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Digital Marketing / Web Design and User Experience
800 words
Digital Marketing Portfolio Website: User Experience, SEO and Professional Digital Branding
This digital marketing assessment requires students to create and launch a fully functional personal portfolio website designed to showcase their professional capabilities, creativity and readiness for employment or client-facing work. The website operates as a digital business card and professional showcase, demonstrating the application of digital marketing strategy, user-centred design, storytelling, usability and digital content creation. Digital Portfolio Website The website must contain a homepage, contact page and portfolio or projects section. The homepage introduces the student and provides an engaging overview of the portfolio, while the contact page includes appropriate professional communication channels such as email or LinkedIn. The projects section must feature approximately three to five digital marketing work samples, which may include mock-ups, content or other digital artefacts. The overall site should demonstrate effective structure, aesthetics, usability and intuitive navigation. Digital Portfolio Website Students must also complete a prescribed LinkedIn Learning course on Wix or WordPress and submit the associated completion certificate. This supports the practical website-development component and demonstrates engagement with contemporary digital-design tools and platforms. Digital Portfolio Website A major component is the reflective rationale, which may be submitted as an 800-word written piece or a four-to-six-minute video commentary. It explains and justifies the design decisions made, the SEO strategies implemented and changes introduced to enhance the user experience. The rationale must be supported by credible sources and demonstrate alignment between theoretical principles and practical design choices. Digital Portfolio Website SEO is explicitly assessed through the effective use of metadata, keywords and image alt text, while design performance also considers originality, calls to action, responsive behaviour, mobile usability and effective use of Wix or WordPress features. The marking structure allocates 20% to website structure and usability, 15% to design and creativity, 15% to SEO, 40% to the reflective rationale, and 10% collectively to the LinkedIn Learning certificate and cover page. Digital Portfolio Website Digital Portfolio Website Overall, the assessment integrates digital marketing, personal branding, web design, UX, SEO, content creation and reflective professional practice. Overview word count: approximately 345 words. AI-use note: this assessment is under Category 1 – Authorised use of AI. AI may be used within platforms such as Wix or WordPress to help create the portfolio, but AI must not be used to write the reflective rationale, which must be composed entirely by the student. Digital Portfolio Website
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500 words
Strategic Digital Marketing Campaign Planning Using the RACE Framework: Cambridge Museum of Technology
This Digital Leadership and Disruptive Innovation assessment requires students to act as digital marketing consultants for the Cambridge Museum of Technology and develop a strategic social media campaign focused on the Reach and Act stages of the RACE framework. The campaign is designed to increase awareness of the museum and encourage meaningful interaction using paid and owned content across Facebook and Instagram. Assessment Brief for CW1 (1) The first deliverable is a two-page landscape campaign poster created in Canva. Page 1 focuses on Reach and should present a clear awareness objective, one Facebook paid advertisement mock-up, and one Instagram owned post. Page 2 focuses on Act and should include an engagement objective, one Instagram paid advertisement and one Facebook owned post. Each mock-up must be clearly labelled by platform, RACE phase and media type. Assessment Brief for CW1 (1) Assessment Brief for CW1 (1) The poster is expected to function as a strategic visual board, rather than a website or social feed simulation. It should demonstrate clear visual hierarchy, logical layout, consistent headings, spacing and alignment, and communicate how the Reach and Act strategies connect. The target audience persona should not appear on the poster itself. Assessment Brief for CW1 (1) The second deliverable is a maximum 500-word written rationale. This should define a relevant target audience persona using demographic, psychographic and behavioural characteristics, and explain needs, motivations and online behaviour. The persona should be visually designed and embedded in the written document. Assessment Brief for CW1 (1) The rationale must also justify the Reach content strategy, including platform choice, content format, targeting and budget considerations, and explain the Act engagement strategy, including tactics such as event promotion, interactive posts and comment prompts. Students should also explain how layout, visual structure and calls-to-action support user experience and engagement. Assessment Brief for CW1 (1) The assessment requires application of relevant digital marketing theories and frameworks and at least five credible academic or industry sources, using Cite Them Right Harvard referencing. Suitable evidence can include peer-reviewed journal articles and recognised sources such as Mintel, Statista and industry reports. Assessment Brief for CW1 (1) The marking scheme gives 30% to Reach, 30% to Act, 20% to the written rationale, and 20% to poster design, communication and professional presentation. Assessment Brief for CW1 (1) Assessment Brief for CW1 (1) Overall, the assessment integrates RACE-based campaign planning, paid and owned social media, audience segmentation, persona development, targeting, budget awareness, content strategy, UX principles and professional visual communication within a real cultural-organisation context. Important: the brief explicitly states that the applicable AI category is Category 2 – Proofreading only permitted, meaning AI may be used for proofreading but not for creating assessment content. Assessment Brief for CW1 (1)
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Artificial Intelligence
Developing an Intelligent Chatbot and Expert System for UK Train Services
