Unit Overview

Description

AI is increasingly embedded in accounting practices and finance, influencing how information is produced, analysed, and evaluated. However, students learning AI are often through technically focused courses, leaving students with limited opportunities to understand how AI interacts with the core business functions related to accounting and finance, as well as professional standards and institutional constraints. This unit responds to this gap by providing an integrated introduction to AI that is grounded in accounting and finance applications.

The unit first introduces students to the foundations of AI and relevant data tools, exploring how AI is used across accounting contexts. The unit further explores key AI concepts and analytical approaches through applications in corporate reporting, auditing, taxation, management accounting, and financial markets. Emphasis is placed on understanding how AI supports, augments, and sometimes challenges professional judgment.

Throughout the unit, students will engage with real-world datasets and AI-enabled analytical tools to examine both structured and unstructured data, including corporate reports, sustainability disclosures, and other financial communications. The unit also foregrounds issues of reporting, governance, risk management, and ethics, enabling students to critically assess the reliability, limitations, and accountability of AI-assisted analysis. Technical prerequisites are kept to a minimum to ensure accessibility for students from diverse backgrounds, with guided exercises used to illustrate analytical workflows where needed.

By the end of the unit, students will be equipped to evaluate and apply AI tools responsibly within accounting contexts, while being well prepared for more advanced study involving artificial intelligence.

Credit
6 points
Offering
AvailabilityLocationModeFirst year of offer
Not available in 2026UWA (Perth)On-campus
Outcomes

Students are able to (1) explain the role of AI and data tools in accounting and financial market decision-making; (2) critically review the use of AI across accounting functions; (3) demonstrate the ability to analyse and apply AI-enabled tools and data analytics techniques to accounting and finance problems; (4) critically evaluate the impact of AI on current and emerging accounting practices; and (5) develop competencies to evaluate and manage risks associated with AI adoption.

Assessment

Indicative assessments in this unit are as follows: (1) in-class quizzes; (2) case study; and (3) exam. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Alex Zhang
Unit rules
Prerequisites
Successful completion of
or one level 5 6 points Unit(s) ACCT5432 Introductory Financial Accounting or equivalent
Successful completion of
or one level 5 6 points Unit(s) FINA5533 Finance Essentials or equivalent
Contact hours
3 hours per week, 12 weeks in total
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Visit the Essential Textbooks website to see if any textbooks are required for this Unit. The website is updated regularly so content may change. Students are recommended to purchase Essential Textbooks, but a limited number of copies of all Essential Textbooks are held in the Library in print, and as an ebook where possible. Recommended readings for the unit can be accessed in Unit Readings directly through the Learning Management System (LMS).
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.