CourseDefense, Military Engineering & Land Systems Applications open

Course in Ethics in Explainable AI in Military Simulation

Online 300-hour course in Ethics in Explainable AI in Military Simulation. 6 modules covering Regulation (EU) 2024/1689 (AI Act, transparency and human oversight), IEEE 7001 (transparency of autonomous systems) and SHAP. Personal tutor assigned and enrolment open.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate

Course in Ethics in Explainable AI in Military Simulation — studying from Australia

Defence is one of Australia's largest engineering employers, with major programmes in land systems, naval platforms, aircraft and integrated systems managed through the Department of Defence. Land systems engineering supports armoured vehicles, weapons and logistics equipment, often through prime contractors and local industry partners. Industry involvement is strongly encouraged through the Defence industry policy framework. Engineers work on sustainment, capability development, systems integration and safety assurance, typically in Canberra, Adelaide, Melbourne and regional defence precincts.

Fees for Australia: A$1,270 (AUD), with instalments and employer sponsorship available.

Relevant bodies in Australia

  • Department of Defence
  • Defence Science and Technology Group (DSTG)
  • Australian Defence Force (ADF)
  • Standards Australia

Typical roles

  • Defence Systems Engineer
  • Land Systems Engineer
  • Military Engineering Officer
  • Capability Sustainment Engineer

Questions from Australia

How does defence engineering recognition work in Australia?

Defence engineering in Australia is largely run through the Department of Defence and its industry partners, which set their own security clearance and qualification requirements. Engineers Australia is the professional body for accreditation. SEIUM's private qualifications are not accredited by these bodies, so recognition is for employers or the relevant body to decide.

Which Australian employers hire in defence and land systems?

Australia has defence prime contractors, the Department of Defence, and specialist land systems firms. Many of these roles require Australian security clearances, which depend on citizenship and background checks rather than course completion. Ask employers how they weigh private qualifications against experience and clearance requirements.

Is the course manageable with a job, and does it need a visa?

The programmes are 100% online and taught in English, so you can study from Australia without a student visa. Specialist courses take 300 hours, diplomas 800 and master's 1,900. An assigned technical tutor supports you, fees are payable in instalments, and employer sponsorship may be possible.

Virtual lab · try it before you enrol

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Learning outcomes

What you will be able to do

01

Explain the concepts and techniques of explainability in AI.

02

Analyse the use of intelligent agents in military simulation.

03

Apply explanation techniques to decision-making agents.

04

Evaluate the calibration of user trust in AI.

05

Identify transparency and human oversight requirements.

06

Assign responsibilities in the design of AI-based simulations.

07

Develop a simulation with explainable components.

Profile

Who it's for

  • Researcher in explainable AI who wants to specialise in Ethics in Explainable AI in Military Simulation.
  • Simulation engineer who wants to specialise in Ethics in Explainable AI in Military Simulation.
  • Human factors specialist who wants to specialise in Ethics in Explainable AI in Military Simulation.
  • Reinforcement learning engineer who wants to specialise in Ethics in Explainable AI in Military Simulation.
  • AI verification analyst who wants to specialise in Ethics in Explainable AI in Military Simulation.
  • Training environment designer who wants to specialise in Ethics in Explainable AI in Military Simulation.
  • Graduates in engineering or sciences looking to enter simulation and training.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (AI Act, transparency and human oversight)
  • IEEE 7001 (transparency of autonomous systems)
  • ISO/IEC 22989 (AI concepts and terminology)
  • STANAG 4603 (HLA simulation architectures in NATO)
  • IEEE 1516 (High Level Architecture)
  • ISO/IEC TR 29119-11 (testing of AI systems)
  • NATO Principles for the Responsible Use of AI (2021)
Syllabus

6 modules · 300 h

  1. 1.1Interpretability versus explainability
  2. 1.2Intrinsically interpretable models
  3. 1.3Post hoc explanations: LIME and SHAP
  4. 1.4Saliency maps and Grad-CAM
  5. 1.5Counterfactual explanations
  6. 1.6DARPA XAI programme
  7. 1.7ISO/IEC TR 24028 and trustworthiness
  8. 1.8Recipients of the explanation
  9. 1.9Limits of explanations
  10. 1.10Case study: comparing explanations of the same model
  1. 2.1Constructive, virtual and live simulation
  2. 2.2Computer-generated forces and AI agents
  3. 2.3Reinforcement learning in wargames
  4. 2.4Simulation for training and for planning
  5. 2.5Intelligent adversarial agents
  6. 2.6HLA and DIS interoperability
  7. 2.7Evaluation of AI agents
  8. 2.8Why explain in military simulation
  9. 2.9Risks of opaque agents
  10. 2.10Laboratory: a reinforcement learning agent in a simulation environment
  1. 3.1Explaining reinforcement learning policies
  2. 3.2Distilled decision trees
  3. 3.3Attention and explanation of sequences
  4. 3.4Causal explanations
  5. 3.5Visualising learned strategies
  6. 3.6Evaluating the quality of explanations
  7. 3.7Misleading explanations and fidelity
  8. 3.8Attacks on explanation methods
  9. 3.9Instructor cognitive load
  10. 3.10Practice: SHAP explanation of a simulation agent
  1. 4.1Calibrated trust in AI systems
  2. 4.2Automation bias and distrust
  3. 4.3XAI-supported after action review
  4. 4.4Student learning from AI
  5. 4.5Transfer of simulated strategies to the real world
  6. 4.6Assessing students using AI
  7. 4.7Fairness in assessment
  8. 4.8Transparency towards students
  9. 4.9NATO principles: explainability and traceability
  10. 4.10Case study: an explainable debrief of an exercise
  1. 5.1Explainability as a design requirement
  2. 5.2Documentation of models and scenarios
  3. 5.3Auditing simulation systems
  4. 5.4Accountability for decisions based on simulation
  5. 5.5Use of simulation in Article 36 reviews
  6. 5.6Academic research in defence XAI
  7. 5.7Publication and dual use
  8. 5.8Procurement requirements
  9. 5.9Explainability indicators
  10. 5.10Workshop: explainability requirements for a simulator
  1. 6.1Definition of the simulation scenario
  2. 6.2Development of the AI agent
  3. 6.3Selection of explanation methods
  4. 6.4Evaluation of explanation fidelity
  5. 6.5User testing
  6. 6.6Ethical and trust analysis
  7. 6.7Documentation and traceability
  8. 6.8Usage recommendations
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, making it easy to combine with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is SHAP, LIME, Python (PyTorch), Unity ML-Agents, in free, academic or evaluation versions. Tests are studied using real laboratory procedures for model explainability, simulation of decision-making agents and user trust assessments. Assessment is continuous, module by module, plus the final integrating project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-based Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, OT cyber security, IHL and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Definition of the simulation scenario

Explain the concepts and techniques of explainability in AI.

Development of the AI agent

Analyse the use of intelligent agents in military simulation.

Selection of explanation methods

Apply explanation techniques to decision-making agents.

Evaluation of explanation fidelity

Evaluate the calibration of user trust in AI.

User testing

Identify transparency and human oversight requirements.

Career opportunities

Where this takes you

AI Explainability ResearcherSimulation EngineerHuman Factors SpecialistReinforcement Learning EngineerAI Verification AnalystTraining Environment Designer
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Spanish language level sufficient to follow technical regulatory texts.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

FAQ

Frequently asked questions

The programme totals 300 hours, spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

You work with SHAP, LIME, Python (PyTorch), Unity ML-Agents. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the higher programme. Please check the recognition policy when you enrol.

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