TL;DR — Quick Answer Artificial intelligence (AI) is the field of building computer systems that can perform tasks normally requiring human intelligence — such as understanding language, recognising images, making decisions, and solving problems. AI includes several approaches, the most important today being machine learning, where systems learn from data. The main types are narrow […]
Read More →TL;DR — Quick Answer To prepare for a PhD viva (the oral defence of your thesis): reread your thesis thoroughly, anticipate likely questions, know your contribution and limitations inside out, prepare to defend your methodology, read your examiners’ relevant work, and practise with a mock viva. The viva tests whether the thesis is genuinely your […]
Read More →TL;DR — Quick Answer To design a research questionnaire: start from your research objectives, write clear and unbiased questions, choose appropriate question types (closed or open-ended), use established scales like the Likert scale where suitable, organise questions logically, keep it as short as possible, and always pilot test before full distribution. A good questionnaire collects […]
Read More →TL;DR — Quick Answer A research hypothesis is a clear, testable statement that predicts the relationship between two or more variables. To write one: identify your variables, state the expected relationship between them clearly and specifically, ensure it is testable and falsifiable, and ground it in existing theory or research. The main types are the […]
Read More →TL;DR — Quick Answer A research proposal is a structured document that explains what you plan to research, why it matters, and how you will do it. It is used to gain approval and funding for a research project — for PhD admission, grant applications, and ethics approval. A strong proposal includes a clear title, […]
Read More →TL;DR — Quick Answer A literature review is a critical summary and synthesis of existing research on a topic. To write one: define your scope, search systematically for relevant sources, read and evaluate them critically, organise them thematically (not source by source), synthesise the findings to show patterns and gaps, and write it as a […]
Read More →TL;DR — Quick Answer Data science is the field that uses scientific methods, statistics, programming, and domain knowledge to extract meaningful insights from data. A data scientist collects, cleans, analyses, and interprets data to answer questions, predict outcomes, and support decisions. The core skills are statistics, programming (usually Python or R), data visualisation, and domain […]
Read More →TL;DR — Quick Answer A cover letter for journal submission is a one-page letter to the editor that introduces your manuscript and makes the case for why it belongs in their journal. It should state the title and type of your paper, summarise its key contribution, explain why it fits the journal’s scope, confirm the […]
Read More →TL;DR — Quick Answer A systematic literature review is a rigorous, structured method of identifying, evaluating, and synthesising all the relevant research on a specific question, following a transparent and reproducible process. Unlike a traditional literature review, it uses predefined criteria, a documented search strategy, and systematic screening to minimise bias. The main stages are […]
Read More →TL;DR — Quick Answer AI in human resource management uses artificial intelligence to automate, support, and improve HR tasks — including recruitment, candidate screening, employee onboarding, performance management, training, and workforce analytics. It helps HR teams save time, reduce repetitive work, and make more data-informed decisions. However, it carries real risks around bias, fairness, privacy, […]
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