Tutorial Outlines

You will find basic information regarding tutorials below:

Stream 1

<<💻🔧 Python Initialisation>>

• Set up your computer for programming and running AI/ML models.
• Learn the basic workflow for starting code development.

<<💻📝🧠 Getting Started>>

• Learn basic programming principles using Python.
• Understand the fundamentals of LLM interaction through programming and online UI.

Please find code covered by tutorial [here].

<<💻📝🧠 Advancing in Python>>

• Apply Python to basic scientific computing with NumPy.
• Load and manipulate data using pandas.
• Create and customise basic visualisations with Matplotlib.

Please find code covered by tutorial [here].

Stream 2

<<🎬 Basic Movie Editing>>

• Acquire fundamental movie editing skills.
• Please come with CapCut installed.

Please view the recorded session [here] and related material [here].

<<✍️ Précis Writing>>

Please bring an article of no more than 1,000 words, fiction or non-fiction, on a subject that matters to you. Choose the article with care; it should be one you have already read and can examine critically. It will form the basis of the Précis Writing Exercise. Please bring both a printed copy and a digital version of the text. The exercise includes both AI and non-AI components; please bring a laptop.

Please view slides [here].

<<✍️ Problem Statement>>

(1) Key Idea

This tutorial develops your ability to write a clear, well‑scoped problem statement as a foundation for your course project.

A problem statement is a concise account of a situation in which something is missing, unknown, inadequate, or harmful. It should say:

• what the problem is
• where it occurs
• who/what is affected
• why it matters now

Equally important is what a problem statement is not:

• not a method (“I will use AI to analyse…”)
• not a solution (“I will build an app that…”)
• not just a broad topic (“AI in education”) without a specific issue in a concrete setting

Methods, tools, and objectives are responses to a problem; they come after the problem has been clearly defined.

Your problem should either:

• concern the use or effects of AI, or
• be something that could meaningfully be investigated using AI.

(2) Preparation

Bring to the tutorial one draft problem statement (about 150–200 words) that is:

• Concrete – focused on a specific issue, not “AI is biased” in general
• Situated – grounded in a real context (a course, platform, community, workflow)
• Clearly bounded – small enough to investigate within this module

You are not expected to propose a solution, tool, or methodology at this stage.

(3) Example Statements

To make this less abstract, the brief includes several example pairs:

• an example problem statement – the kind of thing you are being asked to write
• an example objective/method statement – a possible direction someone might pursue later
  The objective/method is shown only to illustrate how a clear problem can lead to an approach. It is not part of the problem statement and is not required for this tutorial.

Note: These example directions (analysing AI, using AI for analysis, developing AI‑powered tools) are illustrative rather than exhaustive; other directions are also possible, as long as they grow from a well‑framed problem.

• Analysing AI

  • Problem Statement: AI image generators used to create “professional” profile images for platforms like LinkedIn may depict women predominantly as young, white, and conventionally attractive, even when prompts are neutral. This risks reinforcing narrow stereotypes about what a “professional woman” looks like and marginalising those who do not fit this image. There is little systematic evidence on how frequently this occurs or which visual attributes are most affected.
  • Possible Direction: Analyse outputs from one or more image‑generation systems using controlled prompts about “professional women”, and assess how attributes such as age, race, and body type are represented.

• AI for Analysis

  • Problem Statement: In a second-year interaction design module, students submit written reflections, but there is no systematic way to see which themes or user groups are addressed across the cohort. Tutors suspect that accessibility and disability are rarely discussed, yet this has never been examined at scale. As a result, important blind spots in students’ assumptions and in the curriculum may remain invisible.
  • Possible Direction: Use AI-based text analysis to scan a corpus of student reflections and identify how often issues such as accessibility, disability, and other user characteristics are mentioned.

• Developing AI‑Powered Tools

  • Problem Statement: In a first-year visual communication course, tutors must give feedback on many poster designs in short studio sessions. Time pressure and differing critique styles lead to inconsistent, often vague comments, leaving students unsure how to improve or how their work maps to assessment criteria. This makes it difficult for students to understand expectations and track their progress.
  • Possible Direction: Prototype an AI-assisted feedback tool that helps tutors quickly generate structured, criteria-based comments on poster designs.

(4) Tutorial Plan

In the session, we will share and discuss draft problem statements in small groups, check that each one states a clear, well‑scoped problem (not a solution, method, or vague topic), and tighten scope, context, and stakeholders while loosely connecting problems to possible AI‑related directions; by the end, you should have a clearer, more focused problem statement and a rough sense of how it might develop later in the module.

<<🎤 Presentation Workshop>>

This session introduces key strategies for communicating ideas clearly, effectively, and with confidence across different formats. Students will explore structure, visual storytelling, and delivery techniques through guided examples and discussion.

Please view slides [here].

<<🧠 Crit-Syle Peer-to-Peer Review>>

This session introduces the practice of design crits, a core component of architectural studio culture that fosters critical thinking through structured dialogue and reflection. Students will learn to give and receive constructive feedback, applying these skills during midterm Pecha Kucha presentations to evaluate course projects, refine ideas, and develop more thoughtful and rigorous design responses.

Please view slides [here].