BODY ELECTRIC ( HT 2557/HT 2812 )
BREANNA BROWNING-WILSON SPRING 2026, SCI-ARC
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Syllabus - Body Electric


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Scanning Electron Microscope image of bacteria inside the human body – from the Journal of Cell Biology (JCB)



Course Number
HT 2557 + HT 2812 
Spring 2026

Instructor
Breanna Browning-Wilson
breanna_browning@sciarc.edu

Course Meetings
Wednesday, 9:00 – 11:50 
Room 225


Course Abstract

The second design lab focuses on advanced orthographic techniques. Students develop experimental workflows for integrating data with their individual field research. Students are asked to articulate positions relative to the problematic relationship between fact and data in contemporary society.

Course Description

This seminar examines the body as a landscape in constant exchange with the world, positioning "you" as an environment rather than an observer of one. Students will gather and analyze self-data, explore perception mediated through computational tools, and examine the body's entanglement with biological and technological systems. The course asks students to develop new ways of modeling and representing what we sense but often can't articulate - treating the body as continuous with nature rather than separate from it.

Projects Overview 
The course is structured in three parts:

    1.    Gathering
    Supported by class readings and discussions, students will develop protocols and tactics for collecting self-data. Throughout this exercise, students will accumulate as much data as possible. The types of data collected will vary, and could include qualitative, quantitative, media-based, 3D, or other forms of information. This exercise will establish the body as a site generating, absorbing, and mediating constant flows of matter, energy, and information.

    2.    Mediation and Analysis
    Working with LLMs and other emerging technologies as perceptual prostheses, students will analyze, visualize, and transform the data collected in Phase 1. We will ask: what can computational tools help us see about our bodies and their entanglements with technological, biological, and environmental systems? What can these tools reveal that resists capture through traditional observation? 

    3.    Modeling
    In the final phase of the course, students will work to synthesize their earlier work into new models of the body as a landscape. Students may choose to continue and deepen their studies from phase 2 (creating more refined models and outputs using their gathered data and computational tools) or they may generate and speculate on novel strategies for representing the things we sense but are traditionally difficult, or impossible, to depict and communicate. The types of models and outputs from this section will vary, correlating to each student’s chosen inquiry.

Material Needs

Hardware (required):
  • Computer capable of running 2D/3D software tools
  • A mouse
Software – TBD per student. Could include:  
  •  3D tools (Rhino, Blender, Unreal)
  • Various AI tools (genAI, LLMs, etc)
  •  MCP tools
  • Photogrammetry/3D scanning
  • Data visualization

Note on technical skills:
As the second course in the Synthetic Landscapes Design Lab sequence, students are expected to leverage the technical skills developed in the previous semester, including 3D modeling and rendering, Unreal Engine, AI tools, etc. For students taking this course as an elective, written tutorials can be provided to support skill development upon request.

Technical support will be provided as needed and on a case-by-case basis. The instructor will help identify appropriate workflows, troubleshoot technical challenges, and connect students with relevant resources. Class-wide tutorials will be provided if necessary. Technical skills should be developed in support of critical inquiry.

Student Learning Objectives and Outcomes


  • Student Learning Objective 1: The course will challenge and expand students' ideas of "site," "landscape," and "data."
  • Student Learning Outcome 1: Students will reframe their understanding of site, landscape, and data through class discussions, readings, and field exercises. 

  • Student Learning Objective 2: The course will expose students to the use of computational tools in support of critical analysis.
  • Student Learning Outcome 2: Students will explore experimental workflows through project-based explorations in support of their research and writing. In-class critique sessions and conversations will support project development.

  • Student Learning Objective 3: The course will challenge students to develop novel representational strategies.
  • Student Learning Outcome 3: Students will use technological tools, physical modeling techniques, AI, and/or hybrid methods to model difficult-to-articulate conditions.


Course Organization 

All course materials will be uploaded to the class website (here). The course will have several components: 

  • Lectures: Presentations outlining the conceptual framework for the course.
  • Readings: Short essays or excerpts. Primarily provided in pdf format via the course website.
  •  Class Discussions: Structured conversations around assigned readings, project development, and themes of the class.
  •  Tutorials / One-on-one meetings: In-class technical help that supports project development and skill acquisition. 
  • Projects: Three projects (outlined in Projects Overview, above) 
  • Critiques: Class and/or small group critique sessions where students share works-in-progress or receive feedback.

Components of Grading


PERCENTAGE
DESCRIPTION

25
Project 1

25
Project 2

25
Project 3

25
Participation  

100
Total Percentage Possible

  • Detailed expectations for each project will be communicated via the course website.
  • Participation includes active engagement in class discussions, constructive participation in critique sessions, consistent attendance, and completion of assigned readings.

Course Schedule

To ensure the course aligns with the needs of the class and progression of student work, the schedule is subject to change. Please refer to the schedule page of the course website for the most updated information, including assignment and project deadlines.

