Class Meetings

Mondays and Wednesdays, 2 - 3:30 PM.

- Merged sessions will be held in room 4-145.

6.S891: Biodiversity and environment

Sessions for this fork will be held in room 4-145.

• Canvas: https://canvas.mit.edu/courses/37848
• Piazza: https://piazza.com/mit/spring2026/6s891

12.S992: Climate models

Sessions for this fork will be held in room 55-110.

• Canvas: https://canvas.mit.edu/courses/36079
• Piazza: https://piazza.com/mit/spring2026/12s992

6.S893: Power and energy systems

Sessions for this fork will be held in room 4-257.

• Canvas: https://canvas.mit.edu/courses/37849
• Piazza: https://piazza.com/mit/spring2026/6s893

Course Structure

This is the first time we are running this course. As such, it is experimental and the exact structure and timing may be subject to change.

Homework and Assessments

• In-Class Quizzes: Quick in-class quizzes will be given electronically at the beginning of each class session, starting in the second class. Each quiz will cover content from the previous lecture and/or from required paper readings (see next).
• Paper Readings: During forked weeks, there will be ~3-5 papers assigned weekly within each fork as required reading. Readings should be completed before the class in which they will be covered; a schedule of readings will be made available on the course website. Additional optional readings will also be available for those who are interested.
• Paper Reflections: Each Tuesday during the forked weeks, students will be required to submit a 1-page paper reflection for one of that week’s required readings. These should summarize the paper, provide opinions and critiques, and propose possible extensions.
• Paper Presentations: Approximately 2-4 times throughout the forked weeks, students will be asked to conduct an in-class presentation of a required paper reading and subsequently lead an in-class discussion on that paper. Presentations will be done in groups, and should contextualize the paper, summarize its content, assess and critique the content, propose possible extensions, and pose discussion questions. A sign-up sheet for presentations will be circulated in the first few weeks of class.
• Spring 2026: Each paper will be presented by 2 people. Each registered student is required to present 3 papers.
• Group Project: Students will have the opportunity to propose a new AI-based research project for a climate application. We are very open to broad interpretations of both of these. Proposals should be clearly motivated by the climate need, as well as clearly grounded in the existing AI literature. Students will be asked to give a project presentation during the last few weeks of the forked lessons, and to submit a written report by the end of the semester.

Grading

• 10% Paper reflections
• 10% Quizzes (in class)
• 30% Paper presentations
• 50% Project

Late Assignment Policy

• Paper reflections can be submitted up to a week after the original deadline, with a five-point deduction per late day. Additionally, each student is allotted three automatic one-day extensions (“free late days”) that can be used for paper reflections with no penalty. More than one “free late day” can be applied per paper reflection.
• Paper presentations and project deliverables: Late submissions will not be accepted for paper presentations or the project deliverables. In the case of extenuating circumstances, please reach out to the course instructor for your fork.
• Quizzes must be completed in-class, and cannot be completed outside of the time they are assigned. The lowest quiz grade will be automatically dropped (absence counts as low grade).

AI Assistants Policy (Honor Code)

Paper reflections and paper presentations: We expect all paper reflections and presentations to reflect your knowledge and ability to communicate concepts learned from the readings. As such, we ask that all reflection text and presentation content be written and created by you, and that you do not (e.g.) upload the paper and assignment prompt to AI assistants to ask them to generate your reflections/presentations for you, or to otherwise use AI assistants to create all or parts of your reflections/presentations. You have elected to take this class because this topic excites you, and part of the learning process is to think critically about these topics. Using AI assistants to generate the reflections will be a waste of time, especially your own. To get the most out of this class, we encourage you to take the assignments seriously, read the papers, think deeply about the content, and be prepared to make mistakes; don’t deprive yourselves of this experience just because shortcuts are available.

Project:

• You are not allowed to ask AI to generate ideas or content for the project. We hope that by participating in the class, you will be able to have lots of ideas and context to be able to formulate a proposed project well.
• Allowable uses of AI assistants are to discover relevant literature, and to co-edit prose (with the strong request that you do not generate a bulleted list, ask AI to expand on it, and submit it without iteration — this usually reads badly and we do not want to read annoying AI-generated overhyped text :] ).

Collaboration Policy

We strongly encourage students to discuss course concepts and approaches with other students and with the teaching staff to better understand the material. However, it is important that work that you submit under your name be entirely your own. In particular, you must write up your course reflections independently, and your paper presentations and project deliverables should reflect the work of you and your group members. When you submit an assignment under your name, you are certifying that the details are entirely your own work and that you played at least a substantial role in the conception stage. All quizzes should be completed fully independently, without discussion with other students and without consulting external resources.

Students should not take credit for work done by other students. Copying work, or knowingly making work available for copying, in contravention of this policy may incur reduced grades, failing the course, and/or other disciplinary action. For more information, see the MIT academic integrity handbook.

Inclusivity

MIT values an inclusive environment. All members of this course are expected to contribute to a respectful, welcoming, and inclusive environment for every other member of the course, regardless of background, beliefs, ethnicity, national origin, gender identity, sexual orientation, religious or political affiliation - and other visible and nonvisible differences. If this standard is not being upheld, please feel free to speak with course staff.

If you have experienced an incident of discrimination, harassment, or bias (either inside or outside of this course), we invite you to reach out to course staff so they can connect you with available resources. Please note that instructors and teaching assistants are required to notify MIT's Institute Discrimination and Harassment Response Office of some kinds of incidents (including sexual assault, sexual harassment, intimate partner violence, or stalking among other situations).

Mental Health

As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may impact your ability to attend class, concentrate, complete work, take an exam, or participate in daily activities.

If you are experiencing such challenges, we encourage you to reach out for academic and personal support here: https://studentlife.mit.edu/support/support-services

For urgent or after-hours concerns, please contact MIT Police at 617-253-1212 or the Student Mental Health & Counseling Services (SMHCS) Clinician on Call at 617-253-2916.