- CET3016 Enterprise Software Engineering Assignment 1 Sample – Sunderland
- SWE4202 Computing Infrastructure UOB Portfolio Assignment Answer, UK
- TM353 IT Systems: Planning For Success Assignment Sample UK
- Agile Methodologies Assignment Answer UK
COM4024 Green Computing AI Report: Model Answer Extract (Arden University)
For information and reference only – not to be submitted as your own work. This is a model-answer extract for Arden University COM4024, written to show structure and standard. Your own prompts, sources, spreadsheet and figures must come from you.
This COM4024 model answer takes the green computing topic and shows how a strong Arden submission handles all three tasks of the COM4024 Academic Skills and Professional Development assessment brief: a prompt-engineering log with source checks, a five-role graduate comparison, and a SFIA-mapped development plan. Salary and energy figures come from named sources; anything personal is marked illustrative.
Reviewed by: David Lee, computer science and programming expert. He holds a Bachelor’s degree in Computer Science from the University of Glasgow and has about 4 years’ experience as a software engineer.
Struggling to turn your own prompts and spreadsheet into a 3,000-word report? Send us your COM4024 brief and we will respond with a guided plan.
What the COM4024 assessment asks for
Arden’s single assessment is worth 100% and can be a 3,000-word report or a 15-minute narrated presentation, with an Excel file for Task 3. You choose one of four topics. This extract uses green computing and sustainable technology.
| Task | Marks | Approx. words | What the marker wants to see |
|---|---|---|---|
| 1. AI prompt engineering and research | 35 | 1,050 | Iterated prompts, checked sources, honest reflection |
| 2. AI-assisted research into graduate roles | 30 | 900 | Five roles compared with citations; justified top 2 or 3 |
| 3. Personal development with Excel and SFIA | 35 | 1,050 | SFIA-mapped self-assessment, radar chart, SMART plan |
The marking matrix sits in the submission portal, so read it before you write. This answer follows the task descriptions only.
Task 1: Prompt-engineering log and source verification
Prompt iterations (illustrative log)
The log below is a worked example of the format. Your appendix must contain the real prompts and replies from Gemini or Copilot.
| Round | Prompt (summary) | Weakness found | Refinement |
|---|---|---|---|
| 1 | “Tell me about green computing.” | Generic, no sources | Add role, scope and output format |
| 2 | “Act as a research assistant. List five peer-reviewed sources on the energy cost of training AI models, with authors, year and DOI.” | Some references could not be located | Ask only for search keywords, then find the papers myself |
| 3 | “Give 10 search strings for Google Scholar on energy use of machine learning.” | Useful, but some strings too broad | Narrow by year and add “carbon” |
Round 2 is the point markers look for. An AI tool can produce a convincing reference that does not exist, so a source only counts once you have opened it.
Verifying what the AI said
| AI claim (illustrative) | Checked against | Outcome |
|---|---|---|
| Data centres use about 1.5% of global electricity | IEA (2025) | Confirmed: around 415 TWh in 2024, about 1.5% of global electricity |
| Training one NLP model can emit large amounts of carbon | Strubell, Ganesh and McCallum (2019) | Confirmed as a real paper; quote the figures from the paper, not the chatbot |
| Hardware and datacentre choices can cut footprint dramatically | Patterson et al. (2021) | Confirmed: authors report reductions of roughly 100 to 1000 times |
Notice what the table does not do: it never treats the chatbot as the evidence. The right-hand column points to a document you can open, cite and quote. Looking ahead, the IEA’s base case projects data centre demand of around 945 TWh by 2030, just under 3% of global electricity (IEA, 2025). That makes the green computing topic a live employability issue, not only a theory one.
Reflection: opportunities, challenges and skills
The main opportunity is speed: AI shortened my keyword search. The main challenge is trust. Arden’s library states that generative AI platforms are not considered credible academic sources and recommends its databases and journals instead (Arden University, n.d.a). I therefore used AI to find vocabulary and Google Scholar and the library to find evidence. The skills developed were prompt specificity, critical evaluation of sources and accurate Harvard referencing.
Need a computing specialist to review your Task 1 log? Our computer science assignment help team can check structure and referencing before you submit.
Task 2: Five graduate roles compared
Salary bands below come from Prospects job profiles, which describe them as a guide that varies by location and employer. For context, ONS reports median gross annual pay for UK full-time employees of £39,039 in April 2025 (provisional) (ONS, 2025).
| Role | Typical graduate starting salary | Experienced range (Prospects) | Skills the profile stresses | Green computing link |
|---|---|---|---|---|
| Software engineer (Prospects, 2025a) | £30,000 to £31,000 | £42,000 to £69,000 after a few years | Technical competence, problem-solving, communication | Efficient code, lower compute use |
| Data analyst (Prospects, 2026a) | £28,000 to £32,000 | £35,000 to £50,000 with experience | SQL, visualisation, data governance | Energy and emissions reporting |
| IT consultant (Prospects, 2026b) | £22,000 to £31,000 | Over £38,000 | Communication, client management | Advising on sustainable IT estates |
| Systems analyst (Prospects, 2026c) | About £30,000 | About £38,000 to £45,000 | Systems design, databases, client skills | Process and infrastructure efficiency |
| Environmental consultant (Prospects, 2026d) | £24,000 to £28,000 | £30,000 to £42,000 at about five years | Environmental data, legislation, GIS | Sustainability assessment |
Justifying the top three
I rank software engineer first because it pairs the highest starting band with a direct route to energy-efficient design. Data analyst is second: Prospects stresses data governance and visualisation, which match emissions reporting. IT consultant is third for its breadth. Environmental consultant pays less at entry and leans away from computing, so it is a fallback.