Week 1: Introduction
Wednesday, Jan 14
  • Introduction
  • Syllabus overview
  • Course framing lecture
  • Assign intro readings  

Week 2: Intro to Project 1
Wednesday, Jan 21
  •  Group discussion
  •  Introduce project 1
  •  Work / brainstorming day
  •  HW: Come up with data protocols

Week 3: Project 1
Wednesday, Jan 28
  • Group discussion
  • Group critique – students to present their tactics and protocols for data collection
  • HW: Gather data

Week 4: Project 1
Wednesday, Feb 4
  •  In-class help/tutorial sessions
  • HW: Gather data

Week 5: Project 1 Due
Wednesday, Feb 11
  • Project 1 Presentations

Week 6: Intro to Project 2  
Wednesday, Feb 18

  • Lecture: Computation and perception (w/ possible guest - TBD)
  • Begin computational experiments
       
Week 7: Project 2
Wednesday, Feb 25
  • Group critique – share initial progress / directions
  • HW: continue computational experiments

(Synthetic Landscapes Studio Midterm Day)

 Week 8: Project 2
Wednesday, Mar 4
  •  Lecture + discussion: Language as sensing tool
  • HW: continue computational experiments

Week 9: Project 2 Due
Wednesday, Mar 11
  •  Project 2 Presentations

Week 10: Intro Project 3
Wednesday, Mar 21
  • Lecture: Models, representation, and speculation
  •  HW: Proposal for Project 3
       
Week 11: Project 3
Wednesday, April 1
  • Discussion: The non Vitruvian body
  • One-on-one sessions
  • HW: Work on Project 3

Week 12
Wednesday, April 8
  • One-on-one sessions
  •  HW: Finish Project 3

Week 13: Final Review
Wednesday, April 15
  • Final review day. Present work from all three projects.


Readings / Reference Material

Acquiring full texts is not required. PDF excerpts of most required readings will be provided to the class via the course website. The reading list is subject to change, and the list below represents a collection of relevant writings we will sample throughout the course.

  • Walt Whitman – I Sing the Body Electric (1855)
  • Lynn Margulis – Microcosmos: Four Billion Years of Microbial Evolution (1986)
  • Ed Yong – I Contain Multitudes (2016)
  • Gilbert Simondon – On the Mode of Existence of Technical Objects (1958/2017) 
  • Jonathan Crary – Techniques of the Observer (1990)
  • Norbert Wiener – The Human Use of Human Beings (1950, specifically “The Mechanism of Language”)
  • Kate Crawford – Atlas of AI (2021)
  • Emily M. Bender et al. – On the Dangers of Stochastic Parrots (2021)
  • Bruno Latour – We Have Never Been Modern (1991) 
  • Stacy Alaimo - Trans-corporeal Feminisms and the Ethical Space of Nature (2008)
  • N. Katherine Hayles – How We Became Posthuman (1999, specifically Chapter 1)
  • Lauren Slater for Harper’s Magazine - Dr. Daedalus: A Radical Plastic Surgeon Wants to Give You Wings (2001)
  • Mary Shelley Edited by David H. Guston, Ed Finn, Jason Scott Robert - Frankenstein: Annotated for Scientists, Engineers, and Creators of All Kinds By (2017)

Grading Procedures

Grades will be determined based upon the meeting of student learning objectives, quality of work produced, improvement over the course of the semester, completion of project requirements, quality of participation, attendance, attitude, and ethical conduct. SCI-Arc grading policies will be discussed on the first day of class, and any questions regarding grades or policies should be directed to the instructor and/or Lisa Russo, the registrar. A passing grade in the course requires committed completion of all projects. Incomplete work will not be evaluated.

From the SCI-Arc Student Handbook: SCI-Arc employs a narrative grading system, as follows: credit with distinction (CR+), credit (CR), marginal credit (CR-), conditional credit (CCR), no credit (NC), incomplete (I) and withdrawal (W). The grade of no credit (NC) is given whenever cumulative work, final work, and/or attendance are unsatisfactory. It is also given when a student fails to submit a final project or fails to take a final examination without prior approval from the instructor. No credit (NC) grades cannot be altered.

Grade

Point equivalent

CR+
4.0

CR
3.35

CR–
2.7

CCR
2.0

NC
0.0

I
0.0

W
0.0


Academic Integrity Policy (from SCI-Arc Student Handbook)

SCI-Arc takes issues of academic integrity seriously, including plagiarism, which can occur in design classes as well as core and elective classes. Some examples of plagiarism include:

•    Copying words, images, or other material without using quotation marks or other indications of the original source.
•    Paraphrasing another person’s ideas in your own words without crediting the original source.
•    Taking sole credit for assignments without giving credit to those who worked with you.
•    Submitting work for a course that has already/also been submitted for another course.
•    Internet plagiarism, such as submitting work either found or paid for online, failing to cite any internet sources used, or cutting and pasting sentences from various websites to create a collage of uncited words.

