Bachelor of Information Technology
Specialisation in Artificial Intelligence

CRICOS Course Code: 114970B

The Bachelor of IT (BIT) has been designed to provide students with a comprehensive foundation in core IT discipline areas that align with the professional requirements of the ACS. Throughout the course, students may choose to complete their capstone subjects and their electives in Artificial Intelligence (AI) to further enhance their knowledge, competencies, and skill set. A student who chooses to specialise in AI would undertake their two electives and their two capstone projects in this discipline area to supplement the core subjects in the course related to AI.

Undertaking a specialisation in AI will provide students with the knowledge, skills, techniques, and practical applications of AI applications in different contexts and the ethical and legal dilemmas relating to the use of AI. Throughout the course and in their electives and capstone projects, students will develop the knowledge and skills they need to progress their interest in an AI career.

The course has been designed to be a 3-year full-time or equivalent part-time course comprised of 22 core subjects, including the choice of 2 electives in the final year of study. The academic year is comprised of three trimesters each of 12 weeks duration with a typical full-time delivery format of three subjects in trimester one, three subjects in trimester two and two subjects in trimester three.

Graduates of the Bachelor of IT will be able to:
CLO1	Appraise technical and theoretical concepts and frameworks to manage IT problems and challenges ethically and sustainably
CLO2	Critically review, analyse, and synthesise IT knowledge, methodologies, and approaches to support innovation and competitive advantage
CLO3	Communicate relevant knowledge and ideas effectively and efficiently to a range of audiences.
CLO4	Demonstrate initiative and informed judgement in IT planning, problem-solving and decision making.
CLO5	Apply a broad understanding of IT knowledge and technical skills with depth in some areas to solve real-world lT problems.
CLO6	Work in collaboration with others to adapt knowledge and skills in diverse contexts through responsible and accountable IT professional practice.

In addition to the potential employment opportunities for graduates of the BIT, an artificial intelligence specialisation will provide graduates with career opportunities in:

Machine Learning Engineer
Business Intelligence Developer
Research Scientist
Big Data Engineer/Architect
Product Manager
Software Engineer
Software Architect
Data Analyst
Robotics Engineer
NLP Engineer

The mode of delivery is face-to-face, supported by SHEA’s Learning Management System for access to subject-specific learning resources and information, including submitting assessments. Moodle will be the LMS for delivering the learning strategy to engage interactively with students using the digital tools anytime and anywhere to augment their classroom experience.

The assessment types in the course are predominantly authentic assessments with some traditional assessments for knowledge-based subjects in the first year:

Problem-based tasks (either individually or team)
Case studies
Presentations
Report
Peer and self-assessment (moderated)
Quizzes (used well can keep students engaged, formative assessment because quizzes are easy to administer and provide feedback)
Project (individual and in teams)

The subjects highlighted below show the pathway for a specialisation in artificial intelligence

Subject code	Subject title	Pre-req
Year 1
Trimester 1
ITOP501	Computing Systems Fundamentals
PROF502	Ethics and the IT Professional
SCTY503	Cybersecurity Concepts
Trimester 2
PROG504	Programming Principles
PROF505	Analytical and Critical Thinking	ITOP501
DATM506	Statistics and Linear Algebra	ITOP501
Trimester 3
DATM507	Introduction to Databases	ITOP501
DATM508	Network Communications	ITOP501
Year 2
Trimester 4
ITOP601	Enterprise Architecture	ITOP501
PROG602	Web and mobile application development	PROG504, PROF505, DATM507
DATM603	Data Analytics for Business Applications	PROG504, DATM506, DATM507
Trimester 5
DATM604	Data Science and Visualisation	PROG504, DATM506, DATM603
DATM605	Data Structures and Algorithms	PROG504, DATM506
SYSP606	Systems Analysis and Design	ITOP501
Trimester 6
ITMG607	Integrated Systems Technology and Cloud Computing	DATM508, ITOP601, DATM603
PROF608	Contemporary Topics in IT	SYSP606
Year 3
Trimester 7
PRMG701	Project Management	PROF502, PROG602, DATM507
HCEV702	User Experience Design	PROF505, ITMG607
Elective 1 Refer to Table of Electives below
Trimester 8
DATM703	Digitisation and Big Data	DATM506, DATM603, ITMG607
NTAS704	Network Solutions	DATM508, SCTY503, ITMG607
PROF709	The IT Professional (Capstone Part A)	PRMG701, HCEV702 PRMG702, HCEV702 and 108 credit points
Trimester 9
PROF710	The IT Professional (Capstone Part B)	PROF709 (Part A)
Elective 2 Refer to Table of Electives below
Total	144	3
Choose two electives from the following list:
DATM751	Artificial Intelligence and Machine Learning
DATM752	Data and AI Ethics
DATM753	Adversarial Machine Learning in Large Language Models
DATM754	Natural Language Processing and Generative AI

ADMISSION REQUIREMENTS

Mode of Delivery

On Campus

We focus on learning and teaching delivery method where students will be required to engage in the subject content prior to classes and tutorials so that the face-to-face engagement can be used to develop higher-order skills such as interpersonal skills and teamwork.

On campus
A mix of online tools through the Learning Management System to subject-specific learning resources and information including submitting assessments
Out-of-class activities to engage students throughout their course.

CREDIT FOR PRIOR LEARNING

Skyline Higher Education Australia (SHEA) grants credit towards a course of study on the basis of prior learning, whether from formal studies or professional work experience to ensure that students commence study at a level appropriate to their prior learning experiences so that they are not required to repeat prior learning. For more information please visit SHEA’s Credit For Prior Learning Policy.