Unit rationale, description and aim

The Industry/Applied AI Internship provides students with an opportunity to gain authentic, practice-based experience in applying artificial intelligence within professional, industry, or research contexts. Delivered fully online, the unit enables students to engage remotely with host organisations or project partners while developing applied expertise in data-driven innovation, automation, and AI system integration. 

Students will undertake a structured program of supervised work, applying technical, analytical, and ethical skills to address real-world challenges in sectors such as health, energy, education, and technology. Through reflective engagement and collaboration with industry mentors and academic supervisors, students will have the opportunity to strengthen their professional judgement, problem-solving, and communication skills. 

Aligned with ACU’s mission to advance the common good and the UN Sustainable Development Goals (SDG 9 – Industry, Innovation and Infrastructure; SDG 16 – Peace, Justice and Strong Institutions), the aim of this unit is to empower students to apply artificial intelligence responsibly through professional practice that supports flourishing lives, thriving communities, and an ethical digital future. 

2026 10

Campus offering

No unit offerings are currently available for this unit.

Prerequisites

Nil

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.

Apply artificial-intelligence knowledge and techni...

Learning Outcome 01

Apply artificial-intelligence knowledge and technical skills to practical challenges in professional or organisational settings.
Relevant Graduate Capabilities: GC2, GC8, GC9

Integrate ethical, social, legal, and industry sta...

Learning Outcome 02

Integrate ethical, social, legal, and industry standards into the design, development, and application of AI solutions across diverse domains, demonstrating awareness of Aboriginal and Torres Strait Islander perspectives, compliance frameworks, and responsible innovation practices
Relevant Graduate Capabilities: GC5, GC6

Critically analyse work-integrated experiences to ...

Learning Outcome 03

Critically analyse work-integrated experiences to evaluate how AI methods contribute to decision-making, innovation, and organisational value creation.
Relevant Graduate Capabilities: GC2, GC7

Communicate professional insights, project outcome...

Learning Outcome 04

Communicate professional insights, project outcomes, and ethical reflections clearly to both technical and non-technical audiences.
Relevant Graduate Capabilities: GC10, GC11

Reflect on personal and professional growth to ide...

Learning Outcome 05

Reflect on personal and professional growth to identify pathways for continuous learning and responsible practice in the AI profession.
Relevant Graduate Capabilities: GC1, GC3, GC6, GC12

Content

Topics will include:

  • Application of AI models, analytics, and automation to real organisational challenges 
  • Integration of human-centred, ethical, legal, and cultural design principles — including recognition of First Peoples’ perspectives on knowledge, data, and technology use 
  • Communication of AI insights through professional reports or stakeholder presentations that reflect ethical and cultural awareness 
  • Reflection on professional identity, social responsibility, and continuous learning, with consideration of diversity, equity, and Indigenous knowledges.

Assessment strategy and rationale

The assessment strategy is designed to develop advanced ethical judgment, professional leadership, and applied research capability through authentic, industry-aligned tasks. Assessments are sequenced to progress from critical analysis to applied professional practice and reflective synthesis. 

The first task requires students to produce an analytical report evaluating complex ethical and governance challenges in artificial intelligence using recognised international frameworks, fostering higher-order reasoning and academic integrity. The second task involves completing an applied AI project or internship report that demonstrates the design, implementation, and evaluation of AI methods in a professional or organisational context. The final reflective leadership portfolio integrates ethical reasoning, professional reflection, and evidence of personal growth, demonstrating the student’s ability to connect theory, practice, and professional identity. 

This combination of analytical, applied, and reflective tasks ensures students demonstrate independence, accountability, and the capacity to apply ethical theory to professional AI practice. Assessments are supported by continuous feedback and optional webinars, providing flexibility while maintaining academic rigour. 

To pass the unit, students must achieve all learning outcomes and an overall grade of 50% or higher. 

Overview of assessments

Assessment Task 1: Analytical Report – Ethical an...

Assessment Task 1: Analytical Report – Ethical and Governance Challenges in AI

Students produce an analytical report evaluating complex ethical, legal, and cultural challenges in AI by integrating international and Australian Aboriginal and Torres Strait Islander knowledges and ethical frameworks, fostering understanding of how Indigenous worldviews inform responsible governance and contribute to social change.

Weighting

30%

Learning Outcomes LO1, LO2, LO3
Graduate Capabilities GC2, GC5, GC8, GC9

Assessment Task 2: Applied Internship/Project Rep...

