Our objective
Develop practical AI tools that help people learn, work with information and make better use of data.
Independent AI research & development
From research.
To practical AI.
MKN explores how machine learning and language models can solve practical problems — starting with tools for cybersecurity education.
FocusApplied artificial intelligence
StageEarly development
ApproachResearch to prototype
01 Our approach
Start with the problem.
Build towards a useful tool.
MKN is an early-stage, founder-led initiative connecting applied AI research with focused software development. The aim is to turn promising ideas into tools that can be tested, understood and improved.
Our scope
Applied machine learning, language-model applications and educational software, with cybersecurity teaching as the current development focus.
How we develop
Define a narrow use case, build a prototype, and evaluate it before extending its scope. Keep human judgement central to the design.
02 Projects & research
Where the ideas
take shape.
Our current development direction, alongside research experience that informs our approach.
In development
AI-assisted cybersecurity teaching
A five-module framework designed to support both learners and educators: from contextual tutoring and assessment to controlled practice, incident-response scenarios and teaching materials.
Initial prototype focus
Discuss this project
L1 · Context-aware Tutor
L5 · Teaching & Case Generator
L1Context-aware TutorPrototype focus
Guidance designed around a learner’s task and progress. The aim is to help learners understand their reasoning and next steps, with educators retaining oversight.
L2Process-aware AssessmentPlanned module
Assessment that considers how a learner approaches a task, alongside the final answer. The proposed module would help educators review reasoning, decisions and progress.
L3AI Red / Blue OpponentPlanned module
A proposed practice partner for offensive and defensive exercises within controlled, authorised teaching labs, with learning objectives and boundaries set by educators.
L4Incident-Response SimulatorPlanned module
Scenario-based practice for investigating incidents, prioritising actions and explaining decisions. Intended for teaching and reflection in simulated environments.
L5Teaching & Case GeneratorPrototype focus
Tools to help educators draft lesson materials, exercises and case studies around learning objectives. Generated content would be reviewed and adapted by the educator before use.
The framework is at an early development stage. Module descriptions set out the intended scope; a public product is not yet available.
Founder’s collaborative researchDamp & mould risk
Damp & mould risk
in UK housing
Research exploring positive–unlabelled learning for recorded damp and mould risk in housing data. This work informs an interest in applying machine learning where records are incomplete and labelled examples are limited.
- Research area
- Applied machine learning for housing
- Method
- Positive–unlabelled learning
- Relationship to MKN
- Research experience of the founder
03 People
A founder-led
initiative.
MKN begins with a focused development agenda and an interest in collaboration across research, education and software engineering.
Manh Khang Nguyen
Founder · AI engineering & research
Manh Khang’s work centres on applied machine learning and AI systems. His current development interests include AI-assisted cybersecurity education and practical uses of machine learning with real-world data.
- Applied machine learning
- AI systems
- Cybersecurity education
04 Contact
Have a problem
worth exploring?
We welcome conversations with educators, researchers and potential collaborators interested in practical AI applications.
General enquiries & collaboration
contact@mknuk.work Website enquiries
support@mknuk.work