Filter by keyword, subject, or both. Updates live as new model answers are added to our portal.
Management / Digital Transformation and Leadership
1,500 words
Leading Through Digital Change: Digital Transformation Report and Future Technology Poster
This Masters-level assessment for the Leading Through Digital Change module examines how organisations can respond strategically and effectively to rapid technological and digital transformation. Students take the role of a Digital Transformation Manager for one selected international organisation and prepare a professional Digital Transformation Report accompanied by an A4 digital poster. The purpose is to evaluate the organisation's current digital context and recommend changes that can strengthen competitive advantage and create sustainable business value. The first component requires critical evaluation and recommendation of one appropriate digital transformation strategic framework. Students may apply frameworks such as the McKinsey 4Ds, BCG Three Stages, Gartner's Six Steps or Cognizant's Four Pillars. The analysis should establish clear digital transformation objectives relevant to organisational functions such as operations, ICT and marketing, while using organisational evidence, academic research and practical examples to justify the proposed strategic direction. The second component is an academic poster evaluating two disruptive technologies or techniques expected to affect the chosen organisation, its industry, employment and the labour market over the next five years. Potential technologies include Artificial Intelligence and Machine Learning, 5G connectivity, the Internet of Things, robotics, drone delivery, blockchain, augmented reality and virtual reality. The poster should combine academic literature with real-world examples to demonstrate the likely opportunities, challenges and wider organisational implications of technological disruption. The final component focuses on digital leadership. Students analyse and recommend two suitable leadership approaches for managing and supporting digital transformation. Relevant approaches may include agile leadership, ethical-tech leadership, people-oriented leadership, hyperaware agile leadership and Goleman's leadership styles. Overall, the assessment integrates digital strategy, innovation, emerging technologies and leadership. The wider module also covers digital transformation strategies, data-driven decision-making, leadership in the digital age, artificial intelligence in contemporary business, digital risk management and planning for the future. Reference style: Harvard. Main report word limit: 1,500 words. Poster: A4 size with no specified word count.
Read Model Answer →
Management / Business Management
5,000 words
Principles of Management: AstraZeneca Management Analysis and Skills Development Report
This Masters-level Principles of Management assignment examines the management practices, organisational environment and managerial functions of AstraZeneca plc. The assessment is structured as a 5,000-word management report in which the student acts as a business consultant and critically evaluates how management theories, concepts and practices contribute to organisational performance and value creation. The work requires the application of relevant academic literature alongside case-study evidence and independent research on AstraZeneca. The first part of the assessment considers key management theories and practices. Students are expected to examine how management contributes to AstraZeneca's success, apply an appropriate dimension of the Competing Values Framework, and select an additional relevant management theory. Suggested theoretical perspectives include Scientific Management, Bureaucratic Theory, Administrative Theory, Contingency Theory and Total Quality Management, although students are encouraged to select theories most appropriate to the organisation rather than applying every available model. The assignment also evaluates AstraZeneca within the global business environment. Frameworks such as LoNG-PEST or Porter's Five Forces may be used to analyse global, national and local influences, including significant opportunities and threats. Students then critically analyse an internal organisational challenge and consider how the core management functions of planning, organising, leading and controlling can be applied to address it. Supporting analytical approaches may include Value Chain analysis, VRIO and stakeholder analysis. A further component focuses on personal and professional development. Students complete a Personal SWOT analysis and construct a Skills Development Plan linked to future career aims. This is followed by a 500-word reflective statement examining personal management competencies, including self-management, problem-solving and decision-making. An appropriate reflective framework, such as Borton, Kolb or GROW, is applied to structure the reflection. Overall, the assessment integrates management theory, organisational analysis, global business considerations, managerial decision-making and reflective professional development. It is designed to demonstrate Level 7 critical analysis and the practical application of management concepts to a contemporary multinational organisation. The wider module covers management theories, stakeholder management, global management, planning and decision-making, human resource management, leadership, operations and finance for managers
Read Model Answer →
Artificial Intelligence
2,000 words
End-to-End Applied AI Development — Comparative Machine Learning and Neural Network Modelling on a Public Dataset
This assessment runs a complete applied AI development cycle end to end: problem definition, dataset selection, preprocessing, model building, optimisation, evaluation and critical reflection. Students identify a real-world problem themselves, formulate a research question from it, and source a suitable dataset from a recognised public repository such as UCI, Kaggle, Data.gov or OpenML. Dataset choice carries more weight than students expect. It must be genuinely suitable for supervised learning, complex enough to make preprocessing and feature engineering meaningful, and — critically — structured so that a traditional machine learning approach and a deep learning approach can be sensibly compared on it. A dataset too small or too clean makes the neural network component pointless; one too large or too noisy makes the whole pipeline unfinishable within the page limit. The source must be referenced and the choice explicitly justified against the research problem. The modelling requirement is fixed: at least two supervised machine learning models, plus one artificial neural network built in a mainstream deep learning framework, all trained and tested. The comparison between them is the analytical core of the work. Reporting that the neural network scored higher is not an answer; explaining why, in terms of the data's structure and each model's inductive assumptions, is. Marks are distributed across problem framing, the traditional models, the deep learning model, evaluation and critical analysis including responsible AI considerations, and academic communication. That responsible AI component is easy to overlook and is not decorative — it asks what the model's limitations mean for anyone who might rely on it. Presentation requirements are specific. The report is page-limited rather than purely word-limited, and every plot must be described in the text while also being legible enough to communicate on its own — a common failure is dense default library output pasted in without axis labels or scale. The implementation is documented in a notebook combining markdown and code cells so the development process is visible, not just the final result, and submissions typically include the cleaned dataset alongside the code. The strongest submissions treat the notebook and the report as one argument. Weaker ones produce a working notebook and then write a report that describes it, rather than a report that uses it as evidence. Our support on assessments of this type is guidance-based. Typical areas of help include: advising on whether a candidate dataset can actually support the required model comparison, explaining how to justify preprocessing decisions, clarifying which evaluation metrics suit which problem type and why accuracy alone is often misleading, showing how to structure a critical limitations and responsible AI discussion, checking Harvard referencing, and reviewing a student's own draft against the published marking criteria.
Read Model Answer →