Aaron Sloman
School of Computer Science
The University of Birmingham

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This is a first draft set of notes on a possible set of syllabus proposals for teaching Artificial Intelligence in Schools. This could be seen as a supplement to current proposals for teaching Computer Science in schools. The aims of the two sets of proposals are different.

A typical Computer Science syllabus would take a 'bottom up' approach in which an understanding of computing is based on understanding of some of the important features of computers and aimed at producing people with the ability to develop and to think about computing systems that meet important current and future practical needs.

The AI proposals presented here are intended to complement such a syllabus by offering a type of education that would be attractive to a different kind of student, such as might be interested in the study of philosophy, psychology, language, social science, biology, or mathematics. It would adopt a more 'top down' approach, by introducing such students to 'higher level' ideas such as the notion that there are various kinds of information processing, some of which occur in nature, e.g. in all animals, including microbes and humans, whereas others occur only in man-made machines. This opposition would lead to the challenge of producing machines that exhibit more of the capabilities that are characteristic of humans and other animals. Such a syllabus should introduce

An example of the difference between the two kinds of syllabus is that whereas a Computer Science syllabus may start its programming component with work on logical and arithmetical operations, the proposed AI syllabus would begin with list-processing programs using a high level AI language providing pattern matching capabilities used to implement simple kinds of 'natural language' processing, such as generating and later parsing sentences, and then making plans, solving puzzles, and perhaps doing some learning. Some schools might wish to introduce AI teaching based on programming physical or robots, though that should be an option rather than a requirement.

What is artificial intelligence (AI)?

AI is a (badly named) field of enquiry with two closely interrelated strands: science and engineering.

AI is inherently highly interdisciplinary because all kinds of intelligence, whether natural or artificial are concerned with subject matters that are studied in other disciplines, and the explanatory models of natural intelligence have to take account of and be evaluated in the disciplines that study the natural forms.

Further information about the scope of AI is provided in an accompanying document:


Like Alan Turing, in 'Computing machinery and intelligence', Mind, 59, pp. 433--460, 1950, I believe attempting to define 'intelligence' is a complete waste of time. We can collect many different examples of competences displayed by humans or other animals, and examples of challenging biologically-inspired behaviours required in future machines, and we can investigate requirements for modelling or replicating them without needing to draw any definite line between those that are and those that are not intelligent. We may find it useful to subdivide the cases in terms of either their capabilities, or the mechanisms required, or the kinds of information they use, or their potential usefulness in various contexts, and those divisions will be much more interesting and useful than any binary division based on a pre-theoretical concept like 'intelligence'.

Why would a student choose to study Artificial Intelligence?

The collection of course descriptions below is aimed at students who are interested in finding out how important ideas associated with the development of computer-based systems are relevant to the broad study of naturally occurring information-processing systems, and to the development of new machines with human-like or animal-like capabilities.

By taking this course such students will not only learn how to design, test, analyse, and compare working computer models of various kinds, but will also learn about their broader significance in helping us understand such diverse phenomena as human use of language, learning, visual and other forms of perception, problem solving, creativity, and also some of the evolutionary processes that produced them and some of the kinds of behaviours found in other animals. They will also be introduced to some of the practical applications of these techniques, e.g. in medical diagnosis, in computer games and in new forms of entertainment, as well as to philosophical and ethical debates related to these ideas.

The syllabus is not specifically aimed at students who wish to go on to higher education courses in computing or employment as computer developers or advanced computer users, though it may help such students. It may also be useful for students who wish to study other subjects at University, such as psychology, biology, linguistics, philosophy, engineering, or management, where it may be useful to evaluate current achievements and limitations of Artificial Intelligence and requirements for future applications of AI in those disciplines.

Students following this syllabus do not require any prior knowledge of computing, though it will be helpful to have basic familiarity with keyboard, mouse and text-input. A great deal of the work will involve typing text into a computer and reading textual output, and students who find that difficult will need special help. The course does not require specific mathematical skills though logical and mathematical potential will be very useful, and there will be opportunities to use and develop such capabilities. For example, learning AI will inevitably involve learning some formal logic and set theory (both of which can be learnt on-the-job), and study of complexity issues can be used as a basis for teaching students about combinations and permutations. Requirements for programs with graphical interactions can be used to teach students about coordinate systems and some linear algebra. The most important prerequisite is a liking for solving problems with an intricate structure, such as crossword-puzzles, soduku, or rubic cubes, and a strong desire to understanding how complex things work.

High level overview of the units

The syllabus will be made up of four units, the first two of which, tuahgt over one year, could form an AS-syllabus, and when supplemented with the remaining two, taught in the second year, could form an Advanced-level syllabus. This is a first draft specification, subject to revision. It may be better to split some of the units into smaller, separately assessed components.



Some practical challenges and possible (initial) solutions

There are several practical problems that will have to be addressed if this proposal is to be implemented. These problems and possible solutions are discussed further on this web page:

Background to this proposal

This proposal has the following influences:

Overview document on AI

An associated web file elaborates on the brief definition of AI given above.

It is broadly based on the document on AI produced for the QAA benchmarking panel in 1999, available at
which was informed by consultation with university teachers and researchers in AI in the UK.

It's purpose is to provide a reminder of the scope of AI that can inform the more detailed design of a syllabus. However, many of these topics are too difficult to be included in a school syllabus, and are listed merely for information.

Additional information

The Association for Computing Machinery has a list of sub-fields of computer science, which changes from time to time. Several versions are available at

It is not updated often, so it is now out of date and should be treated with caution.

AI Organisations

There are two main UK AI organisations.

The Society for the Study of Artificial Intelligence and Simulation of Behaviour, claims to be the oldest AI society in the world. See

The British Computer Society Specialist Group on Artificial Intelligence (BCS-SGAI) has a more applied focus, though its seminars and annual conferences are very wide ranging. See

The main European AI organisation is ECCAI (European Coordinating Committee on AI), to which national AI organisations are affiliated. See

The largest AI organisation is the Association for the Advancement of Artificial Intelligence (AAAI). Information about it is at . It includes AITOPICS, a collection of Web pages (under continual development) that attempt to characterise the scope of AI, and provide a steady stream of news about AI and its applications.

The major regular international conference on AI is The International Joint Conference on AI (IJCAI) held every two years since 1969. See

There are also many national Artificial Intelligence Societies, which organise conferences, and other activities.

There is a growing collection of web sites providing information about AI, some compiled by individuals and some by firms or organisations. for example the New Scientist AI web site

A document on AI for School Careers Advisers

A document was put together in 1998 for a conference of school careers advisers in England giving a more discursive overview of AI:

This includes pointers to several other sources of information about AI, including summaries by some of the founders of the field (e.g. Minsky, McCarthy), some textbooks, and UK universities known to be offering undergraduate degrees with AI in the title in May 1998.

This file maintained by Aaron Sloman
Last updated: 4 Mar 2007; 27 Aug 2017 (re-located)