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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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Cyber Security / Penetration Testing
2,400 words
Grey-Box Penetration Testing: Vulnerability Assessment, Exploitation and Mitigation
This technical cyber-security project presents an authorised grey-box penetration test conducted within a controlled virtual laboratory environment. The objective is to assess the security posture of a deliberately vulnerable target system, identify weaknesses in exposed network services, demonstrate how those weaknesses could be exploited, evaluate their security and organisational impact, and recommend appropriate mitigation measures. The assessment follows a practical penetration-testing workflow supported by technical evidence, screenshots, activity records and academic research. The project begins with laboratory configuration, network discovery, service enumeration and vulnerability analysis. Tools including Kali Linux, Metasploitable, VMware, Nmap, Netcat and Metasploit are used across the testing lifecycle. Identified services are mapped to known vulnerabilities before controlled exploitation is undertaken and the resulting access is documented. The activity log records the progression from environment setup and network scanning through vulnerability identification, exploitation, evidence collection and final reporting. Five principal attack vectors are examined. These include the vsftpd 2.3.4 FTP backdoor, Samba username-map-script exploitation, an UnrealIRCd backdoor, insecure Java Remote Method Invocation and a misconfigured DistCC service. The practical demonstrations show how vulnerable or incorrectly configured services can permit unauthorised command execution and, in several cases, privileged shell access. For each vulnerability, the report explains the weakness, exploitation process, observed result, security impact and proposed mitigation. Recommended controls include patching or upgrading obsolete services, disabling unnecessary services, implementing firewall restrictions, strengthening authentication and input validation, restricting access to authorised systems, applying least privilege and monitoring suspicious activity. The project also incorporates group management and reflective practice. Team members perform specialised roles covering laboratory configuration, reconnaissance, vulnerability analysis, exploitation and documentation. Individual reflection considers technical performance, teamwork, evidence management and future skills development, demonstrating how structured collaboration contributes to an effective penetration-testing engagement. Important: unlike the earlier assignment briefs, these uploads appear to be completed student/project materials rather than the official 7COM1068 assessment brief. Therefore I would not invent the university, academic level or academic year. If you upload the actual 7COM1068 assignment guideline, I can fill those fields exactly.
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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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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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