Academic research

Academic research begins with curiosity, but it develops through sustained reading, a question worth investigating, and a convincing explanation of what the work contributes. Before applying to a research program, I would spend time understanding both your own interests and the research environment you hope to join. A supervisor, lab, or research group has a history, a set of current questions, and practical limits on what it can support. Understanding that context helps you make a more informed choice.

This advice reflects my perspective in business analytics and computer science. Research in other disciplines may be organized around individual scholarship, studios, archives, fieldwork, community relationships, or other arrangements. Even within one department, supervisors can have very different expectations. Read the program’s requirements and discuss the proposed working relationship rather than assuming that every research degree follows the same model.

Work out what you want to understand

Start with a problem or question that you are willing to spend time thinking about. An interest in artificial intelligence, sustainability, or entrepreneurship is a useful beginning, but it is still a broad area. Try to explain what puzzles you within it. You might want to understand why an organization adopts a technology, how a particular algorithm behaves under changing conditions, or how people experience a change in their work. These are different questions, even when they concern the same technology.

I would write a short account of what interests you, what you have already read, and what you do not yet understand. Then consider the work involved in investigating it. Enjoying the idea of AI is different from enjoying months of debugging experiments, evaluating datasets, interviewing participants, or reading organizational theory. Your interests should include the process of doing the research as well as the subject you hope to study. Early coursework and small projects can help you discover that fit.

You do not need a finished thesis before applying. You do need enough preparation to explain a direction, recognize what you still need to learn, and have a useful conversation about a feasible project. Follow the program’s instructions: some expect a developed proposal, while others match students with projects or supervisors later. The master’s and PhD guides explain how those application expectations can differ.

Read the supervisor’s work, including Google Scholar

A faculty biography tells you how someone describes their interests; their publications let you examine the questions, evidence, and arguments behind that description. Find the prospective supervisor’s university page, lab website, and Google Scholar profile where available. Check the affiliation and publication titles so you are reading the right person’s work. Scholar profiles can contain attribution errors, and a publication list is only one part of the picture.

Google’s Scholar profile guidance explains how profiles display publications and how their owners can correct records. Use the Year heading to look at recent work, then include earlier papers that appear central to the research direction. The Scholar search guide explains author searches, date filters, and the “Cited by” feature. Following references backward and citations forward can help you see how a question developed. Citation totals alone tell you little about whether someone would be a suitable supervisor for you.

I would choose a few relevant papers and read beyond the abstract. Identify the research question, the authors they build on, the methods they use, the evidence supporting the conclusions, and the limitations they acknowledge. Keep notes about connections between papers. Look for recurring collaborators, student authors, datasets, and application settings. Those patterns can suggest how the group works, but ask about current projects directly: a recent publication may describe work completed several years earlier.

Read student theses and the lab’s current project descriptions as well. They can help you compare the scope of a degree project with the scope of a journal article or a larger research program. If a paper is difficult, work through the introduction and conclusion, look up unfamiliar concepts, and return to the technical sections. A thoughtful question about something you are still learning is more useful than a claim to have mastered work you have only skimmed.

The University of Calgary’s advice on finding a supervisor is a useful companion to this reading. It recommends examining publications and student involvement, considering current research interests, and discussing availability and funding. Use the publications to prepare for that discussion rather than treating them as confirmation that someone is recruiting.

Understand the research agenda you would join

From a faculty perspective, recruiting a research student involves thinking about the work we can develop together. Supervisors may be writing funding applications, carrying out funded studies, publishing papers, maintaining partnerships, and helping existing students complete their degrees. Your academic and professional background matters, but its relevance becomes clearer when you explain how it prepares you to contribute to particular questions, methods, or collaborations.

A strong match might involve an existing technical skill, experience with a research setting, careful writing, or evidence that you can learn and work reliably with others. It also involves intellectual interest: can you explain why the group’s questions matter to you and where you see a connection with your own goals? You should be able to discuss a specific piece of work and a possible next question without pretending that every project the supervisor has published is your ideal topic.

Research fit is a mutual assessment. You are also choosing the training, feedback, and working environment that will help you develop. A shared topic does not establish that a supervisor has funding, time, or space for a new student. Ask how your interests connect to current plans and what support would be available. Your contribution to the group should sit alongside a clear educational purpose for your degree. The funding guide can help you separate potential opportunities from a confirmed offer.

Clarify the project and the working relationship

In some programs, your thesis will be one part of a larger project already underway in the lab. The broad problem, partner, equipment, or dataset may be established, while your task is to develop a particular question within it. Western’s supervision guidance describes how admission can be connected to specific grant-funded or contract-supported projects. In other programs, you will propose a direction and refine it with your supervisor and committee. UBC’s Graduate Game Plan explains this variation in how topics are assigned or developed. Neither arrangement tells you, by itself, how much independence you will have.