Note: the academic integrity policy contains several tiers, depending on the nature and extent of academic dishonesty involved and on whether the student has committed an academic offense before. All cases must be reported to the Academic Advisor and the report is added to the student’s academic record. Refer to the SCI-Arc Student Handbook for the full academic integrity policy including what happens at various tiers, right of appeal, and possible disciplinary outcomes.

Attendance Policy

 (For detailed policy, please refer to SCI-Arc Student Handbook)
Regular attendance and active participation are vital in architecture and design education, where learning is hands-on, collaborative, and iterative. As a result, students must not exceed the following attendance limits, regardless of whether absences are excused or unexcused, unless formal academic accommodations are approved. Exceeding the following limits may result in academic penalties such as grade reduction, course failure, or administrative withdrawal.

•    Seminar (1x/week): Max 3 unexcused / 4 total absences
•    Studio (3x/week): Max 4 unexcused / 6 total absences

Excused absences include illness, medical or family emergencies, religious holidays (with notice), legal obligations, military duty, and disability accommodations. Unexcused absences include undocumented absences, travel, or job conflicts without prior approval. Tardiness or leaving early may count as full absences.

Students must notify faculty in advance, submit documentation, and coordinate make-up work. Faculty must clearly state attendance policies, track attendance, and report extended absences (7+ days) to Academic Advising.

Incomplete Work

A student may receive a grade of incomplete (I) by requesting permission from the instructor prior to the date of the final examination or presentation. Permission will be granted only under extraordinary circumstances and usually for medical reasons. Incompletes must be fulfilled to the satisfaction of the instructor no later than three (3) weeks after the end of term. The student is responsible for providing the instructor with the “Request for Credit” card used for this purpose. This card must be signed by the instructor and returned to the Registrar’s office. Failure to do so will result in the incomplete (I) being changed to a no credit (NC). No credit grades cannot be altered.

Appeal of a Grade

Evaluation and grading of a student’s performance in a course is based upon the instructor’s professional assessment of the academic quality of the student’s per¬formance on a body of work. Such assessments are nonnegotiable, and disputes about them do not constitute valid grounds for an appeal. Students are encouraged to contact their instructor for clarification regarding the grade received in their course.

Grade appeals are rare and subject to appeal only for the following three grounds:
•    improper academic procedures that unfairly affect a student’s grade.
•    application of nonacademic criteria, such as: considerations of race, poli¬tics, religion, sex, or other criteria not directly reflective of performance related to course requirements.
•    sexual harassment.

Students must meet with the Academic Advisor to review the appeal process if they believe the grade received meets one or more of the grounds listed above. Petitions must be settled, and a final grade submitted to the registrar no later than six weeks after the end of the term in which the course was completed.

Archiving

The SCI-Arc Upload site is the school’s official archive of each semester’s work. This is a mandatory requirement of each student’s coursework, and grades will not be submitted until work is uploaded. This archive will be used for all future publica¬tions and graphic material as well as for required accreditation needs of SCI-Arc.  

Archiving requirements may vary based on curricular and/or project requirements. Specific instructions will be communicated to students each semester.

Ownership of Student Work (from SCI-Arc Student Handbook)

Physical copies of student work submitted to the school to satisfy course requirements including but not limited to digital files, papers, drawings, and models become the property of the school. SCI-Arc shall have no obligation to safeguard such materials and may, at its discretion, retain them, return them to the student, or discard them.

Notwithstanding whether it retains any physical copies of such student works, SCIArc shall have an irrevocable, royalty-free, worldwide right in perpetuity to use, reproduce, display, and exhibit works created by students during their studies at SCI-Arc, in publications by or about SCI-Arc, on its websites, on social media, or otherwise. SCI-Arc will make a reasonable effort to credit the author(s) of student work included in publications or other uses. Excepting works which are created during a student’s participation in a Sponsored Project, the student shall have the right to publish or present their own work without compensation to SCIArc. However, SCI-Arc must be informed of this publication and appropriately credited in this publication or presentation unless SCI-Arc requests not to be credited.

Supporting Mental Health

Diminished mental health, including significant stress, mood changes, excessive worry, or problems with eating or sleeping can interfere with optimal academic performance. Reducing stigma about accessing mental health care supports students seeking professional help when it is needed. SCI-Arc provides all students with two free counseling sessions per year. Sessions are available two days per week throughout the fall and spring terms, and one day per week during the summer term. All counseling sessions are confidential. Only with your consent can any details of your session be shared with another individual. Information to schedule an appointment can be found on the Campus Life tab of MySciarc.

Academic Accommodations

SCI-Arc provides reasonable accommodations in compliance with the ADA and related laws. Students must contact the Academic Advisor to begin the process, which requires documentation and is reviewed case-by-case. Students should share accommodation letters with faculty at the start of each term. Accommodations are not retroactive and do not excuse absences unless stated.

Contact the Academic Advisor or refer to SCI-Arc’s Academic Accommodations Policy for details. https://my.sciarc.edu/ICS/Advising/Academic_Accommodations.jnz



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