Academic English
Build clear arguments, discuss uncertainty, explain technical terms and participate in seminars. Practise paraphrasing and concise professional email.
Practice: explain your research question to a reader outside your field.
Develop academic English, research methods, academic writing and presentations, then practise explaining your IT experience in interviews and professional settings.
For emerging researchers, postgraduate learners and IT professionals preparing for research communication or career transitions. Start with your current goal and develop work you can explain independently.
Build clear arguments, discuss uncertainty, explain technical terms and participate in seminars. Practise paraphrasing and concise professional email.
Practice: explain your research question to a reader outside your field.
Find relevant work, compare methods and evidence, identify limitations and organise a literature matrix. Connect sources to the argument you are making.
Practice: compare two studies without reducing the review to two separate summaries.
Develop a bounded research question and justify a qualitative, quantitative or mixed-method approach. Consider sampling, evaluation, validity, ethics and reproducibility.
Practice: turn a broad AI topic into a feasible question, baseline and evaluation plan.
Structure a proposal, abstract or report around a clear contribution. Link claims to evidence, distinguish results from interpretation and revise for coherence and appropriate citation.
Practice: revise your own paragraph and explain the reasoning behind each change.
Plan a storyline for a technical or research audience. Choose useful figures, explain methods and findings, rehearse timing and respond to questions about limitations.
Practice: prepare a short presentation and handle a question you cannot answer yet.
Explain projects, technical decisions and trade-offs in your own words. Practise behavioural examples, teamwork, stakeholder conversations and workplace communication.
Practice: present a project, answer a design follow-up and describe your individual contribution.
Starting topic: “AI improves document analysis.” First identify what improves, compared with which baseline, on what tasks and under which conditions. The exercise develops a plan rather than inventing a positive result.
On a fixed set of document questions, how do evidence checks affect unsupported-claim rates compared with the same assistant without those checks? Define the dataset, source-support rubric, baseline and repeated-run procedure before collecting results.
Research question
Why it matters
Related work and gap
Method and baseline
Data, ethics and permissions
Evaluation and uncertainty
Limitations and next stepsExplain the problem, the research gap, your method, the evidence and the limits. Prepare one slide per main idea and rehearse a concise explanation of the contribution.
Explain the context and your responsibility, the decision you made, the alternatives you considered and what you learned. Use a situation–task–action–result structure when it helps, and distinguish team outcomes from your own contribution.
Discuss the audience, your starting point and the outcome you want to practise.
Work through your own outline, paragraph, slides or project explanation together.
Practise explaining ideas and responding to questions, with focused feedback.
Apply feedback, reflect on the changes and agree a useful next exercise.
Possible outputs include a research-question brief, literature matrix, methodology outline, revised writing sample, presentation storyboard and interview practice notes. Language, session timing and fees are agreed individually. Learners remain the authors of their own research and application materials.
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