Overview

This unit introduces students to Artificial Intelligence (AI) as a driver of business transformation, equipping them with practical skills and the conceptual frameworks needed to engage effectively with emerging AI technologies. Students will develop practical skills with Generative AI and Agentic AI systems such as chatbots, digital assistants, and decision-support agents to innovate business workflows and customer experiences. Using accessible no-code platforms, students will learn to model logic flows, design decision processes, and iteratively prototype AI-enabled business solutions. The unit emphasises prompt engineering as a structured design practice, incorporating abstraction, pattern recognition, and contextual adaptation. Ethical considerations including automation bias, explainability, and responsible deployment are explicitly addressed through structured ethical scenarios and experiential activities, ensuring safe and responsible exposure to AI tools.

Requisites

Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Semester 1
Location
Hawthorn
Start and end dates
02-March-2026
31-May-2026
Last self-enrolment date
15-March-2026
Census date
31-March-2026
Last withdraw without fail date
21-April-2026
Results released date
07-July-2026
Semester 2
Location
Hawthorn
Start and end dates
03-August-2026
01-November-2026
Last self-enrolment date
16-August-2026
Census date
01-September-2026
Last withdraw without fail date
22-September-2026
Results released date
08-December-2026

Unit learning outcomes

Students who successfully complete this unit will be able to:

  1. Apply structured reasoning to design effective prompts for Generative AI tools within industry-relevant business scenarios
  2. Model, evaluate, and iteratively refine logic flows for Agentic AI systems using guided design frameworks
  3. Evaluate the outputs and behaviours of GenAI and Agentic AI systems, addressing issues such as purpose, bias, reliability, and user impact
  4. Develop and justify an AI-enabled business solution that addresses a transformation opportunity in a business workflow or customer experience
  5. Demonstrate awareness of ethical risks in AI use (e.g. automation bias, explainability limitations) and incorporate responsible design principles in their work

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)

On-campus

Class 1

2.00 12 weeks 24

Online

Lecture (asynchronous)

1.00 12 weeks 12

Unspecified Activities 

Independent Learning

9.5 12 weeks 114
Total     150

Assessment

Type Task Weighting ULOs
Assignment 1 Individual 30-40% 1,3,4
Assignment 2 Group 30-50% 2,4,5
Assignment 3 Individual 20-30% 3,4,5

Content

  • Introduction to Artificial Intelligence in business transformation Generative AI, prompt engineering, and iterative design principles
  • Agentic AI: Defining AI agents and exploring industry-specific applications
  • Design thinking methodologies explicitly aligned with AI-powered solutions
  • No-code/low-code chatbot development tools with structured iterative prototyping
  • Ethical case studies, scenario-based learning, and responsible AI deployment practices
  • Structured reflective practices, peer review, and iterative improvement
  • Effective presentation and communication strategies for AI-enabled solutions
  • Career-focused exploration: exploration of AI-related career pathways (e.g., Digital Transformation Consultant, AI Strategist, AI Project Manager), integrated into final showcase and reflective activities

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.