Core Module Information
Module title: Artificial Intelligence

SCQF level: 09:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET09122
Module leader: Ben Paechter
School School of Computing
Subject area group: Computer Science

Module Code: SET08122
Examples of Equivalent Learning Industry implementation of equivalent algorithms and data structures. Learning involving programming of non-trivial algorithms and data structures.

Description of module content:

The module is partly based on the first three sections of “Artificial Intelligence: A Modern Approach” (3rd
edition) by Russell
and Norvig. The indicative content from the book is as follows:
Introduction: What is AI? History of AI and the state of the art.
Agents : An introduction to agents, their behaviors and structure
Searching: Problem solving by searching, heuristics, local search and optimisation and adversarial
Constraint satisfaction problems: defining and solving CSPs
Logic: Propositional logic, first-order logic, knowledge representation
In addition to this the module will feature introductions to other AI techniques including neural networks,
machine learning and nature inspired methods.

Learning Outcomes for module:

LO1: Critically reflect on how AI concepts can underpin problem solving tasks
LO2: Apply the fundamental concepts and origins of artificial intelligence to solve example problems
LO3: Choose, compare and implement AI based solutions to problem solving tasks making use of appropriate software tools and libraries.
LO4: Solve example problems using AI techniques
LO5: Evaluate the effectiveness and appropriateness of specific AI techniques for specific applications

Full Details of Teaching and Assessment
2022/3, Trimester 1, FACE-TO-FACE, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Ben Paechter
Module Organiser:

Learning, Teaching and Assessment (LTA) Approach:
This module will be delivered using a combination of lectures and practicals and tutorials. The practicals offer the students a
chance to utilise tools and software libraries to experiment with
the AI techniques being covered in the lectures (LOs 3,4 and 5). The lecture material will concentrate on LOs 1,2 and 4, with
some coverage of LOs 3 and 5.
Embedding of employability/PDP/scholarship skills
The AI techniques to be covered are widely used in industry and academia.
The AI tools and techniques used within the module are recognised internationally.

Formative Assessment:
To Support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved

Summative Assessment:
The coursework task is designed to provide evidence of key skills. The task will require the analysis of a simple problem and
the use of AI techniques to produce a solution, this will assess
LOs 3 and 4. LO5 will be assessed by having the student submit a short report detailing the approach taken and it’s
The formal exam will assess LOs 1, 2 and 4.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Practical classes and workshops 24
Face To Face Centrally Time Tabled Examination 2
Independent Learning Guided independent study 138
Face To Face Lecture 24
Online Tutorial 12
Total Study Hours200
Expected Total Study Hours for Module200

Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Practical Skills Assessment 50 1,3,4,5 10 HOURS= 16, WORDS= 0
Centrally Time Tabled Examination 50 1,2,4 14/15 HOURS= 2, WORDS= 0
Component 1 subtotal: 50
Component 2 subtotal: 50
Module subtotal: 100

Indicative References and Reading List - URL:
Contact your module leader