This postgraduate Advanced Artificial Intelligence group project requires students to design, implement, evaluate and demonstrate an intelligent conversational system for a UK train operating company. The chatbot combines conversational AI, expert-system concepts, predictive modelling and knowledge-based reasoning to support both railway passengers and operational staff. The coursework is worth 70% of the module and is designed to develop practical experience in applying modern AI techniques to realistic service and operational problems. The first task requires the chatbot to interact with passengers, collect journey requirements such as origin, destination, date and travel time, and identify the cheapest available train ticket. Appropriate railway ticket data sources or APIs may be used, with the selected ticket presented together with access to the relevant booking service. The second task extends the system to improve customer service through train-delay prediction. The chatbot gathers information about a passenger's current train, location, delay and destination before passing these data to one or more predictive models. Students process historical railway-performance data, train and compare suitable machine-learning models, evaluate their accuracy and integrate an appropriate model into the chatbot. The third task introduces an expert system for railway contingencies. Students extract rules and operational knowledge from provided contingency and station-disruption documents and construct a knowledge base capable of advising railway staff during events such as partial or complete line blockages. The system should gather details such as event type, location, time and severity, then provide relevant operational guidance, diversion information, alternative services and passenger advice. The overall architecture may include a user interface, NLP/NLU component, knowledge base, reasoning engine, predictive model, database and optional knowledge-acquisition component. Particular emphasis is placed on context-aware dialogue, reliable reasoning, appropriate fallback responses and effective user experience. Assessment outputs include the working chatbot, source code, a live presentation and demonstration, a detailed group technical report, and an individual contribution report. Overview word count: approximately 375 words. AI-use note: pre-trained LLMs may be used as an engine within the system, but they must not be used to generate coursework code. Any use of an LLM within the solution must be clearly justified and explained in the group report.
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Systems Analysis and Design
Systems Analysis and Design with UML – Individual Systems Design Assignment
This individual assignment forms Part 2 of the Systems Analysis and Design with UML assessment and contributes 50% of the overall module weighting. The uploaded guidance indicates that this component should continue directly from work completed in Part 1, meaning that the analysis and design developed previously should provide the foundation for the second-stage submission. The assignment is centred on the principles and practices of systems analysis and design using the Unified Modeling Language (UML). It is intended to demonstrate the application of structured analytical thinking and modelling techniques to the development or refinement of an information-system solution. Because Part 2 follows an earlier assessment component, continuity between the original problem definition, requirements and subsequent design work is likely to be an important aspect of the submission. Relevant areas may include requirements representation, system modelling, process and interaction analysis, object-oriented design and the use of appropriate UML artefacts to communicate system structure and behaviour. The work should therefore demonstrate how analytical findings are translated into coherent design decisions while maintaining alignment with the system requirements established during the earlier project stage. The assessment is individual and therefore should demonstrate independent understanding of the selected system, its requirements and the rationale underlying the proposed design. The uploaded summary confirms that this component is worth 50% of the module and has a submission deadline of 15 May 2026 at 17:00 GMT. Note: the actual detailed Part 2 brief is not contained in this uploaded summary, so specific UML diagrams, required sections, word count and marking criteria cannot be confirmed from this document alone. Entry 2 — Intelligent User Interfaces Part 1 Field Content to enter Title Smart Study Student Assistant: Intelligent User Interface Prototype University Not specified in the uploaded guideline Subject Intelligent User Interfaces / Human-Computer Interaction Module name Intelligent User Interfaces Academic level Not specified Academic year Not specified Assignment type Presentations and Academic Posters Reference style Not specified Word count Not applicable – maximum 10-minute pre-recorded prototype presentation Amount (₹) Enter your internal project amount Key topics Intelligent User Interfaces, Human-Computer Interaction, Smart Study Assistant, Student Productivity, Adaptive Interfaces, Personalised Learning, Study Planning, Question Generation, Low-Fidelity Prototyping, User-Centred Design, User Behaviour, Learning Progress, Educational Technology, UX Design, Personalisation Assignment overview This project prototype forms Part 1 of Intelligent User Interfaces and contributes 60% of the final module mark. The assessment requires a project prototype supported by a pre-recorded presentation of no more than ten minutes. The selected project concept is a Smart Study Student Assistant, positioned within education and student-productivity technology. The proposed system is an adaptive study-planning and note-based question-generation interface. Its purpose is to help students organise academic work by combining uploaded study notes, deadlines and information about user behaviour to generate personalised study plans and learning questions. The project addresses common problems including scattered study materials, missed deadlines, unclear schedules and the lack of personalised guidance on how and when students should study. The guidance specifically states that the solution should not be unnecessarily advanced. Instead, the design should focus on how the interface will be useful to different types of users. Low-fidelity material