Resource Guide
Research Project Ideas in Computer Science for High School Students
Specific, answerable questions across algorithms, systems and security, human-computer interaction, and applied data, with guidance on how to choose and pursue one.
How to Use This List
Computer science is unusually rich ground for pre-university research, because so much of it can be done with a laptop, open data, and disciplined thinking. The trap students fall into is choosing a project that is really an engineering build, "make an app", rather than a research question with something to find out. The ideas below are framed as questions for exactly that reason.
Treat each as a starting point to narrow further. A strong project takes one of these and sharpens it until it is specific enough to answer in the time available. Our guide to writing a research question shows how to do that, and our literature review guide explains how to ground it in what is already known.
Choose a Method Before a Topic
The choice that decides whether a project is feasible is the method, not the topic. Every question below is tagged with one of these four routes.
Build and benchmark
Implement something and measure it. Computer science is the subject where you can produce real evidence on a laptop, and the difference between a good project and a weak one is measurement: state your inputs, your timing method and your variance, not just that it worked.
A language you know and a question that has a number for an answer.
Open data analysis
Transport, weather, census and government data are all published and largely unused by students. Analysing a real dataset shows the skill that actually matters in computing, which is handling messy data honestly.
A dataset, a notebook, and honesty about what the data cannot show.
Literature review
For questions you cannot benchmark, particularly in security and theory, read the primary papers and lay out what is established and what is contested. Security especially rewards this: the attacks are published.
Papers from arXiv or the ACM Digital Library, much of it free.
User study
Interface questions need people, which brings real obligations: informed consent, the right to withdraw, anonymised results, and school approval before you start. Anyone under 18 needs parental consent. Small and properly run beats large and improvised.
School permission first, then consent forms. Budget time for approval.
Ideas by Sub-Field
Algorithms & theory
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How do sorting or pathfinding algorithms compare in practice on real-world data, beyond their theoretical complexity?
Build and benchmark Intermediate
Output: Implementations timed on real inputs, with the gap from theoretical complexity explained
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What does the P versus NP question actually claim, and why does it matter so much?
Literature review Advanced
Output: An accurate account of what the claim is and why proof attempts keep failing
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How well do heuristic algorithms approximate solutions to hard problems such as the travelling salesman?
Build and benchmark Advanced
Output: Heuristics benchmarked against optimal solutions on instances you generate
Systems & security
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How do common cryptographic methods work, and where are they vulnerable to modern attacks?
Literature review Advanced
Output: An explanation of one cipher and a survey of the published attacks on it
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How do denial-of-service attacks exploit network design, and what defends against them?
Literature review Intermediate
Output: An account of the mechanism and an assessment of published defences
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What trade-offs govern the design of a caching or database system, and how do they affect performance?
Build and benchmark Advanced
Output: A cache implemented under two policies, with hit rates measured
Human-computer interaction
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How do specific interface design choices measurably change how quickly users complete a task?
User study Intermediate
Output: A small task-timing study with consent obtained and the sample limits stated
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What does the research say about accessibility in widely used software, and where does it fall short?
Literature review Intermediate
Output: An audit of named products against WCAG criteria, with failures documented
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How do dark patterns in app design influence user decisions, and how can they be detected?
Literature review Intermediate
Output: A published taxonomy applied to real apps, with screenshots as evidence
Applied computing & data
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Can a simple model predict a public phenomenon, such as transit delays, from open data, and how well?
Open data analysis Intermediate
Output: A predictive model on published transit data, scored against a naive baseline
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What patterns emerge from analysing a large open dataset such as weather, transport, or census data?
Open data analysis Accessible
Output: An analysis with charts you made and a clear statement of what the data cannot show
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How do recommendation algorithms shape what users see, and what are the measurable effects?
Literature review Intermediate
Output: A review of published audit studies on recommender effects
Choosing the Right Scope
The single most common mistake in computer science projects is taking on a question that is either too vast ("how can AI cure cancer?") or really an open-ended build with no claim to test. A good research question has a definite shape: you can describe, in a sentence, what finding would answer it.
A useful test is to ask what your output would look like. A comparison study produces evidence about which approach performs better and why. An analysis produces a characterisation of a system or dataset. A security investigation produces a clear account of a vulnerability and its defences. If you cannot picture the output, the question needs narrowing first.
Where a project leans heavily on machine learning, our companion guide to research project ideas in AI and machine learning goes deeper on that territory, and our mathematics guide covers the theory underneath it.
Taking a Question Further
Computer science borders many fields, and some of the most interesting projects live on those borders: an algorithmic question that becomes an economics question about markets, a security question that becomes a politics question about surveillance, a modelling question that becomes a medical one.
For the wider context, see our Technology, AI & Engineering field page and our broader research project ideas across all six fields. When you are ready to turn a question into a finished project with a mentor who works in the field, the Research Scholar programme is built for exactly that.
Frequently Asked Questions
Do I need to be an advanced programmer to do a computer science research project?
No. Some of the strongest school-level projects are analytical or theoretical and involve little code. What matters is a clear question and rigorous reasoning. Where a project does need programming, a mentor helps a student learn the specific tools it requires rather than assuming fluency in advance.
What can I do without powerful hardware or a research lab?
A great deal. Free cloud notebooks such as Google Colab and Kaggle, together with open datasets, let students run genuine computational experiments. Theory, algorithm analysis, and security research often need nothing more than a laptop and careful thought.
Can a computer science project be research if I am not building new software?
Yes. Comparing how algorithms behave on real data, analysing a system’s design trade-offs, investigating a security vulnerability, or examining the societal effects of a technology are all genuine research, and often more tractable than building something new from scratch.
How does a computer science research project help with university applications?
Selective computer science courses look for evidence of independent problem-solving and genuine curiosity beyond the syllabus. A focused project demonstrates both, and gives an applicant something specific to discuss in a personal statement or interview, including the design choices they made and where their approach broke down.
Free PDF
Take the 50 strongest ideas with you
A free PDF with fifty of these questions tagged by method and difficulty, the output each should produce, and the feasibility checklist we apply before recommending any project. We email it to you, along with occasional guidance worth having. No obligation, and your details are not passed to anyone.
Now placing students for the October start. Places in each field are limited by mentor availability. We review applications on a rolling basis.
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