Be specific about the boundaries. Ask which parts of the project are fixed, which decisions you can make, and how the thesis will demonstrate your own contribution. Discuss what happens if data access falls through, a partner changes direction, or an experiment does not support the original expectation. If you are joining a funded project, clarify the relationship between the research you need for your degree and any separate employment duties. These conversations are easier before everyone is working from different assumptions.

Discuss supervision in practical terms: how often you will meet, how feedback is given, who provides day-to-day help, and how progress is assessed. In a group setting, ask about shared code, equipment, documentation, lab meetings, and collaboration with other students. Talk early about authorship, data access, ownership, and permission to publish, using the institution’s policies and any project agreements. A manuscript target and a degree-completion requirement are different things; establish what the program actually requires.

I recommend recording the agreed expectations and revisiting them as the project develops. A short written account can make it easier to notice when the scope, resources, or timeline have changed. UBC’s supervision guidance provides an institutional example of written expectations and independence appropriate to the degree. Good supervision includes guidance on becoming a researcher, alongside support for completing the immediate project.

Think about the bookshelf your work belongs on

One of my professors explained this to me through the image of writing a book. When your book is finished, it will sit on a bookshelf next to, or between, other authors. Whose books belong beside yours? Who is the audience, what conversation are they already having, and why would they need to read your contribution? I still find that a useful way to think about a research project, whether the final product is a thesis, an article, or something else.

The bookshelf gives your work a scholarly context. You need to know the ideas you are using, the findings you are building on, and the disagreements your argument enters. A literature review should help a reader understand those relationships. UBC’s guide to mapping the scholarly conversation offers writing exercises for connecting the existing discussion to the space your own study will occupy. Use that reading to identify a meaningful question and explain why answering it matters.

Your audience may include both researchers and people who will use the results. A practitioner might want a workable decision tool; an academic reader may also ask what the tool teaches us, how it was evaluated, and what its limits reveal. An unfamiliar setting or a new dataset can support a contribution, but you still need to explain what becomes better understood through studying it. As you read, keep returning to the connection between your specific project and the larger body of knowledge.

Connect ontology, epistemology, theory, and method

It is worth learning these terms early enough that they help you design the study. They concern different parts of the reasoning behind research: what you think exists, how you can know about it, which ideas help you understand it, and how you will investigate it. They are connected, but they are not interchangeable. You do not need to settle every philosophical debate before beginning; you do need to recognize the assumptions that shape the claims you hope to make.

Ontology: what you understand to be real

Ontology concerns the nature of reality and what kinds of things exist. In a study of organizations, you might ask whether an idea such as trust is being treated as a stable characteristic, a relationship, or something formed through social interaction. That choice affects what you would look for. The Open University’s introduction to ontology, epistemology, and methodology provides a starting point for these distinctions.

For computer science readers, there is another use of the word: an ontology can be a formal representation of concepts and their relationships in a domain. Noy and McGuinness’s Stanford guide to ontology development explains that technical usage. Here, I am primarily discussing the philosophical assumptions behind a research project.

Epistemology: how knowledge can be justified

Epistemology concerns knowledge: what can be known, how we can know it, and what makes a claim convincing. Think about the evidence your research question needs. A measured difference, a participant’s account, and a mathematical proof support different kinds of arguments. Explain how your evidence warrants your conclusion, where interpretation enters, and what remains uncertain. The Open University introduction above connects these questions about knowledge to the assumptions behind a study.

Theory: the ideas that organize your argument

A theory provides concepts and relationships that help organize a research problem. Depending on the field and purpose, it may help explain events, make predictions, interpret experience, or guide the design of an intervention or system. Shirley Gregor’s The Nature of Theory in Information Systems is a useful scholarly starting point because it distinguishes several purposes theory can serve in a field close to business analytics and computing.

Ask what your chosen theory helps you notice and what it leaves outside the frame. You might use an existing theory, compare explanations, refine a concept, or develop an argument from your findings. Those are different research tasks. A theory should do some work in the study: shaping the question, guiding analysis, or helping you explain the results. Naming a familiar framework without showing that connection will leave the reader wondering why it is there.

Methodology and methods: the reasoning and the work

Methodology is the reasoning behind your approach to inquiry; methods are the procedures you use to carry it out. A study’s methodology should explain why its design can address the question. Its methods might include interviews, an experiment, document analysis, statistical modelling, or a computational evaluation. The Open University’s GO-GN Research Methods Handbook connects research assumptions with approaches to design and data collection.

