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Computing and Digital Technologies
3,000 words
Contemporary Computing and Digital Technologies: AI Agents Hackathon Reflective Report
This postgraduate reflective assessment forms part of the Contemporary Computing and Digital Technologies module and is based on experiential learning undertaken through an AI-focused hackathon. The hackathon theme, “AI Agents Unleashed – Building the Future of Automation,” requires MSc students from computing-related disciplines to collaborate on intelligent agent-based solutions capable of automating complex tasks, solving real-world problems and supporting human decision-making. The hackathon encourages students to investigate agent-based system design, intelligent automation and responsible AI development. Potential applications include autonomous cybersecurity monitoring, multi-agent systems for information gathering and decision support, automated data pipelines, intelligent software-development assistants, and conversational systems such as virtual tutors or career coaches. Students may use code-based approaches or platforms such as Flowise, Microsoft Power Automate and Make.com, while more advanced implementations can use technologies including LangChain, AutoGen, Python-based agent SDKs, APIs and large language models. The 3,000-word Individual Reflective Report, worth 70% of the module assessment, evaluates the student's learning and professional development arising from these experiential activities. The first component is a 2,000-word Portfolio of Evidence, requiring evidence-based reflection on participation in the hackathon. Students should evaluate their leadership and teamwork competencies using concrete evidence such as screenshots, code commits and feedback while identifying key lessons for personal and professional development. They must also consider how the experience applies to future research, career development or professional practice. The remaining 1,000 words comprise a Critical Self-Reflection examining the student's personal contribution and achievement of learning-contract goals. Students are expected to critically consider challenges encountered, how those challenges were addressed, lessons learned and their development as effective collaborative team members. Overall, the assessment integrates technical experimentation, reflective practice, teamwork, leadership, professional development and responsible use of emerging AI technologies, supported by a structured portfolio of evidence
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Technical Evaluation and Professional Reflection on a Cloud-Based Machine Learning Fraud Detection Solution
This individual technical evaluative report forms the reflective and critical component of the Machine Learning on Cloud module. It builds directly upon a group project involving the development of a machine learning solution for financial fraud detection. The report requires each student to critically evaluate the technical decisions made within the group solution while also reflecting on their individual contribution, teamwork experience and professional development. The first component, Technical Evaluation, accounts for 50% of the individual assessment and has a suggested allocation of approximately 1,000 words. Students critically examine the group’s machine learning solution, including the approaches adopted for data preprocessing, model selection and evaluation. They are expected to discuss the suitability of the chosen techniques and metrics, identify limitations and explain how individual technical decisions affected the final performance and outcome of the fraud-detection solution. The second component, Reflection and Professional Issues, also accounts for 50% and has a suggested allocation of approximately 1,000 words. Students reflect critically on their personal contribution to the project and their experience of working within a team. The discussion addresses challenges encountered, lessons learned and how the experience contributed to technical and professional development. Professional and ethical considerations form an important part of the reflection. Relevant issues include algorithmic bias, fairness, sustainability and data privacy, particularly in relation to machine learning applications within financial services and cloud environments. Students are also required to attach their Seminar Activity Tracker as an appendix, providing evidence of weekly participation and knowledge development. The assessment therefore combines technical critique with reflective practice, requiring students to demonstrate that they understand not only how a machine learning solution was developed, but also why specific technical decisions were made, their consequences, the limitations of the resulting system and the wider ethical and professional implications of deploying AI on cloud infrastructure. Overview word count: approximately 315 words. The brief also states that AI may assist with areas such as grammar, structure, organisation of ideas and suggestions, but the main content, analysis and conclusions must be the student's own work, and AI use must be declared.
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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
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People Practice / Human Resource Management
2,000 words
Core Behaviours for People Professionals: Ethics, Professional Values and CPD
This CIPD Level 3 assessment focuses on the core behaviours expected of people professionals, with particular emphasis on ethical practice, professional values, inclusive behaviour and continuing professional development. The unit considers behaviours that people professionals should apply consistently, including when working in unfamiliar or challenging situations, to support ethical, inclusive and professionally responsible workplaces. The assessment is structured around a job-application scenario for an HR/L&D Assistant position at Nuvascare Ltd, a home-based care organisation employing more than 200 care professionals. Nuvascare emphasises honesty, kindness, openness, teamwork, learning and ethical professional behaviour, and candidates are therefore required to demonstrate how their own experience aligns with these expectations. Learners respond to five areas of assessment. They must explain ethical principles and professional values and provide an example demonstrating how a personal or professional value has influenced behaviour. They must also provide examples of complying with relevant regulation and law in a professional or educational context. The assessment additionally examines behaviours associated with effective teamwork, including contributing views, clarifying problems and working collaboratively with others. Learners then explain how they keep their professional knowledge current and identify methods used to follow developments within the people profession. The final component requires evidence of active continuing professional development (CPD). Learners provide a CPD record containing at least two recent development activities and reflect on how these activities affected their knowledge, capability or professional practice. Written responses to Questions 1–4 should total approximately 2,000 words ±10%. The CPD record is submitted as additional evidence and is excluded from the word count.
