Undergraduate curriculum -- international cohort

Foundations of Science
for a Connected World

A one-semester introductory curriculum designed for international students entering science, technology, or interdisciplinary programmes. No prior university-level science required.

Open enrolment 12 weeks 3 hrs/week English-medium Multilingual support

About this course

Who is this for?

International students at the start of a science or technology degree who need a rigorous but accessible bridge between secondary school and university-level inquiry. Taught in plain English, with vocabulary support for non-native speakers.

What will you learn?

How scientists ask questions, design studies, represent knowledge, and communicate findings. Core concepts from systems science, biology, physics, and computing -- treated as a single connected landscape, not isolated silos.

How is it taught?

Weekly lectures, short readings, one hands-on lab activity per unit, and structured peer discussion. Assessment is cumulative -- small exercises build toward a final project of your own choosing.

What makes this different?

Designed by a systems engineer and knowledge representation researcher. Emphasis on understanding how knowledge is structured, not just memorising facts. AI tools are critically examined, not uncritically used.

Curriculum -- click any unit to expand

Course Modules

01
What Is Science? Asking Good Questions
The nature of scientific inquiry, observation, and hypothesis
Foundations
Week 1 -- 2
  • What counts as a scientific question -- and what does not
  • Observation vs. inference: the difference matters
  • Forming a hypothesis: falsifiability explained plainly
  • The role of uncertainty and error in science
  • How different cultures have contributed to scientific knowledge
  • Vocabulary workshop: key terms in English for international learners

By the end of this unit you will be able to identify a researchable question in any domain, state a hypothesis clearly, and distinguish anecdote from evidence.

02
Systems Thinking: Everything Is Connected
Feedback loops, emergence, and the logic of complex systems
Science
Week 3
  • What is a system? Parts, relationships, and boundaries
  • Positive and negative feedback loops -- with everyday examples
  • Emergence: why the whole is more than the sum of its parts
  • Case study: a forest as a system (energy, matter, information)
  • Drawing a simple system map -- hands-on activity

You will draw a system diagram of a real-world phenomenon of your choice and identify at least two feedback mechanisms within it.

03
The Language of Science: Data, Models, and Representation
How knowledge is structured, stored, and communicated
Methods
Week 4 -- 5
  • What is data? Qualitative vs. quantitative, structured vs. unstructured
  • Models: why scientists simplify reality on purpose
  • Reading a graph, table, or diagram critically
  • Introduction to knowledge representation: ontologies in plain language
  • The problem of translation: science across languages and cultures
  • Lab: building a simple concept map for a chosen topic

You will interpret three scientific figures from published papers and produce your own concept map connecting at least eight terms from a topic of your choice.

04
Life, Information, and the Brain
Core concepts from biology and neuroscience for non-specialists
Science
Week 6 -- 7
  • The cell as an information-processing system
  • DNA: instructions, not destiny
  • How neurons work -- the basics without the jargon
  • Perception, attention, memory: what neuroscience tells us
  • Consciousness -- what we know, what we do not, and why it matters
  • Lab: tracing a neural signal from stimulus to response

You will explain the relationship between brain activity and perception in a short written piece (400 words) using at least four technical terms correctly.

05
Computing, Intelligence, and Knowledge
How machines process information -- and what they cannot do
Applied
Week 8 -- 9
  • From arithmetic to artificial intelligence: a short history
  • How machine learning works -- without the mathematics
  • What AI systems actually do when they "understand" language
  • Knowledge representation in computing: why structure matters
  • Critical perspectives: bias, error, and accountability in AI
  • Lab: evaluating an AI-generated text for factual accuracy

You will evaluate an AI-generated scientific summary, identify at least two factual or representational errors, and explain why they occurred.

06
Science, Society, and Ethics
Who decides what gets studied -- and who benefits?
Foundations
Week 10
  • The politics of funding: what gets researched and why
  • Open science, open access, and the knowledge commons
  • Science in non-Western contexts: indigenous knowledge and formal science
  • Environmental science and systems: climate as a case study
  • Ethical review: what are research ethics and who enforces them?

You will write a one-page reflection on a scientific controversy of your choice, identifying the competing interests involved.

07
Final Project: Your Own Scientific Inquiry
Design, conduct, and present a small original investigation
Methods
Week 11 -- 12
  • Choosing a question you can actually investigate in two weeks
  • Designing a simple but rigorous study or analysis
  • Collecting, recording, and interpreting your findings
  • Presenting science: oral and written communication skills
  • Peer review practice: giving and receiving constructive feedback
  • Final presentations -- open to all students and invited guests

You will present a 10-minute project plus a written summary (800 words) documenting your question, method, findings, and limitations.

At a glance

Weekly Schedule

Week Topic Format
1What is science? Asking good questionsLecture + discussion
2Observation, hypothesis, and falsifiabilityLab activity
3Systems thinking and feedback loopsLecture + mapping
4Data, models, and knowledge representationLecture + discussion
5Reading science: graphs, tables, concept mapsLab activity
6Life and information: cells and DNALecture
7The brain: neurons, perception, consciousnessLecture + lab
8Computing and artificial intelligenceLecture + workshop
9AI critical evaluation labLab activity
10Science, society, and ethicsSeminar
11Project workshop and peer reviewWorkshop
12Final presentationsPublic presentations

How you are assessed

Assessment Structure

40%
Final project
Original inquiry + 10 min presentation + 800-word write-up
30%
Weekly exercises
Short activities submitted after each unit -- marked for engagement, not perfection
20%
Peer review
Two structured peer reviews of classmates' draft projects
10%
Participation
Discussion contributions -- quality over quantity; international learners explicitly supported

Instructor

PDM
Paola Di Maio, PhD
Chair, W3C AI Knowledge Representation Community Group -- Research Lead, Ronin Institute -- Editor-in-Chief, Anthropomorphic Press

Interested in this course?

Contact to discuss delivery, scheduling, or tailored versions for your institution.

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