CQUniversity Unit Profile.

Throughout the term, students complete a sequence of applied project-management activities in the weekly workshops and assemble the resulting artifacts into a portfolio workbook. The portfolio builds progressively from Week 3 to Week 10, with each workshop producing a self-contained artifact that demonstrates a specific project-management practice across the predictive, adaptive, AI, and sustainability approaches covered in the unit. Together the artifacts trace a project from initial structuring through to delivery planning, stakeholder and ethical considerations.

Some workshop activities are conducted in groups; in all cases, however, each student reports the activity and the work done individually, and is marked on their own submission. Core tools include Microsoft Project for the predictive planning artifact, alongside the templates and worksheets provided for each workshop.

The portfolio is submitted in three parts:

1. Sub-assessment 1A (10%) to be submitted on Friday - Week 6. It covers the Week 3–6 artifacts and marked only for completeness of the submitted artifacts
2. Sub-assessment 1B (30%) to be submitted on Friday - Week 11. It covers all artifacts generated between Week 1 and 10.
3. A self-reflection (10%) to be attached to Assessment 1B submission.

Attached to the Assessment 1B submission, each student writes a short individual reflection (approximately <300 words) on their experience building the portfolio across the term. Rather than summarising what was done, the reflection should consider how the unit's project management practices, principles, and ethics shaped the student's thinking and decisions — including how they worked with others during the group-based workshop activities, how ethical and professional considerations influenced their choices, and what they would do differently on a future project. 

The final portfolio (1B) is marked item by item. Each artifact is a separate, self-contained item with its own weight and its own row in the marking rubric.

The portfolio is marked item by item. Each artifact is a separate, self-contained item with its own weight and its own row in the marking rubric, so students can see exactly where each mark comes from — and a weak item is not tangled up with the others.

Marking criteria for 1A is based on the completeness of the artifacts submitted. 

Marking criteria for 1B will be based on submission of the following artifacts. Marking rubric will be shared on Moodle:

1. Work Breakdown Structure and schedule (Wk 3) (15%)
2. Stakeholders register, resourcing & risk register (Wk 4) (15%)
3. Sprint-planning artifact (Wk 5) (15%)
4. Product backlog & estimation (Wk 6) (15%)
5. CPMAI AI-project framing (Wk 7) (10%)
6. P5 Impact Analysis (Wk 8) (20%)
7. Hybrid project plan (Wk 9) (10%)

Marking criteria for self-reflection (10%) is based on:

1. Reflective depth & insight (35%)
2. Engagement with principles & ethics (35%)
3. Connection to practice (20%)
4. Communication (10%)

Overview

Students work in groups through the early phases of a real AI project using the CPMAI methodology, exactly as practitioners do. Each group is assigned a different AI project case study. Using a structured CPMAI workbook, the group completes Phases I–III — Business Understanding, Data Understanding, and Data Preparation — translating a loosely defined business problem into a properly framed, data-ready AI project.

This assessment mirrors how the early phases of CPMAI are applied in practice: before a model is developed, a project team must frame the business problem, define success, and establish that suitable data exists and is ready. The workbook structures exactly that problem-framing and data-readiness work. It develops students' ability to select and apply a context-appropriate delivery approach and to apply project management practice to a real implementation plan, while the business case, success measures, and Trustworthy-AI requirements draw in sustainable value and professional ethics.

Each group completes the workbook sections for the three phases:

• Phase I: Business Understanding. Define the business problem and objectives; specify measurable business and AI success criteria; construct a cost-benefit analysis; identify the relevant AI pattern; outline resource and schedule requirements; and document Trustworthy-AI requirements (data privacy, transparency, fairness, accountability) and the sustainability implications of the proposed solution.
• Phase II: Data Understanding. Identify and describe the data the project would need; explore what is available against what is required; and assess data quality, surfacing gaps, risks, and assumptions.
• Phase III: Data Preparation. Determine which data to select and why; set out the cleaning, enhancement, and labelling the data would require; and justify these choices in light of the Phase I objectives.

Throughout, the group's reasoning must be grounded in their specific case study. Generic, copy-the-example answers will not score well.

This assessment comprises three quizzes. Spaced across the term; during Week 5, Week 9, and Weeks 12. Together the three quizzes are worth 20% of the unit: Quiz 1 (5%) and Quiz 2 (5%) involve multiple-choice, true/false, and/or short-answer items, while Quiz 3 (10%) carries 40 marks and adds scenario-based items reflecting its more application-heavy content (CPMAI, professional ethics, and AI in project management).

Each quiz opens for a specific window to accommodate different schedules. Within that window students take a single attempt; the timer starts when they launch and does not pause. Questions are drawn in randomised order from a larger item pool, so attempts differ between students. The quizzes are designed as open-book. Therefore, items test understanding and application, so external notes won't substitute for engaging with the material. Standard academic-integrity rules apply: the work you submit must be your own. Outsourcing any part of a quiz to another person or service (including contract-cheating sites, peers, or tutors completing items on your behalf) constitutes academic misconduct, as does collusion or sharing quiz questions and answers with other students. The single-attempt, timed, randomised-pool design is the integrity control given the flexible window. 

Quizzes are completed and submitted in the unit's LMS (Moodle). Auto-marked items are scored on submission; short-answer and scenario items (if appears) are released after the window closes and marking is complete, with brief feedback on the manually marked items.

The design of the quizzes is elaborated below

Marking is based on correct answers only. Each correct response earns the marks allocated to that item; incorrect or unanswered items receive zero, with no penalty or mark deduction for wrong answers. Your quiz score is the sum of marks earned, so it is always to your advantage to attempt every question.