is an entirely human judgment call that AI cannot replicate.
Adapting Across Disciplines
This framework, using AI to compile raw information then running a manual equity or gap check adapts easily across fields. In urban planning, students could use AI to gather zoning precedents from redevelopment efforts, independently flag whose housing needs go unaddressed and then weigh AI-suggested infrastructure and policy options against the neighborhood’s needs and history. In business courses, students could use AI to gather case studies on a company’s handling of a major misstep, independently identify which stakeholders’ concerns are missing from the official narrative and weigh AI-suggested operational and reputational fixes. In history or literature courses, students could use AI to gather sources on a social movement, independently flag whose perspective is underrepresented and compare institutional and grassroots framings for its legacy.
Generative AI Teaching Repository
The assignment was developed through the AI Curriculum Corps in partnership with the Center for Teaching and Learning. The CTL Generative AI Teaching Repository serves as a centralized hub for WashU faculty to share, discover, and adapt innovative AI-integrated assignments. To explore peer-reviewed class activities or contribute your own framework, visit the CTL Generative AI Teaching Repository.
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