One caution: Prospects pages carry different update dates, so state the date you accessed each one. A student’s AI-generated salary table should never be pasted in unchecked.
Task 3: SFIA self-assessment and SMART plan
SFIA, the Skills Framework for the Information Age, describes responsibility levels for digital skills. SFIA itself says students do not need to assign themselves a level; the framework is a prompt for better questions about development (SFIA Foundation, n.d.a). Your workbook should still state your reasoning clearly.
Sheet 1 and 2: skills mapped to SFIA 9
Codes and level ranges below were checked on the SFIA 9 pages. The “current” column is an illustrative first-year self-rating, not SFIA guidance.
| SFIA 9 skill | Code | Levels defined | Current (illustrative) | Target in 3 years (illustrative) |
|---|---|---|---|---|
| Programming/software development | PROG | 2 to 6 | 2 | 4 |
| Data analytics | DAAN | 2 to 7 | 2 | 4 |
| Formal research | RSCH | 2 to 6 | 2 | 3 |
| Functional testing | TEST | 1 to 6 | 1 | 3 |
| Sustainability | SUST | 4 to 6 | Not yet at level 4 | 4 |
Note that SUST only defines levels 4 to 6 (SFIA Foundation, n.d.b). Other codes are from SFIA Foundation (n.d.c). A radar chart plotting a level-1 current value for it would mislead, so say so in the workbook notes. Apply conditional formatting to the gap column (target minus current): red for 2 or more, amber for 1, green for 0.
Sheet 3: SMART action plan
| Horizon | Specific action | Measure | Deadline |
|---|---|---|---|
| Short term | Complete a Python module and commit weekly code to GitHub | Four repositories with comments | End of this term |
| Medium term | Build one dashboard on public energy data | Published portfolio piece | Within 12 months |
| Long term | Secure a placement or graduate role in software or analytics | Offer received | Final year |
Tie each action to a source such as Lovelock and Wright (2024) on tech careers, and name what could go wrong. Markers reward a plan that admits risk. If the write-up itself is the sticking point, our report writing help covers structure and referencing.
Where marks are usually lost
Three slips come up repeatedly. First, a prompt log pasted without commentary: the marker cannot see what you learned from each round. Second, a reference list built from chatbot output, where authors, years or DOIs have drifted. Third, copying AI ideas straight into the answer: Arden’s academic skills guidance puts it bluntly, “never copy and paste ideas from AI” (Arden University, n.d.b).
Because COM4024 does ask you to work with Gemini or Copilot, the safe route is simple. Quote only what the brief requires, label it as AI output, include the prompt, and put your own analysis around it. Referencing details are covered in our Harvard referencing tips for UK students.
About this extract
The model answer is guidance only; the student remains responsible for following Arden’s academic integrity rules and for declaring AI use as the brief requires. For the full process, read our guide to Google Scholar and AI sources in COM4024, browse more Arden University assessment briefs, or submit your brief for a quote.
References
Arden University (n.d.a) Can I use generative AI as a source in my assignment? Available at: https://answers.arden.ac.uk/faq/282989 (Accessed: 21 October 2026).
Arden University (n.d.b) What are some ways I can use generative AI for academic work? Available at: https://answers.arden.ac.uk/faq/282954 (Accessed: 21 October 2026).
International Energy Agency (IEA) (2025) Energy and AI. Paris: IEA. Available at: https://www.iea.org/reports/energy-and-ai (Accessed: 21 October 2026).
Lovelock, C. and Wright, G. (2024) Careers in tech, data and digital. Swindon: BCS, The Chartered Institute for IT.
Office for National Statistics (ONS) (2025) Employee earnings in the UK: 2025. Released 23 October 2025. Available at: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2025 (Accessed: 21 October 2026).
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M. and Dean, J. (2021) Carbon emissions and large neural network training. arXiv:2104.10350. Available at: https://arxiv.org/abs/2104.10350 (Accessed: 21 October 2026).
Prospects (2025a) Software engineer. Available at: https://www.prospects.ac.uk/job-profiles/software-engineer (Accessed: 21 October 2026).
Prospects (2026a) Data analyst. Available at: https://www.prospects.ac.uk/job-profiles/data-analyst (Accessed: 21 October 2026).
Prospects (2026b) IT consultant. Available at: https://www.prospects.ac.uk/job-profiles/it-consultant (Accessed: 21 October 2026).
Prospects (2026c) Systems analyst. Available at: https://www.prospects.ac.uk/job-profiles/systems-analyst (Accessed: 21 October 2026).
Prospects (2026d) Environmental consultant. Available at: https://www.prospects.ac.uk/job-profiles/environmental-consultant (Accessed: 21 October 2026).
SFIA Foundation (n.d.a) Planning your development with SFIA. Available at: https://sfia-online.org/en/tools-and-resources/sfia-for/sfia-for-students/planning-your-development-with-sfia (Accessed: 21 October 2026).
SFIA Foundation (n.d.b) Sustainability (SUST), SFIA 9. Available at: https://sfia-online.org/en/skillcode/9/SUST (Accessed: 21 October 2026).
SFIA Foundation (n.d.c) SFIA 9 skill pages: Programming/software development (PROG), Data analytics (DAAN), Formal research (RSCH), Functional testing (TEST). Available at: https://sfia-online.org/en/skillcode/9/PROG (Accessed: 21 October 2026).
Strubell, E., Ganesh, A. and McCallum, A. (2019) ‘Energy and policy considerations for deep learning in NLP’, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, pp. 3645–3650. doi:10.18653/v1/P19-1355.
UPTO 35% DISCOUNT
