Unit Outline

This unit provides an introduction to the core concepts, methods, and applications of artificial intelligence. Students explore major AI approaches, including symbolic methods, supervised learning, unsupervised learning, reinforcement learning, generative models, and large language models. The unit focuses on developing a clear understanding of how these systems work, where they are effective, and what limitations and assumptions underlie their behaviour.

Across weekly hands-on activities, students learn to build and evaluate simple machine learning models, interpret a range of AI outputs, compare the strengths of different techniques, and understand how evaluation methods guide model selection. Practical work emphasises the use of appropriate data-handling and evaluation methods, as well as the importance of robustness, context, and ethical considerations when developing or analysing AI systems.

By the end of the unit, students will be able to interpret and assess AI, understand their societal and ethical implications, and form a critical perspective on contemporary and emerging AI technologies. They will also develop foundational practical skills in working with data, training models, and communicating AI results.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1	Describe major approaches in artificial intelligence and explain their core principles, typical applications, and strengths and limitations.
2	Apply basic AI and machine learning techniques to explore data or agent behaviour and interpret results in context.
3	Evaluate AI models and outputs using appropriate evaluation methods, considering robustness, assumptions, and data quality.
4	Critically analyse societal, ethical, and practical implications of contemporary AI systems.

Alterations as a result of student feedback

KIT108 has been completely redesigned.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
Assessment Task 1:	Assignment 1	Week 8	30 %	LO1, LO2, LO4
Assessment Task 2:	Assignment 2	Week 13	30 %	LO1, LO2, LO3, LO4
Assessment Task 3:	Tutorial work	Refer to Assessment Description	40 %	LO1, LO2, LO3, LO4

Assessment details

Assessment Task 1: Assignment 1

Task Description:

Mini-project in which students apply foundational AI and supervised learning methods to analyse a dataset, develop and compare two supervised learning models, and communicate their findings

Task Length:

Annotated Notebook 1000-word report 3-minute (recorded) presentation

Due Date:

Week 8

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explains the selected supervised learning methods, their core principles, and their strengths or limitations in relation to the task	LO1
2	Prepares data appropriately, implements the selected methods correctly, and carries out the modelling process in a coherent way	LO2
3	Compares the two models using relevant evidence, interprets results in context, and draws supported conclusions	LO1, LO2
4	Communicates the project clearly in written and recorded form, and reflects on practical, contextual, or ethical considerations relevant to the work	LO4

Assessment Task 2: Assignment 2

Task Description:

Open-ended mini-project in which students investigate a selected AI problem, apply appropriate methods, evaluate outcomes, and communicate their analysis and conclusions

Task Length:

Project Notebook 1500-word report 3-minute (live) presentation

Due Date:

Week 13

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Defines the selected problem clearly, justifies the choice of methods, and shows awareness of the practical context and implications	LO1, LO4
2	Applies appropriate techniques, tools, data, or workflows effectively to investigate the selected problem	LO2
3	Evaluates models, outputs, or system behaviour using appropriate methods, and critically analyses assumptions, robustness, limitations, and implications	LO3, LO4
4	Communicates the investigation, results, and conclusions clearly and coherently in the required formats	LO1, LO3, LO4

Assessment Task 3: Tutorial work

Task Description:

Weekly (weeks 2-11) in-tutorial practical exercises completed during scheduled tutorials. Students apply unit concepts in guided notebook/lab activities, with marks based on completion, demonstrated engagement, and peer assessment.

Task Length:

10x 30mins

Due Date:

Refer to Assessment Description

Weight:

40 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Applies relevant unit concepts, models, techniques, or workflows to the tutorial task.	LO1, LO2
2	Uses the required computational tools, code, data, algorithms, or knowledge representations appropriately to carry out the activity.	LO2, LO3
3	Interprets results, explains the approach taken, and communicates conclusions or decisions drawn from the activity.	LO1, LO3, LO4

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Academic progress review

The results for this unit may be included in a review of your academic progress. For information about progress reviews and what they mean for all students, see Academic Progress Review in the Student Portal.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Academic integrity

Academic integrity is about acting responsibly, honestly, ethically, and collegially when using, producing, and communicating information with other students and staff members.

In written work, you must correctly reference the work of others to maintain academic integrity. To find out the referencing style for this unit, see the assessment information in the MyLO site, or contact your teaching staff. For more detail about Academic Integrity, see Important Guidelines & Support.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.

Required Resources

Required reading materials

N/A