EDPU5003 Future Learning and Teaching
The Complete Study & Assessment Guide · Semester 2 2026EDPU5003 Overview
- Postgraduate unit
- Semester 2 2026
- University of Sydney
EDPU5003 Future Learning and Teaching is a postgraduate University of Sydney unit about evaluating and designing education under changing technological and social conditions.
- Assessed by Three tasks weighted 40%, 30% and 30%
- Core method Connect purpose, evidence and redesign
- Best preparation Practise applied, qualified educational judgement
What you get in the full EDPU5003 Study & Assessment Guide
- 8 chapters, 27 pages — every assessed topic
- 23 original practice questions
- 8 worked examples, solved the way EDPU5003 marks
- 11 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Group project presentation (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How EDPU5003 is assessed
| Component | Weight | Format |
|---|---|---|
| Group project presentation | 40% | Critical analysis of an educational activity, research-informed redesign and multimodal evaluation plan |
| In-class written assessment | 30% | One-hour supervised written assessment on the early lecture material |
| Individual poster presentation | 30% | Design Thinking applied to a postgraduate learning or developmental challenge |
What other students are asking about EDPU5003
- Where can I find lecture notes or study guides for EDPU5003 at USyd?
- The EDPU5003 study guide in AskSia Library covers Future Learning and Teaching in 8 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in EDPU5003?
- No final exam is listed in the offering this AskSia Library guide is built from; EDPU5003 is assessed by Group project presentation 40%, In-class written assessment 30%, Individual poster presentation 30%.
- What does EDPU5003 cover on visions and purposes of education?
- In AskSia Library's EDPU5003 guide, the Visions and Purposes of Education chapter explains performative and values-embedded visions, alignment and consilience.
- What does EDPU5003 cover on values, trust and the predictive brain?
- In AskSia Library's EDPU5003 guide, the Values, Trust and the Predictive Brain chapter explains prediction, interoception, relationships and defensible inference.
- What does EDPU5003 cover on generative ai and large language models?
- In AskSia Library's EDPU5003 guide, the Generative AI and Large Language Models chapter explains capabilities, educational uses, opportunities, risks and evaluation.
Current EDPU5003 dates
| Date | Item | Control |
|---|---|---|
| 4 September 2026 | In-class written assessment | Semester 2, 2026, Week 5 |
| 9, 16 and 23 October 2026 | Group presentations | Semester 2, 2026, Weeks 9 to 11 |
| 30 October 2026 | Poster presentation | Semester 2, 2026, Week 12 |
Dates are as published in the Semester 2, 2026 tutorial and weekly schedule materials. Confirm exact deadlines and submission settings in the live LMS.
What EDPU5003 covers
The unit moves from systems thinking and competing purposes of education through generative AI and human understanding, then applies embodied learning, multimodal evidence and Design Thinking to educational redesign.
Dynamic Systems Thinking
Feedback loops, causal caution and evidence for educational futuresDynamic systems thinking asks educators to explain learning through interacting conditions, feedback loops, non-linear change and development over time. It resists claims that one technology, trait or teaching move independently produced an outcome.
- Single-cause explanations and their limits
- Feedback loops in learning systems
- Correlation, causation and connective logic
- Observable and implicit influences
- Multiple data types and system boundaries
- Non-linear and emergent patterns
Visions and Purposes of Education
Performative and values-embedded visions, alignment and consilienceEducational purpose determines which methods appear sensible and which outcomes count as success. A performative vision foregrounds curriculum attainment, visible performance, standards and comparison.
- Performative visions of education
- Values-embedded learning
- Knowledge, achievement and wellbeing
- Teaching strategy as a purpose choice
- Assessment and hidden value commitments
- Consilience between competing visions
Values, Trust and the Predictive Brain
Prediction, interoception, relationships and defensible inferenceThe predictive-brain framing treats perception and learning as active, situated processes. Expectations guide attention and action, while mismatches can support updating.
- Prediction and prediction error
- Interoception and bodily state
- Trust in educational relationships
- Values as part of educational purpose
- Silence and participation feedback
- Biological, relational and institutional levels
Generative AI and Large Language Models
Capabilities, educational uses, opportunities, risks and evaluationGenerative AI creates new content from learned patterns, while a large language model generates sequences of language by estimating plausible continuations.
- Artificial intelligence and nested categories
- Generative models and LLMs
- Generation, support and analysis
- Educational opportunities and perils
- Learner agency and productive struggle
- Accuracy, bias and privacy
Human Understanding in an AI World
Fluency, interpretation, transfer, judgement and responsibilityFluent language is not the same as situated human understanding. A language model handles patterns computationally; a learner interprets through a body, history, purpose, relationship and value commitments.
- Computational language processing
- Situated human interpretation
- Fluency and its evidential limits
- Knowledge, purpose and values
- Reconstruction and transfer
- Judgement and accountable choice
Embodied and Active Learning
Bodies, objects, spaces, social action and reflective redesignEmbodied learning connects cognition with bodily action, perception, emotion and environment. Active learning is more than movement: the learner action must be meaningfully coupled to the concept.