Assessment Task 2: Applied Internship/Project Report

Students complete an applied AI project or internship report demonstrating the design, implementation, and evaluation of AI methods in a professional or organisational context, drawing on insights from Aboriginal and Torres Strait Islander experiences and knowledge systems, demonstrating ethical collaboration and respect when designing or implementing AI solutions. 

Weighting

40%

Learning Outcomes LO2, LO3, LO4, LO5
Graduate Capabilities GC1, GC2, GC5, GC6, GC10, GC11

Assessment Task 3: Reflective Leadership Portfoli...

Assessment Task 3: Reflective Leadership Portfolio

Students develop a professional portfolio integrating reflective analysis, ethical reasoning, and evidence of leadership growth. The portfolio demonstrates the ability to connect theory, practice, and personal development in responsible AI practice. 

Weighting

30%

Learning Outcomes LO1, LO3, LO4, LO5
Graduate Capabilities GC6, GC11, GC12

Learning and teaching strategy and rationale

This unit adopts a work-integrated learning (WIL) approach that bridges academic theory with professional AI practice in an online learning environment. Students undertake a virtual internship or applied AI project in collaboration with industry partners, supported by joint supervision from an academic advisor and an industry mentor. 

Learning is facilitated through online professional engagement, reflective journaling, and continuous feedback that connects technical experience with ethical, cultural, and strategic considerations. The approach promotes professional readiness, critical reflection, and responsible innovation, enabling students to address real-world challenges while upholding ethical and professional standards. 

Through these experiences, students develop the ability to integrate technical, social, organisational, and cultural perspectives including First Peoples’ perspectives into the design and deployment of AI solutions. The unit reflects ACU’s commitment to human-centred, socially responsible innovation and aligns with the UN Sustainable Development Goals (SDG 9 – Industry, Innovation and Infrastructure; SDG 17 – Partnerships for the Goals). 

Representative texts and references

Representative texts and references

AIATSIS Code of Ethics for Aboriginal and Torres Strait Islander Research (2020) https://aiatsis.gov.au/research/ethical-research/aiatsis-code-ethics

Bura, C., & Myakala, P. K. (2024). Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation. arXiv. https://arxiv.org/abs/2411.15971

CARE Principles for Indigenous Data Governance (GIDA) https://www.gida-global.org/care

Carvão, P. (2024). The Dual Imperative: Innovation and Regulation in the AI Era. arXiv. https://arxiv.org/abs/2407.12690

Eacersall, D., Pretorius, L., Smirnov, I., et al. (2024). Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use. arXiv. https://arxiv.org/abs/2501.09021

Engaging Global Perspectives in AI | Microsoft https://www.microsoft.com/en-us/security/business/solutions/security-for-ai?msockid=0a3b30f5f88b6061226e245bf9b96140 

Google. (2024). Responsible AI: Our 2024 Report and Ongoing Work. https://blog.google/technology/ai/responsible-ai-2024-report-ongoing-work/

Indigenous Protocol and Artificial Intelligence Position Paper (2020) https://www.indigenous-ai.net/position-paper

International Telecommunication Union (ITU). (2024). Responsible AI – 2024 Review. https://www.itu.int/osg/year-in-review-2024/shaping-transformation/responsible-ai/ITU

KPMG US. (2024). Driving Responsible Innovation: Reflections on a Year of AI Governance. https://kpmg.com/us/en/articles/2024/driving-responsible-innovation-reflections-ai-governance.html KPMG

Lowitja Institute – Indigenous Data Sovereignty & Governance Resources https://www.lowitja.org.au/page/services/resources/data-governance

Maiam nayri Wingara – Indigenous Data Sovereignty Principles (Australia) https://www.maiamnayriwingara.org/

ModelOp & CDO Magazine. (2024). Responsible AI Benchmark Report 2024. https://www.modelop.com/resources-ebooks/responsible-ai-report-2024 modelop.com

Perera, H., Lee, S. U., Liu, Y., Xia, B., et al. (2024). Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagement. arXiv. https://arxiv.org/abs/2409.10520 arXiv

Security for AI | Microsoft Security https://www.microsoft.com/en-us/security/business/solutions/security-for-ai?msockid=0a3b30f5f88b6061226e245bf9b96140 

Šekrst, K., McHugh, J., & Rodriguez Cefalu, J. (2024). AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development. arXiv. https://arxiv.org/abs/2411.14442 arXiv

Stanford HAI. (2024). The 2024 AI Index Report. https://hai.stanford.edu/ai-index/2024-ai-index-report/ Stanford HAI

Tools and Weapons with Brad Smith - Podcast - Apple Podcasts 

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