Assessment Breakdown
The course is assessed entirely through coursework. Assessment is structured to balance continuous engagement, individual analytical work, and collaborative project-based production.
| ID | Assessment Component and Description | Individual (%) | Group (%) |
|---|---|---|---|
| A.G | General Participation: Continuous assessment of engagement across lectures, tutorials, desk crits, group discussions, and peer-review activities. Emphasis is placed on the quality of contribution, clarity of communication, and constructive interdisciplinary interaction. Attendance is logged via pop-up Mentimeter surveys. | 15 | – |
| A.0 | Applied Exercise: Complete a short, guided exercise involving the collection, preparation, or annotation of data for use with Generative AI. Identify relevant features or information from the selected data, test how an AI system interprets or responds to them, and briefly compare the resulting outputs with human observations. | 20 | – |
| A.1 | Case Study: Students will complete one of three individual case study topics: (1) Mechanics & Application — investigate how a Generative AI system works and how one aspect of its design influences what it can and cannot do; (2) Responsibility & Ethics — investigate an ethical issue arising from Generative AI, its consequences, who is responsible, and how the issue might be addressed; or (3) Datasets & Risks — investigate how data used for Generative AI is collected, generated, or prepared, and how these choices influence AI outputs and associated risks. | 20 | – |
| A.2 | Course Project: A team-based AI-assisted design investigation addressing a meaningful problem/challenge. Group outputs include a mid-term presentation for formative feedback and a final project submission comprising an abstract, slides and artefacts, video, and design-oriented paper. Individual assessment is based on reflective documentation of specific contributions, learning, and collaborative practice. | 5 | 40 |
Total: 100% coursework
Examination: None