Unit rationale, description and aim
To make analysts’ work faster, more efficient, and more accurate—and to enable natural-language interaction with analytics systems—organisations are increasingly adopting Artificial Intelligence (AI) and augmented analytics. Augmented analytics uses enabling technologies such as machine learning, natural-language processing, and advanced algorithms to support data preparation, insight generation, and insight explanation within analytics and Business Intelligence (BI) platforms. These capabilities extend the knowledge and practical skills of both expert and citizen data scientists.
This unit provides students with foundational knowledge of AI-enabled analytics as computerised decision-support. Students will develop practical skills in applying contemporary tools and techniques to design and implement AI-driven analytics solutions, with an emphasis on real-world analytical workflows and decision-making contexts.
The unit aims to prepare students for the era of cognitive analytics and artificial intelligence, which is reshaping how people interact with data, how systems generate value, and how organisations operate. By the end of the unit, students will be equipped to build AI-driven solutions that support decision-making for individuals, organisations, and society.
Campus offering
No unit offerings are currently available for this unit.Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Explain the principles and key technologies of aug...
Learning Outcome 01
Apply AI-driven analytical techniques to real-worl...
Learning Outcome 02
Develop an AI-powered augmented analytics solution...
Learning Outcome 03
Critically evaluate the effectiveness, limitations...
Learning Outcome 04
Content
Topics will include:
- Major AI technologies and architectures used in practice
- Business and industry applications of AI-driven analytics
- Role of AI and augmented analytics in decision-making support
- Human–AI collaboration for faster and more accurate insights
- Natural-language interaction with analytics and BI systems
- Supervised and unsupervised machine-learning techniques
- Predictive analytics pipelines, evaluation, and interpretation
- Deep learning fundamentals and cognitive computing approaches
- Knowledge-based AI systems: expert systems, recommenders, chatbots, assistants, robo-advisors
- Automated data preparation and feature engineering in augmented analytics
- Insight generation and explanation techniques for transparent analytics
- Caveats and impacts of AI: ethics, bias, privacy, governance, organisational and societal effects
Assessment strategy and rationale
A range of assessment procedures will be used to address the unit learning outcomes and develop graduate capabilities in line with university assessment requirements. Assessment 1 is an individual, scaffolded case-study task that introduces augmented analytics concepts and requires students to analyse how AI supports data preparation, insight generation, and decision making in a real organisational setting. This task builds foundational understanding and prepares students for more applied work. Assessment 2 is an individual predictive analytics portfolio in which students apply machine-learning techniques to a real-world dataset, document their workflow, and interpret results for decision support. Assessment 3 is a group project where students design, develop, and present an AI-powered augmented analytics solution for a defined business or societal problem, including an insight-explanation component and critical consideration of ethical, privacy, and organisational impacts. Together, these assessments incrementally develop conceptual, technical, and evaluative skills, and provide clear evidence of achievement across all learning outcomes.
To pass the unit, students must demonstrate achievement of every unit learning outcome, pass hurdle tasks, and obtain a minimum mark of 50% in graded units
Overview of assessments
Assessment Task 1: Case Study Analysis – A...
Assessment Task 1:
Case Study Analysis – Augmented Analytics in Context (Individual)
Students analyse a contemporary, unit-provided case study on the adoption of AI-powered augmented analytics in an organisation and submit a concise written report that explains the problem context, identifies where AI supports data preparation/insight generation/insight explanation, and evaluates decision-making benefits and risks. To strengthen academic integrity students complete a brief in-class or live oral check-in where they justify key choices and interpretations.
Submission Type: Individual
Assessment Method: Written
Artefact: report
25%
Assessment Task 2: Machine Learning Predictive An...
Assessment Task 2: Machine Learning Predictive Analytics Portfolio
Students develop and submit a practical analytics portfolio using a real-world dataset, including data preparation, feature selection, model building with supervised/unsupervised techniques, evaluation, and a short reflective commentary on model performance and limitations. Deliverables include a reproducible notebook plus a concise report that interprets findings for a non-technical stakeholder, emphasising how automation can enhance exploratory and predictive workflows.
Submission Type: Individual
Assessment Method: Program Files + Presentation
Artefact: Program files+ + Live / Recorded with face-overlay Presentation (7 minutes) + Online Viva
30%
Assessment Task 3: Augmented Anal...
Assessment Task 3: Augmented Analytics Project
Working in small teams, students design, develop, and demonstrate an AI-powered augmented analytics solution addressing a defined business or societal problem, incorporating machine learning and/or natural-language interaction, plus an insight-explanation component. Teams submit a project report, technical artefacts (e.g., prototype, pipeline, or dashboard), and deliver a presentation showcasing the solution’s value, design decisions, and ethical/organisational considerations.
Submission Type: Group
Assessment Method: Practical Task + Written Report + Presentation
Artefact: Report + Program File + Live / Recorded with face-overlay Presentation (10 minutes) + Online Viva
45%
Learning and teaching strategy and rationale
This unit is delivered through Attendance and Online modes using a single, integrated learning and teaching strategy designed to ensure equivalent learning outcomes and a comparable learning experience for all students, while supporting diverse learning needs and maximising access.
Across both modes, learning activities are intentionally aligned to the unit learning outcomes and assessment tasks, and are underpinned by active learning, guided engagement with disciplinary knowledge, opportunities for peer interaction, and regular, timely feedback. While the mode of delivery shapes how students participate, the pedagogical intent, expectations and standards remain consistent.
In Attendance mode, students engage in weekly face-to-face classes at designated locations, supported by preparatory activities prior to workshops and opportunities for consolidation following classes. Online learning platforms are used to complement face-to-face teaching through additional resources and learning activities.
In Online mode, students engage with the same core content and learning outcomes through a combination of synchronous and asynchronous activities, including structured discussions and applied learning tasks that support learning in professional contexts.
Across both delivery modes, students should plan to commit approximately 150 hours to this unit over the semester, including participation in learning activities, independent study, readings and assessment preparation.