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Computing / Artificial Intelligence / Digital Transformation / Management Consultancy
7,000 words
AI Readiness and Programme Adoption Strategy for the NewFutures: AI Programme at Northumbria University London
This postgraduate consultancy project focuses on developing an AI readiness, skills-development and programme-adoption strategy for students and recent alumni at Northumbria University London. The project supports the university’s participation in NewFutures: AI, a funded AI skills and career-readiness programme offering a four-week online course covering responsible AI foundations and specialist pathways in Marketing and Communications, Finance and Accounting, Business Operations and Logistics, Administration, and ICT and Technical Support. The central consultancy challenge is to understand the AI literacy, confidence, readiness and training needs of Northumbria University London students and alumni and translate that evidence into a practical implementation and outreach strategy. The client aims to reach approximately 12,000 students and recent alumni and support a target of 6,000 LMS registrations during the 2026–2027 programme period. The project requires primary and secondary research into AI readiness, demand for different AI-skilling pathways and barriers to participation such as awareness, time, perceived value and accessibility. The consultancy team is also expected to benchmark comparable initiatives and use research evidence to develop recommendations appropriate to different academic disciplines and student and alumni groups. The implementation component focuses on designing an evidence-based outreach and adoption campaign, including appropriate communication channels, messaging, timing, incentives, faculty engagement and stakeholder participation. Recommended channels may include email campaigns, newsletters, social media, student services, events, learning platforms and alumni communications. The project also requires an implementation timeline and indicative budget for the 2026–2027 programme period. The wider assessment develops professional consultancy capability through business and requirements analysis, research methodology, ethical research practice, practical implementation, testing and strategic recommendations. The project charter additionally establishes milestones for research design, data collection, analysis, report development, review and presentation, together with defined responsibilities for project management, data analysis, AI expertise and stakeholder communication. The individual component complements the consultancy work through critical reflection on personal contribution, skills development, decision-making, problem-solving, communication, collaboration, technical capability, innovation and continuous professional development.
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Engineering and Professional Practice
3,000 words
Internship Reflective Report and Poster Presentation — Professional Practice in an Industry Placement
An internship module assessment asks students to convert a period of workplace experience into evidenced academic reflection. It is not a report on what the host organisation does; it is an account of what changed in the student's own understanding of their discipline as a result of working outside the taught programme. The written component typically runs to around three thousand words and is built in weighted sections. An opening section covers how the placement was secured — the reasoning behind the choice, the search and application process, and what that process itself taught the student. The bulk of the marks then sit on the reflection proper: what the student actually did, how existing disciplinary knowledge held up when applied in an unfamiliar setting, and where gaps appeared. A final section addresses personal and professional development, supported by specific examples rather than general claims of having "grown in confidence." The second component is a short poster presentation summarising the placement and its learning, delivered live. Posters are assessed on professional presentation, clarity, correct referencing and balance — students commonly over-fill them with text that a viewer cannot read at presentation distance. Formatting requirements on this type of assessment are unusually prescriptive and carry marks: a cover page with name, student ID, tutor name and declared word count; a table of contents; page numbers; captions on every figure and table; specified font, size and line spacing; numbered headings; and a strict file-naming convention. Marks are lost here for no reason other than inattention. Note also that in-text citations and quotations usually count toward the word limit even when tables and references do not, and markers may simply stop reading once the limit is exceeded by more than the allowed margin. The defining challenge of reflective assessment at this level is criticality. Rubrics consistently distinguish description from evaluation: recounting tasks performed scores at the lower bands, while analysing why something was difficult, what it revealed about a gap in preparation, and what will be done differently scores at the top. Reflective frameworks give this structure, but they must organise genuine experience rather than substitute for it. This assessment is inherently personal — the content derives entirely from the student's own placement. Our support is therefore confined to guidance: explaining what distinguishes descriptive from critical reflection with worked examples, showing how a reflective framework structures a section, advising on poster layout and information density, checking formatting and referencing against the specification, and reviewing a student's own completed draft against the published rubric.
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Professional Development & Employability
2,100 words
Professional Development & Planning Portfolio — SWOT, Career Plan, UK CV & Interview Reflection
A Professional Development and Planning portfolio is a Level 7 employability assessment that asks postgraduate students to evidence their own professional growth rather than analyse an external case. It typically integrates three connected components into a single submission: a critical self-analysis with a forward-looking career plan, a set of job-application documents, and a reflective account of a practical employability exercise such as a recorded mock interview. The first component combines a SWOT analysis with a structured career development plan. Students are expected to move beyond listing traits, and instead show how each strength, weakness, opportunity and threat actually shapes their short- and long-term trajectory. Established reflective frameworks — Gibbs, Kolb, or comparable employability models — are commonly used to give the self-analysis theoretical grounding. The plan that follows usually requires a career vision, two or three SMART goals derived directly from the SWOT, an action plan with resources and timelines, and a strategy for reviewing the plan over time. Marks are lost most often where the components sit side by side without integration: a SWOT that never feeds the goals, or goals that are specific in form but generic in substance. The second component is a UK-format CV and a targeted cover letter for a real advertised role. UK conventions matter here — no photograph, no date of birth, no full address — alongside a concise personal statement, achievement-focused bullet points rather than duty lists, and visible evidence of research into the employer's values and strategic direction. The cover letter is expected to argue for candidacy rather than restate the CV, and is normally capped at one page. The third component is a reflection on a recorded practice interview, written against the platform's own scored feedback on speech rate, filler words, self-positioning and answer structure. Because this reflection must respond to individual results, it is inherently personal and cannot be written generically. Our support on this type of assessment is structured around guidance rather than production. Typical areas of help include: explaining what distinguishes a descriptive SWOT from an analytical one, showing how SMART goals should trace back to identified development needs, reviewing CV and cover letter formatting against UK employer expectations, clarifying how reflective models are applied at postgraduate level, and checking a completed draft against the assessment's own marking criteria before submission.
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