Graduate Studio:
Technology B

Week 1–5: Crack Inside the Black Box

This module examines how intelligence has been modeled, simulated, and represented across different historical and cultural contexts.

Rather than treating artificial intelligence as a recent technological breakthrough, the module situates contemporary AI within a longer history of technological attempts to formalize cognition, prediction, and decision-making. With critical readings, students will be challenged to compare how different systems define intelligence, what forms of knowledge they privilege, and how their underlying assumptions shape human perception and behavior.

By unpacking these historical precedents alongside the broader history of technology, students develop a critical foundation for understanding contemporary computational AI systems not as magical, autonomous intelligences, but as designed structures that emerge from datasets, rules, interfaces, and human decisions.

Project 1: Epistemological Research of Technology

Students will select a historical or computational model of intelligence and examine its underlying logic through research and prototyping. Models may range from ancient systems of knowledge and divination to mechanical devices, games, algorithms, simple rules, decision trees, conversational scripts, classification systems, or other procedural frameworks. By working with these underlying rules, structures and assumptions, students will explore how different technologies have attempted to represent or produce the appearance of intelligence.

Alongside reconstructing these systems, students are encouraged to consider how their prototype might simulate not only reasoning, but also affect. They may explore whether a minimal system can produce a convincing sense of emotional response or relational presence. Rather than passing a traditional Turing Test based on correctness, the project opens the question of whether a system can pass a more subjective or emotional threshold, where users perceive intention, personality, or care.

Week 5–10: Gazing Into the Code

This module introduces machine learning as a shift from general artificial intelligence and rule-based programming to systems specifically trained on classification, prediction, and decision-making. The algorithms powering today's software and generative tools actively scrape, label, and flatten everyday visual culture. This coded gaze inherits the legacy of Enlightenment-era taxonomy and colonial record-keeping, and now relies on the invisible human labor of data labeling to enforce existing social hierarchies and oversimplify nuanced complexity.

How do technologies decide what gets categorized, classified and sorted? What happens when complex, lived experiences are reduced to labels, patterns, and possibilities? And how do these massive algorithmic systems inherit and reproduce existing power dynamics by shaping what is visible, recognized or excluded?

In response, we will draw inspiration from contemporary practices in interaction design and creative coding to actively interrogate and expose these embedded biases.

How can we as designers, make hidden rules visible, challenge the categories it imposes and create alternative ways of organizing knowledge? Together, these works demonstrate how art and design can function as critical methodologies, offering speculative approaches, auditing techniques, and alternative frameworks that challenge the systemic flattening of AI slop.

Project 2: Uncertain Archive

In Project 2, students will act as both archivist and algorithm to examine how classification systems generate content, shape knowledge, and reproduce power. Students will manually construct a corpus of 100 to 500 distinct items and organize it using Morphology, Taxonomy, and Typology (MTT), approaching these systems as subjective and interpretive frameworks rather than neutral structures.

Through this comparison, students will begin to identify what each system makes visible or invisible. Manual classification may foreground nuance, intention, and situated knowledge, while AI systems often prioritize pattern recognition, scale, and speed. At the same time, both approaches introduce forms of bias, omission, and distortion. Students are encouraged to treat these differences not as problems to resolve, but as material to work with.

At this stage, students will develop these observations into a more fully formed design proposal, serving as an intermediate waypoint toward Project 3. Working with patterns, sequences, and a chosen medium, they will begin to articulate the relationship between human judgment and machine processing through form. A short written reflection will accompany the work, considering how both systems produce meaning, where they break down, and what kinds of cultural or aesthetic assumptions they reinforce.

Week 11–15: Disrupting the Chain-of-Thought

This module narrows our focus to Large Language Models (LLMs) and the contemporary obsession with natural language processing. Operating entirely on statistical probability, commercial systems like Claude, ChatGPT and Google NotebookLM simulate genuine comprehension through massive textual extraction. We will analyze how various proprietary and open-source models differ in their architecture, yet similarly inherit histories of industrial efficiency, cybernetics, and techno-utopian thought. By reducing collective cultural memory to computable weights, these foundation models frame intelligence as an extractable resource. Consequently, the prompt interface and techniques like chain-of-thought reasoning act as control mechanisms that enforce a rigid, standardized logic on how knowledge is produced.

Finally, the module explores how art and design can prototype speculative systems to subvert these dominant language paradigms. By analyzing these interventions, students will ultimately reclaim these language technologies as sites for critical world-building and design alternative interfaces that actively embrace uncertainty, slowness, and interdependence.

Project 3: Alternative Now(s)

For the final project, students will create a speculative design prototype that reimagines artificial intelligence beyond dominant narratives of productivity, optimization, and accelerationism. Using tools explored throughout the semester, including local language models, custom datasets, archives, physical computing, publishing, or other experimental media, you will imagine an alternate reality grounded in different values and ways of knowing.

This project asks you to move beyond pure critique alone. What happens after the critique is written? How do we avoid both technological utopianism and dystopianism, leaving space for desire, ambiguity, care, and collective imagination?

Rather than asking how AI can become more efficient or intelligent, consider how computational systems might support memory, ritual, ecological relationships, uncertainty, or other forms of knowledge generation.

Your final outcome should be an exploratory, open-ended design proposition that uses art and design to imagine alternative relationships with technology. Rather than offering solutions or predictions about the future, the work should engage critically with the tensions and possibilities of computation, exploring how technology might become a site for imagination, cultural meaning, and alternative forms of world-building. The medium is open and may take the form of an interface, publication, installation, artifact, performance, or something else entirely.

Your performance will be evaluated on its own merits, not based on comparing your work with other students. We focus on your understanding of concepts and your ability to apply them in a meaningful way. We don’t prioritize technology efficiency or professionalism as the primary criteria. We value the learning process and recognize that everyone progresses at their own pace.

Should you have any inquiries regarding grading, please don't hesitate to reach out to the faculty directly.

Active participation is essential and comprises 25% of the final grade. This includes, but is not limited to: keeping up with readings, assignments, and projects, contributing meaningfully to class discussions, active participation in group work, and coming to class regularly on time.

While attendance is one aspect of active participation, absence from a significant portion of class can compromise successful attainment of the course objectives. We consider a significant portion to be three weeks or 20% of class time. Lateness or early departure from class may be recorded as one full absence. We encourage you to let us know as much in advance as possible if you need to miss a class, are running late, or need to leave class early. More than three uncommunicated absences, late arrivals, or early departures will result in a deduction from your final grade.