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Artificial Intelligence / International Business
3,000 words
AI Innovation Consultancy: Evaluating Artificial Intelligence Solutions for Business Problems
This individual consultancy assessment requires students to act as an AI Innovation Consultant and evaluate how artificial intelligence could address a significant real-world business problem. Students select one industry—such as healthcare, retail, FinTech, manufacturing or agriculture—and concentrate on a single clearly defined organisational challenge rather than comparing multiple sectors. Potential issues include long waiting times, high operating costs, fraud and risk, poor customer experience or inefficient supply chains. The report develops a practical AI solution by identifying suitable technologies such as machine learning, natural language processing or computer vision and explaining how they would operate within the chosen organisational context. Students are not required to build an AI system; instead, the emphasis is on demonstrating business-level technical understanding, critical thinking and the ability to assess whether the proposed technology can realistically integrate with existing organisational processes. The analysis considers the capabilities and limitations of AI, technical feasibility, integration requirements and the skills or organisational capabilities required for implementation. Students must also critically examine ethical, legal and social implications, including issues such as algorithmic bias, transparency, accountability, privacy and regulatory obligations such as UK GDPR. Appropriate risk-mitigation measures should be proposed. A substantial element of the report develops the business case for AI adoption. Students evaluate implementation costs and expected benefits, estimate return on investment, identify assumptions and commercial risks, and assess the overall strategic value of the solution to the organisation. The report concludes with clear recommendations, implementation priorities and a final judgement on whether the proposed AI initiative is feasible and worthwhile. The assessment places strong emphasis on critical analysis, technical understanding, business acumen and professional communication. Students are expected to support arguments with credible academic, industry and government evidence and include at least two professional visualisations such as frameworks, diagrams or tables. Harvard referencing is required throughout. Overview word count: approximately 330 words. The brief also allows authorised use of generative AI for idea generation, drafting/structuring and proofreading, provided the student verifies accuracy, references appropriately and submits the required GenAI declaration.
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Digital Data Acquisition, Recovery and Analysis
1,500 words
Digital Data Acquisition, Recovery and Analysis – Autonomous Vehicle Forensics
This individual coursework for the Digital Data Acquisition, Recovery and Analysis module requires students to produce a 1,500-word technical research paper critically investigating autonomous vehicle (AV) forensics. The assignment focuses on the challenges, methodologies and tools involved in extracting, preserving, analysing and interpreting digital evidence from autonomous vehicles and their associated systems. The work specifically considers how forensic evidence can be used to reconstruct events and establish accountability following accidents, security breaches or system malfunctions. The assignment examines the increasing importance of digital evidence generated by intelligent transportation systems and smart vehicles. Students are expected to consider data produced by different sources within an autonomous vehicle ecosystem, including LiDAR, radar and GPS sensors, vehicle control units and connected infrastructure. The report should explore how these different forms of information can be collected and forensically preserved while maintaining their evidential value for subsequent investigation and analysis. The coursework requires a detailed technical analysis rather than a general overview of autonomous vehicles or digital forensics. Students are expected to engage with academic literature, industry frameworks and technical examples, while providing critical insights into the subject. The report should analyse the practical challenges associated with AV forensics and critically examine the tools and methodologies that can be applied to obtain and interpret evidence from autonomous vehicle environments. Legal, ethical and privacy considerations form an important part of the assignment. The report should examine issues surrounding the use of autonomous vehicle evidence, including legal responsibility, regulatory considerations, privacy implications and ethical challenges associated with collecting and analysing potentially sensitive vehicle and user data. These considerations should be connected to the wider forensic investigation process and the reliability and admissibility of digital evidence. The required report should follow an academic research-paper structure, including a cover page, abstract, keywords, table of contents, clearly organised sections and subsections, references and an appendix where required. The brief requires APA referencing and a reference list at the end of the paper. Students are expected to use their own words and critically analyse the literature rather than simply summarising existing research. The assessment evaluates five equally weighted areas: structure and presentation with supporting references; balance, objectivity, critical evaluation and original insight; identification of AV-forensics challenges and quality of analysis; analysis of tools and methodologies used in AV forensics; and understanding of AV forensics together with its legal, professional and ethical considerations. Each area contributes 20% to the assessment.
