Prompt. Explore.Submit your experiments.
Try new AI capabilities, get agent feedback and track your progress with the class.
Scan to submit your experiments
AI practice feedback
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Every enrolled student, including those still to submit. Earlier classroom projects are linked below. AI practice scores /100 are separate from university grades. See the official weights and criteria →
| Rank | Student | Group | Submission | AI practice /100 | Feedback |
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Projects
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Open a project, try it and give useful feedback. Expand a card for links, commits and evidence.
Course evidence: ✓ code or documentation found · ○ pending, unknown or not detected. Evidence does not confirm a working feature or set your grade. Expand a card’s checks for evidence and the live test to perform.
University course grading
Participation 15% · Digital Presence 30% · Digital Advertising 20% · Experimentation 35%.
Each component needs at least 5.5/10. Official marks require Field Expert assessment; saved projects and AI scores do not automatically award grades. Read all university grade bands →
Prompt. Explore. Show what you can do.
This is classroom practice, not the complete university assessment. Register your name, GitHub and live website here, and use the experiment log for feedback. Upload your individual portfolio and supporting evidence to the university’s separate Connect platform. See exactly what to upload and how it is graded →
Everyone must submit. Use AI to solve real problems in your everyday life, studies or work, and discover different useful things you can do. Start with one experiment today, then keep adding to it. Your name, submission status and agent score appear on the class leaderboard.
- Try something new: research a customer, write a campaign, create an image, build a page, analyse data, connect a service, automate a task or test a feature.
- Prompt, inspect, improve: save the prompt, show the result, verify it against sources, calculations or a practical test, and try a better prompt.
- Submit your experiment log: include what worked, what failed and what you learned. The agent rates your evidence and gives you a next challenge.
AI practice score /100 (not a university grade): real problems and useful exploration 25 · prompting and iteration 25 · verification and evidence 35 · learning 15. Explain what became easier, faster or possible. Usefulness earns up to 15 points; breadth earns up to 10. Honest failed experiments count toward learning. Repeating the same task does not increase breadth.
Everyday examples: understand a difficult topic, plan a realistic week, compare options using reliable sources, analyse a sample budget, build a useful website or automate a repetitive task. Show how you checked the answer instead of assuming AI is right.
Working version: 19 November 2026. Improve after feedback by 26 November 2026. Submit now even if your project is unfinished. A name or project link alone does not count as an experiment submission.
Submit your experiment log
Need design resources or a worked example? Open the assignment guide →
Sign in at the course hub, return here, then . Your course account matches your submission to the class roster.
Ideas for your next experiment
Use your business project as a playground. Pick a new capability, prompt an agent, inspect the output and show what you learned.
Research & communicate
Compare customer needs, improve your offer, draft a campaign, generate a visual or turn research into a presentation. Check facts and explain your edits.
Build & connect
Build a page, make a booking or enquiry flow, connect storage or email, or add a useful AI feature. Show the result and test it. Use test mode for payments.
Analyse & automate
Analyse a dataset, make a chart, track a useful event or automate a repetitive task. Check a result by hand and try a failure case.
Test & improve
Ask an agent to find a bug, test mobile and keyboard use, or improve work from feedback. Include the original prompt, revised prompt and before/after result.
You do not need every platform or all dashboard signals. Explore different capabilities and show concrete evidence. The existing project quests are optional milestones; their instructor-approved points are separate from the agent leaderboard.
Download assignment textDownload experiment templateDownload agent review promptAdd or update your project links
Optional project quests (separate from the agent leaderboard): 100 core points + 20 bonus. Expand your project to submit evidence from the browser you registered with. Instructor: sign in at the course hub, then .