University of Essex Online · CertHE Computer Science · Level 4 · Tutor: Dr Anupam Mazumdar
This module served as an introduction to the academic world. It established the core foundations of research and scholarly practice that underpin the entire programme.
Conduct systematic research, including searching academic databases (Scopus, IEEE Xplore) and applying strict selection criteria for source relevance and rigour.
Read, interpret, and synthesise information from multiple peer-reviewed articles to build a coherent understanding of complex subjects.
Produce structured academic writing — from research proposals to a mini research project — with clear argumentation and conclusions.
Apply the Harvard referencing system correctly and consistently across all written work, maintaining academic conventions.
Understand and justify methodological choices, including the distinction between qualitative and quantitative research approaches.
Apply ethical principles in research, including the Data Protection Act 2018 (UK GDPR) and transparent declaration of generative AI use.
Three pieces of assessed work were produced during this module, showing a clear progression from foundational academic writing through to an independent research project.
A comparative academic essay examining how the European Union and China are approaching the regulation of Artificial Intelligence from fundamentally different philosophical positions. The essay analyses the EU AI Act's four-tier risk framework against China's state-centred regulatory model, exploring how political philosophy shapes the technological future and may give rise to two distinct AI ecosystems.
As the first piece of academic writing in the programme, this artefact established my ability to construct an argument from credible sources and to declare the use of generative AI (Google Gemini) transparently, in line with academic integrity standards.
Written expression assessed as strong, professional and engaging, with a logical flow and consistent academic tone. Research described as extensive, using credible and up-to-date academic, policy and media sources, all correctly cited and referenced. Submitted ahead of deadline.
A formal research proposal investigating how generative AI is transforming Non-Player Character (NPC) dialogue in modern video games, with attention to narrative, immersion, and ethical challenges. The proposal included a systematic literature search — narrowing 127 candidate articles down to three rigorously selected sources — and a justified choice of qualitative methodology.
This artefact demonstrated the structure of academic research: a defined research question, aim and objectives, a literature search strategy, a methodology section, and explicit consideration of research ethics under the Data Protection Act 2018.
Strong awareness of how generative AI is transforming NPC dialogue, with a relevant and specific research title. Methodology showed sound analytical reasoning in justifying the qualitative approach. Literature search described as systematic and clearly explained. Suggested improvements: narrowing the research question and enriching practical grounding with industry examples.
The culminating research project of the module, expanding the earlier proposal into a full literature-based study titled "Generative AI for Non-Player Characters: Exploring Impacts on Immersion and Ethics in Modern Games." The project examined how generative AI architectures for NPC dialogue transform player immersion and narrative agency, while creating ethical tensions in contemporary video games.
The work featured an annotated bibliography of eight carefully selected sources — expanding significantly from the three used in the proposal — spanning technical architecture (MemoryRepository, Generative Agents, VOYAGER), player experience, and AI ethics (anthropomorphism, the "mirages" of humanity). This was my strongest piece of the module, and the topic was one I chose myself out of personal interest.
Described as an exceptional and deeply insightful research project demonstrating sophisticated understanding of generative AI and its implications for immersion, narrative agency and ethical design. Application of theory to practice assessed as outstanding; analytical and evaluative skills as exceptional. Reading and referencing described as exemplary, using current and reputable sources. Presentation rated as professional and publishable standard.
Written using the 3 W's framework (What / So What / What Next), as recommended by the UoEO Department of Computing.
This module served as my introduction to the academic world. While I had previous experience with formal writing, this was my first time engaging with academic research and writing. The module introduced the core foundations of academic practice: conducting research, assimilating information, applying methodology, reading and interpreting peer-reviewed articles, writing an academic paper, revising it, and using the Harvard referencing system correctly.
The most challenging aspect for me was assimilating information from multiple articles to understand a subject that is often complex, and then constructing a coherent line of reasoning — building an argument with a clear conclusion from those sources.
This challenge is reflected in my progression across the module. My Mid-Module Research Proposal achieved 71% (Distinction), and my End-of-Module Mini Research Project achieved 75% (Distinction). The improvement came from two factors. First, the tutor's feedback helped me significantly across several areas, and I applied research practices I had already begun developing to strengthen my writing. Second — and importantly — the proposal topic (71%) was predefined, whereas the final project topic (75%) was my own choice. Writing about a subject closer to my own interest, the use of generative AI for NPC dialogue in video games, made the work more engaging and the result stronger.
A key part of professional academic practice I engaged with was the transparent declaration of AI use. Declaring my use of generative AI (Google Gemini) as a drafting and analysis aid was both a conscious decision and a requirement of the university and the wider academic world. This taught me an important principle of academic integrity in the age of AI.
The most valuable outcomes of this module were understanding the academic world and academic writing, and learning to use referencing correctly. Above all, I learned how to research properly — a skill that is fundamental to academic work and one I will carry into every subsequent module of this programme. Going forward, I intend to keep refining my ability to synthesise complex sources into clear, well-argued conclusions.
An assessment of the skills gained or enhanced during this module, with evidence and a forward-looking action plan.
| Skill | Before | After | Evidence | Action Plan |
|---|---|---|---|---|
| Academic Research | None | Proficient | Systematic literature search (127 → 3 → 8 sources) across Scopus and IEEE Xplore | Apply same rigour to technical research in later modules |
| Academic Writing | Formal writing only | Competent | Three assessed pieces; two Distinctions (71%, 75%) | Continue improving synthesis of complex sources into clear arguments |
| Harvard Referencing | None | Proficient | Referencing assessed as "exemplary" in final project feedback | Maintain consistency across all future submissions |
| Source Synthesis | None | Developing | Annotated bibliography of 8 sources with thematic synthesis | Practise building argument structures from larger source sets |
| Research Methodology | None | Foundational | Justified qualitative methodology in research proposal | Explore quantitative and mixed methods in future research |
| Research Ethics & AI Integrity | None | Competent | Applied Data Protection Act 2018; transparent AI declaration | Continue declaring AI use and applying ethical principles |