and supporting notes are also required, indicating that the project should demonstrate user-centred design thinking rather than focusing solely on technical sophistication. The intended goal is to provide personalised study plans, questions and guidance based on the student's notes and deadlines, supporting more consistent study habits and improved organisation. Tutor feedback describes the concept as relevant and well motivated but identifies one important weakness: the system's intelligent behaviour is currently under-specified. The tutor specifically recommends clarifying how study plans and question generation adapt according to user behaviour or learning progress. Accordingly, the prototype should communicate both the interface design and the adaptive logic that makes the system genuinely intelligent and user-centred. Entry 3 — Intelligent User Interfaces Part 2 Field Content to enter Title Intelligent User Interfaces – Individual Essay University Not specified in the uploaded guideline Subject Intelligent User Interfaces / Human-Computer Interaction Module name Intelligent User Interfaces Academic level Not specified Academic year Not specified Assignment type MS Essays Reference style Not specified Word count Not specified Amount (₹) Enter your internal project amount Key topics Intelligent User Interfaces, Human-Computer Interaction, Adaptive Interfaces, User-Centred Design, Intelligent Systems, Personalisation, User Experience, Educational Technology, Interaction Design, AI Interfaces Assignment overview This individual essay forms Part 2 of the Intelligent User Interfaces module and contributes 40% of the final module mark. The uploaded guidance identifies the submission deadline as 21 May 2026 at 16:00. The assessment belongs to the same Intelligent User Interfaces module as the Part 1 prototype project. The broader module context therefore concerns the design and evaluation of interfaces that use intelligent or adaptive behaviour to improve interaction between users and digital systems. Relevant considerations may include personalisation, adaptive interaction, usability, human-computer interaction, user behaviour and the responsible design of intelligent systems. However, the uploaded summary does not contain the actual Part 2 assessment brief, essay question, required word count, marking criteria or prescribed topic. It only confirms that the assessment is an individual essay worth 40% of the final mark. For that reason, the Reference Library overview should remain general rather than claiming that Part 2 specifically assesses the Smart Study Student Assistant. If the Part 2 essay is intended to continue or evaluate the Part 1 prototype, that relationship should only be added once the actual Part 2 assessment brief confirms it. The current document explicitly provides the Smart Study Student Assistant instructions under Subject 2, while no corresponding topic instructions are shown for Subject 3. So for Entry 3, I would keep the title and overview generic until the actual KV7005 Part 2 brief is uploaded.
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Digital Marketing and Analytics
2,974 words
Digital Marketing Analytics Consultancy Report — Accessibility, Social Listening and Web Analytics for an E-Commerce Storefron
This assessment places the student in a consultancy role advising a global brand's e-commerce storefront on its digital marketing strategy. It is written as a business report rather than an academic essay, in the third person, with appendices used only for supporting material that the main text explicitly refers the reader to. The work is built from four analytical layers, and the mark weighting tells students where the effort belongs. The first is an accessibility and user experience evaluation of the client site benchmarked against two self-selected industry competitors — the selection itself must be justified, and the analysis must cover both technical and content dimensions using recognised evaluation tools rather than impressionistic browsing. The second layer, carrying the smallest weight, is social listening: which networks the brand and its competitors are active on, what engagement looks like, which content types perform, and who the influential voices are. This is paired with qualitative sentiment or content analysis of actual social comments, on the premise that quantitative engagement metrics describe reach but not attitude. The third and heaviest layer is quantitative analysis using a web analytics platform, typically via a demonstration account that provides real traffic data. Students query the data themselves and extract performance insights. Comparison across time periods is what separates competent work from strong work here: a single-period snapshot describes, while period-over-period comparison explains. The fourth layer, weighted equally with the analytics, asks the student to synthesise everything into strategic recommendations for the coming year, covering areas such as customer segments, user behaviour, landing and exit page performance, search ranking positions, advertising budget allocation, marketing channels and e-commerce performance. This section is where most marks are lost. Recommendations that do not trace back to a specific finding from the preceding analysis read as generic digital marketing advice, and rubrics at this level penalise exactly that. Presentation requirements are prescriptive — specified font, size, line spacing and justified margins — and the report is expected to be concise despite the breadth of analysis, which makes ruthless selection of evidence part of the task. A draft submission point for similarity checking is usually provided separately from the marked final submission. Assessments of this type commonly require a signed declaration itemising any AI tool use, with an explicit confirmation that AI was not used to generate sentences, paragraphs or sections. Our support on assessments of this type is guidance-based. Typical areas of help include: explaining how accessibility evaluation tools are used and what their output actually shows, clarifying the difference between reporting analytics figures and interpreting them, showing how a recommendation should be traced to a specific finding, advising on report structure and appendix discipline, checking APA consistency, and reviewing a student's own draft against the published rubric.
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