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Entrepreneurship / Entrepreneurial Marketing
1,200 words
Reflective Analysis of Entrepreneurial Capabilities and Development: The GymBuddy Venture
This reflective Entrepreneurial Marketing assessment evaluates the development of the student's entrepreneurial capabilities through the GymBuddy business concept, a proposed fitness application designed to improve motivation and accountability among beginner gym users. The reflection examines how entrepreneurial learning, customer research and creativity frameworks changed the student's original approach from developing a highly complex product toward a more focused and testable business proposition. Entrepreneurial_Marketing_Refle… A central theme of the report is entrepreneurial self-assessment. Opportunity recognition is identified as an important strength, supported by interviews with beginner gym users and competitor analysis involving platforms such as Strava, MyFitnessPal and Nike Training Club. At the same time, the reflection identifies areas requiring development, particularly financial understanding, customer acquisition cost, lifetime value, conversion-rate analysis and negotiation capability. These strengths and weaknesses are used to establish priorities for future entrepreneurial development. Entrepreneurial_Marketing_Refle… The report also evaluates the evolution of the GymBuddy product using SCAMPER, Design Thinking and Lean Startup principles. Rather than pursuing a feature-heavy application, customer feedback leads to greater emphasis on simplicity, peer accountability, social competition and iterative validation. SCAMPER is applied to eliminate unnecessary features, substitute complex recommendations with simpler motivational mechanisms, combine useful functionality and adapt gamification to the needs of beginner users. Entrepreneurial_Marketing_Refle… Another major area of reflection concerns teams, networks and collaborative innovation. The student considers how group creativity exercises challenged the assumption that a venture should be developed independently and highlights the importance of mentors, developers, industry professionals and potential investors. The report links entrepreneurial networks with access to resources, opportunity identification, legitimacy and emotional support and proposes deliberate networking activities as part of future venture development. Entrepreneurial_Marketing_Refle… The visual development framework on page 3 summarises the entrepreneurial journey in five stages: GymBuddy concept and market research, capability assessment, product development and validation, teams and networks, and a time-based action plan. The reflection is structured using the Gibbs Reflective Cycle, linking experience with evaluation, analysis and future action. Entrepreneurial_Marketing_Refle… Overall, the assessment demonstrates reflective learning through the application of entrepreneurial theory to a practical start-up concept. It shows a shift from perfectionism toward rapid validation, customer-centred development, collaborative working and concrete action planning, while identifying financial management, negotiation and network development as important areas for continued improvement. Entrepreneurial_Marketing_Refle… Note: this file is a completed reflective submission rather than the original university assessment guideline, so the university, academic level and academic year should remain Not specified unless you also upload the official brief.
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Business Ethics / Corporate Social Responsibility
2,500 words
Success Through Business Ethics (BS846) — Individual Report: Ethical Failure in an FMCG Brand, Moral Philosophy, Decision-Making and Leadership Response
This Level 7 individual report examines a real fast-moving consumer goods brand that was found to have engaged in unethical business practices between 2000 and 2023, and works through why the failure happened rather than simply recounting what occurred. The brand is selected on the basis that sufficient documented evidence exists about both the company and its external environment — regulatory findings, investigative journalism, financial disclosures and NGO reporting — so that every analytical claim can be supported rather than assumed. The introduction establishes what business ethics means as an academic field, drawing on recognised sources, before introducing the chosen brand and setting out concisely what the unethical practices were, when they came to light and who was harmed. The first and largest analytical section applies three moral philosophical bases from the module to the case. Each is used as a lens rather than described in isolation: the utilitarian calculation the company appears to have made and where its accounting of harm was deficient, the duty-based obligations to consumers, workers or communities that were breached regardless of outcome, and the character and organisational culture questions that virtue ethics raises about how such decisions became normal internally. The evaluation is genuinely critical — it identifies where the company's conduct could be partially defended under one framework while failing badly under another, which is where the analytical marks sit. The second section takes one of the two theoretical approaches offered and applies it in depth. Where ethical decision-making is chosen, the report traces the sequence of judgements that produced the outcome, examining awareness, intent, organisational pressure and the moral intensity of the issue. Where social accounting is chosen, it assesses what the company disclosed about its social and environmental impact against what was actually occurring, and what that gap reveals about the purpose its reporting served. The third section evaluates leadership. It characterises the prevailing leadership style from the evidence and then focuses on the response once the practices were exposed — whether leaders denied, deflected, settled quietly or accepted accountability — supported by tables, figures and quoted material showing the consequences in share price, revenue, regulatory penalties and documented social or environmental harm. Three concrete recommendations follow, each derived directly from a failure identified in the preceding analysis and justified specifically for this brand rather than offered as generic good practice, with a short conclusion drawing the sections together. Harvard referencing is applied in text and in an alphabetised reference list.
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Digital Forensics / Cybersecurity
2,000 words
AI-Augmented Digital Forensics Workflow Audit: Feasibility and Risk Assessment of ForensiScan AI
This digital forensics assessment examines the feasibility, reliability and legal risks associated with introducing AI-assisted analysis into a conventional forensic investigation workflow. Students act as a Lead Forensic Consultant assessing a proposed black-box system called ForensiScan AI, which claims to automatically classify illicit images and identify suspicious intent within encrypted messaging applications using Large Language Models. The central objective is to determine whether the efficiency benefits of AI can be achieved without compromising evidential integrity, transparency or legal defensibility. The report maps the proposed AI system across the four stages of the NIST forensic process: Collection, Examination, Analysis and Reporting. For each phase, students identify the data entering and leaving the AI system and determine whether the technology should be used for preliminary triage or as part of final forensic analysis. A major component concerns verification and validation. Because AI systems may hallucinate or misclassify evidence, students must design a ground-truth protocol involving human verification, statistical sampling and known datasets. The assessment also investigates whether AI-generated results can be reproduced reliably when identical evidence is processed again. The report further addresses chain of custody and data integrity, particularly whether AI processing could alter timestamps, metadata or other forensic artefacts. Ethical and legal analysis covers potential model bias, language and contextual limitations, and whether AI-generated outputs could satisfy the requirements of the Daubert Test for expert evidence. Overall, the assessment combines digital-forensic architecture, AI governance, evidential integrity, model validation, legal admissibility and professional accountability. The grading criteria place particular emphasis on forensic soundness, protection against evidence alteration, critical analysis of AI limitations such as hallucination and non-determinism, and professional technical communication. Overview word count: approximately 340 words. AI-use note: the brief permits AI only for limited assistance such as brainstorming risks, structural feedback and grammar refinement. It explicitly prohibits full report generation, unverified forensic claims and using AI to substitute for the student's own final recommendation or verification protocol. Any AI use requires an appendix containing the tool, exact prompts and a human verification log.
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