A.2: Course Project : Final Project Submission

📅 Submit by: 2026.04.22 ⏰ 00:00 (Midnight before class)

📤 Upload your submission here:
CCAI9012 Final Project Submission Form

All components must be submitted together. Late submissions may affect evaluation and presentation scheduling.

Submission Components

Each team will submit four coordinated artefacts that together communicate the project as a coherent whole.

This structure follows the case study format, featuring:
(1) Annotation; (2) Artefact (now in the form of an academic paper); (3) Vignette (a short video presentation); and (4) optional evidences.

0. Online Gallery Post

As with previous assignments, you are expected to prepare the core items together—namely the paper and video—as a coherent pair.

For archival purposes, you will also translate your work into an online gallery post, which should communicate the project as a self-contained narrative through a sequence of image slides.

Be strategic in how you construct your material:
- develop core figure components that can be reused across your paper, video, and gallery post
- maintain a consistent narrative across formats

The gallery post can be understood as a image slide-based version of your paper and video, presenting the same contents through a digestible visual image sequence. There is no need to do new work.

Please prepare your online gallery post using a template similar to your case study [here].

1. Annotation: Title & Caption

• 300-word overall case description
    – Defines context, framing, and significance of the project

• Three focused sections (max 200 words each):
    – Ethics, responsibility, and governance
    – Application mechanics and system pipeline
    – Data construction and associated risks

These sections should clearly relate to one another, demonstrating how the system operates through the interaction of these dimensions.

2. Artefact: Paper

• Max 1,500 words
• Max 10 pages (including figures, excluding references)
• Must follow provided template (to be released)
• Written in an academic / scientific format

Suggested Structure (Indicative rather than mandatory. Sections may be adapted or combined, provided the reasoning remains explicit and well structured):

• Introduction & Motivation
    – Define the problem and why it matters
    – Situate within context (e.g. built environment, sustainability)

• System Overview & Approach
    – Describe the overall workflow or pipeline
    – Clarify the role of AI

• Methods & Implementation
    – Data sources / generation / preprocessing
    – Models, tools, and technical decisions

• Results & Outputs
    – Demonstrate system behaviour through examples or visuals
    – Avoid overstating results

• Analysis & Reflection
    – Critically examine interaction between:
    ◦ mechanisms
    ◦ datasets
    ◦ responsibility

• Limitations & Future Directions
    – Identify constraints and possible improvements

• Conclusion
    – Summarise contribution and key insights

3. Vignette: Short-Form Video

• Duration: Approximately 5-8 minutes

The video should communicate the core argument of the project under limited viewing time. You must use a vertical viewing format.

• Present:
    – the central idea
    – the system or workflow
    – key findings or insights

• The video should not reproduce the full paper, but instead:
    – highlight key contributions
    – communicate clearly and concisely
    – focus on coherence and narrative

4. Evidences (Optional)

You may include supplementary materials in an appendix form, such as:

• Code snippets
• Parameter sweeps
• Prompt comparisons
• Additional diagrams or tables
• Experimental results

What Your Submission Should Demonstrate

The final submission should communicate a complete and resolved project.

• Problem Framing

    – A clearly defined and bounded problem
    – A meaningful context and motivation

• AI-Assisted System

    – A coherent system, workflow, or design proposal
    – A clear role for AI within the project

• Implementation and Results

    – Demonstration of system behaviour through outputs
    – Evidence of testing, iteration, or evaluation

• Critical Integration

    – Reasoning across mechanisms, datasets, and responsibility
    – Awareness of limitations, risks, and implications

Expectations

The emphasis is on clarity, coherence, and critical thinking.

Your submission should:

    • writing
    • visuals
    • narrative

Grading Criteria

In a nutshell

We are not grading:

We are grading:

Criterion What It Means
Problem Framing Is the problem or opportunity clearly defined, bounded, and situated in context?
System Concept Is the proposed AI-assisted approach understandable, coherent, and plausibly aligned with the problem?
Process Thinking Do you demonstrate structured thinking about workflow, methods, and experimentation (i.e., how you will build, test, and iterate)?
Integration Do you reason critically about how mechanisms, datasets, and responsibility interact in the creation and use of the system?
Communication Are ideas communicated clearly through visuals and a coherent spoken narrative?

Rubric

Criterion Excellent Adequate Insufficient
Problem Framing Clear, specific, well-motivated problem with appropriate scope and constraints Problem identifiable but somewhat broad, vague, or under-scoped Problem unclear, overly broad, or poorly motivated
System Concept System/workflow clearly articulated with logical structure and clear role of AI Approach described but with gaps in structure, feasibility, or AI role clarity Approach confusing, inconsistent, or not clearly AI-assisted
Process Thinking Clear plan for methods and experimentation; shows iterative logic (what will be tested, how, and why) Some plan or early experiments shown, but methodology is underdeveloped Little evidence of planned or executed experimentation; process is ad hoc
Integration Thoughtful early synthesis of mechanisms + datasets + responsibility, with clear implications for design decisions Mentions these dimensions but connections are shallow or unclear Treats dimensions separately or ignores one or more; no critical reasoning
Communication Strong visual storytelling; minimal text; clear pacing; narrative makes feedback needs explicit Understandable but uneven; some text-heavy slides or unclear transitions Hard to follow; too dense; visuals do not support the narrative