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.1 | Case Studies: A set of individual case studies developing critical and technical literacy in AI. Topics span responsibility and ethics, system mechanics and behaviour, and datasets and data-generation pipelines. Students are assessed on analytical depth, technical understanding, and the ability to articulate risks, limitations, and governance implications in structured written form. | 30 | – |
| A.2 | Course Project: A team-based AI-assisted design investigation addressing a built-environment challenge. Group outputs include a mid-term presentation for formative feedback, a final recorded presentation, a project video, and a written paper or report structured to conference or journal conventions. Individual assessment is based on reflective statements and peer assessment, evaluating specific contributions, learning outcomes, and collaborative practice. | 10 | 45 |
Total: 100% coursework
Examination: None