K. Huang

Teaching

I teach environmental studies at NYU Shanghai in three courses: a debate-driven introduction to environmental thought, a systems-science gateway, and a course on the environmental challenges and opportunities of cities.

Award · 2025–2026

NYU Shanghai Teaching Excellence Award

Recognized in the 2025–2026 cycle. The classroom observation was of Cities at a Crossroads (SOCS-SHU 208), which NYU Shanghai also notes was named a Shanghai City-Level Undergraduate Key Course in 2025.

Award program (NYU Shanghai)↗NYU Shanghai feature story↗

Courses

SOCS-SHU 135CORE STS gateway

Environment and Society

Fall 2026

Introduces environmental thought through six structured debate cycles on contested motions: nature, movements, population, the commons, values, and technological futures.

  • Six two-week debate cycles with randomly assigned For/Against sides
  • IPAT quantitative lab and a classroom Public Goods Game
  • Final project applying course ideas to one real-world environmental issue
SOCS-SHU 204Environmental Studies gateway

Environmental System Science

2022–2025 · Fall 2026

A systems-science survey of energy and material flows, dynamics, footprints, and environmental change: one of the two gateway courses in the Environmental Studies track.

  • Six problem sets: population, prey–predator, S-curve growth, IPAT, personal energy footprint, resource-constrained growth
  • Mid-term presentation on the climate, biome, and environmental change of a student's hometown
  • Final presentation and report on a contemporary environmental challenge
SOCS-SHU 208

Cities at a Crossroads: Environmental Challenges and Opportunities in Cities

2022–2025 · Spring 2026

Urban environmental challenges and opportunities across energy, heat, water, food, materials, and land, with spatial data skills and city-focused policy projects.

  • Weekly analytical reports
  • Field-trip report
  • Group final city policy report (Tableau / Google Earth Engine optional)

★ Shanghai City-Level Undergraduate Key Course (2025)

Public syllabus↗

Course readings are distributed to enrolled students through Brightspace and are not posted here.

Signature methods

  1. Assigned-side debates

    SOCS-SHU 135

    Six debate cycles. Each cycle the class is randomly split into For and Against teams, and every member speaks. Defending a position you didn't choose is the fastest way to understand it.

  2. Device-free floor

    SOCS-SHU 135

    Evidence decks freeze 24 hours before each debate, and the floor is device-free. Preparation may use any tool, including AI; performance is live and unassisted.

  3. Oral defenses

    Across courses · TEA interview

    After an assignment, students get about ten minutes with devices off and answer concrete questions about their own work: Why this way? How did you get this answer? Even if AI helped produce the artifact, they still have to understand and explain it.

  4. Scaffolded analytical reports

    SOCS-SHU 208

    Weekly reports follow the course modules—energy, heat, water, land, waste—and rehearse the same skills each time: frame the problem, weigh evidence, name tradeoffs, and say what you would do. Complexity rises; the competencies stay fixed.

  5. Field observation vs. AI claims

    SOCS-SHU 208

    Teams combine satellite footprints with floor counts they collect on the street, then check fluent AI descriptions of density and street life against what people actually do. AI can help with calculation; judgment about a place still needs observation.

  6. Systems-dynamics problem sets

    SOCS-SHU 204

    Six problem sets move from exponential growth to resource-constrained feedbacks. Students build and run simple models, then carry that systems habit into mid-term and final analyses of real environmental change.

  7. Structured participation

    Across courses · 3rd-year review

    Device-minimal lecture segments, then laptops back for modeling. Turn-and-share and assigned-role formats make speaking turns more even, so a few voices do not carry the room alone.

  8. Motivation through fear and hope

    Across courses · TEA interview

    One thing AI does not easily replace is motivation. I engage students through the risks of climate change and through concrete hope—new technologies and ways of designing cities that could help.

Student work

Outside class I supervise undergraduate capstones and research through the CLUEs Lab. Projects, placements, and honors are on the lab and advisee pages.

Capstones & advisees →CLUEs Lab →

Teaching statement

I design each course from what students should still be able to do after the semester: build an analytical frame, connect ideas across fields, judge evidence under uncertainty, and state recommendations with the tradeoffs named. Generative AI is part of that setting, so adaptive learning matters as much as systems thinking.

In Environmental System Science, students work through feedbacks, energy–material flows, and system-dynamics models. In Cities at a Crossroads, they diagnose real urban problems (energy, heat, water, land) and propose evidence-based policy, including through fieldwork. Students may use generative AI as a technical assistant; they must check outputs against observation, and they perform live and unassisted when the grade is for judgment. The aim is that they leave better at framing questions, choosing methods on purpose, and acting on the socio-environmental systems they will inhabit.