Be able to explain why the proposed evidence is appropriate, how you will obtain and analyse it, and what conclusions the design can support. Using a particular software package is only one practical decision within that reasoning. Similarly, a predictive model’s accuracy does not by itself explain why people behave as they do. Match the method to the claim you want to make, and be explicit about the limits.

Paradigms connect these choices

A research paradigm brings together assumptions about reality, knowledge, and inquiry. You will encounter terms such as postpositivism, interpretivism, constructivism, critical realism, and pragmatism. Their meanings and uses deserve reading in your own discipline. The GO-GN handbook offers an accessible introduction; it is a starting point for study rather than a rulebook assigning every project to one category.

Different fields make these commitments more or less explicit. An interview does not automatically make a study interpretivist, and numbers do not settle its philosophy. Read how researchers justify their designs and ask your supervisor what treatment is expected in your proposal or thesis. The practical aim is coherence: your question, assumptions, theory, evidence, and conclusions should make sense together.

See how one interest can become different studies

Consider a hypothetical interest in AI-assisted decisions at work. One project could investigate whether a particular interface changes decision accuracy. You would need to define accuracy for the task, decide what comparison would be informative, and design a study that supports the inference you intend to make. A theory about attention or decision-making might help explain the expected relationship. Even a promising result would need to be interpreted within the participants, tasks, and conditions studied.

Another project could investigate how employees come to trust, question, or work around the same system. Interviews and observation might help you understand how people interpret its role in their everyday work. You would need to explain how participants were selected, how accounts and observations were analysed, and how your position as a researcher influenced the inquiry. The contribution could concern how trust is negotiated in a particular organizational setting, rather than an estimate of the interface’s effect on accuracy.

A computing project might instead develop an evaluation method for detecting failures under changing data conditions. Its central contribution could be a technical approach supported by an analysis of assumptions and comparisons with existing methods. These are illustrative possibilities, not a fixed mapping between disciplines and methods. Each needs relevant prior scholarship and a defensible design. Combining approaches can be useful when you can explain how their findings will inform one another.

This is also why supervisor fit requires more than a shared keyword. Several groups may describe their work as AI research while asking very different questions and expecting different preparation. Read deeply enough to recognize the kind of inquiry you would be joining and the training you would need.

Understand what contribution means at your level

Early research experiences often emphasize learning to read critically, use an established method, document decisions, and make a careful argument. Reproducing a result or completing a well-scoped analysis can be a valuable part of that education. Some undergraduate projects also produce original findings. Ask what the particular course, project, or degree expects instead of assuming that every piece of academic work must immediately create a new theory.

At the research master’s level, you are developing the ability to conduct sustained scholarly work and explain its place in a field. Originality requirements vary by program and thesis format. For example, UBC’s thesis and dissertation scope guidance describes a master’s thesis in terms of knowledge of the field and substantial scholarly work, with an original contribution where possible. Course-based and professional master’s programs can have different final assessments, so consult the actual requirements.

Doctoral work normally carries a stronger expectation of an original contribution and increasing intellectual independence. UBC’s guidance describes a doctorate as contributing significant new knowledge or understanding and allows that contribution to involve synthesis across fields or the application of knowledge to a problem. A contribution does not have to be a wholly new subject. What matters is being able to establish what your work adds, why it matters, and how the evidence and reasoning support it.

I would practise expressing that contribution in a short paragraph: identify the existing understanding, explain the question or limitation you will investigate, and describe what a reader could learn from the study. Keep the claim proportionate to the work. A useful replication, a carefully justified extension, a new explanation, or an evaluated technical method can matter for different reasons; whether it meets the standard for your degree is a decision to clarify with your supervisor and committee.

Arrive ready for a research conversation

Before meeting a potential supervisor, prepare a short account of your interests and connect it to a few things you have read. Explain what you understand about the group’s direction, what preparation you bring, and where you would need training. Identify a question you would like to investigate and why it belongs in that setting. Be ready for the conversation to change your understanding of the project.

Use the meeting to learn how the proposed degree would work. Establish the expected scope, the balance between an existing agenda and your own direction, the available resources, and the process for developing a proposal. Ask what successful progress looks like in that program. Follow the application guide for the documents and formal process, and use the student life guide to think about the working relationships and support you will need after arrival.

Your aim is to understand the research well enough to make a thoughtful commitment. The reading continues after admission, and the question will usually become more precise as you learn. Keep returning to the bookshelf: the people you are writing for, the ideas you are building on, and the contribution you can explain clearly.

University guidance, Google Scholar documentation, and research-methods sources linked here were reviewed on 13 October 2026. The examples are illustrative and the advice reflects my perspective; your program’s regulations, supervisor’s expectations, and disciplinary standards determine what applies to your degree.