- Embodiment and cognition
- Action coupled to concepts
- Affordances and constraints
- Objects, spaces and social relations
- Field visits and situated inquiry
- Access within core design
Multimodal Evidence and Neurotechnology
Triangulation, signal interpretation, ethics and false precisionMultimodal evidence combines sources such as talk, action, artefacts, self-report and contextual observation. Each mode has a defined evidential role and an interpretive limit.
- Claims before instruments
- Talk, action and artefact evidence
- Self-report and contextual observation
- Signal versus educational meaning
- Triangulation across modes
- Neurotechnology and false precision
Design Thinking for Educational Futures
Empathise, define, ideate, prototype, test and evaluateDesign Thinking turns a concern into an iterative, learner-centred redesign through Empathise, Define, Ideate, Prototype, Test and Evaluate. The process does not replace educational theory or evidence.
- Empathy without stereotyping
- Learner-centred problem definition
- Educational theory in design
- Divergent ideation and selection
- Prototypes as assumption tests
- Testing use and learning
It begins with dynamic systems thinking, which resists single-cause explanations and asks how learner histories, tasks, relationships, institutions, technologies and environments form feedback loops. The unit then compares performative and values-embedded visions of education. That comparison matters because every teaching strategy and assessment method carries a view of worthwhile learning.
The early lectures also connect values, trust and the predictive brain to learning, prompting careful discussion of embodiment, relationship and the limits of machine analogies. Generative AI and large language models are considered through their educational uses, opportunities and perils, while the later AI material distinguishes computational language handling from situated human understanding.
The applied half of the unit turns to embodied and active learning, multimodal evidence, neurotechnology and Design Thinking. Students analyse an existing educational activity, propose a research-informed redesign and specify how learning could be evaluated without overclaiming what any one signal means.
The final design work applies Empathise, Define, Ideate, Prototype, Test and Evaluate to a significant postgraduate learning or developmental challenge. Across all tasks, the recurring standard is clear conceptualisation, relevant evidence, careful connective logic, critical reflection and a decision that remains responsive to context.
Worked example · free
Audit a claim that an AI tutor improved learning
- +1Define the learning outcome rather than treating completion as learning.
- +1Map concurrent changes, learner selection and feedback loops.
- +1Compare artefacts, transfer performance and learner accounts across relevant groups.
- +1Test alternative explanations and privacy or access effects.
- +1State a conditional decision and what evidence would revise it.
Key terms
- Dynamic systems thinking
- An approach that explains patterns through interacting conditions, feedback, non-linearity and change over time.
- Performative vision
- A view of education that foregrounds measured attainment, standards and comparison.
- Values-embedded learning
- A view that places values, relationships, belonging and wellbeing within teaching and achievement.
- Predictive brain
- A framing in which expectations guide perception and action while mismatches support updating.
- Large language model
- A generative model that produces language by estimating likely continuations from learned patterns.
- Embodied learning
- Learning understood through the interaction of cognition with bodily action, perception and environment.
- Multimodal evidence
- Evidence assembled from more than one mode, such as talk, action, artefacts and self-report.
- Neurotechnology
- Technology that measures or interacts with nervous-system activity and requires cautious educational inference.
- Design Thinking
- An iterative process of empathising, defining, ideating, prototyping, testing and evaluating.
EDPU5003 FAQ
What assessments are used in this unit?
The official structure has a 40% group project presentation, a 30% in-class written assessment and a 30% individual poster presentation. Together they total 100%. The supervised written task is not listed as a final examination.
What does dynamic systems thinking add to educational analysis?
It replaces a simple single-cause story with an account of interacting conditions, feedback and change over time. A useful systems response still discriminates: it selects the most relevant relationships and identifies evidence that could challenge the proposed mechanism.
How should I compare the two visions of education?
Define the performative and values-embedded visions fairly, show how each changes teaching and assessment, identify a risk in each and state a purposeful synthesis. Consilience means retaining valid demands, not choosing an empty midpoint.
How is generative AI treated in the course?
The course asks what generative AI and large language models are, how they are used in education and which opportunities and perils follow. Strong analysis identifies the particular learner action and evidence rather than treating AI as one undifferentiated cause.
What makes an embodied-learning redesign credible?
The redesign links bodily or material action to a target concept, considers access, includes structured reflection and proposes evidence that can reveal learning. Activity alone is not enough, and an enjoyable experience does not establish conceptual change.
How should multimodal data be interpreted?
Start with a specific educational claim, explain what each signal can contribute, consider alternative interpretations and use the least intrusive adequate method. Talk, movement, artefacts, self-report and sensor data each have limits when used alone.
What should the Design Thinking poster show?
It should make the reasoning from empathy evidence to a bounded problem definition, alternatives, a feasible prototype and an evaluation plan visible. A credible conclusion states what the prototype taught the designer and what should change next.
Questions students ask in each EDPU5003 chapter
- Why is a list of factors not yet systems thinking?
- A list becomes a systems account only when it explains relationships, timing and feedback. Select the conditions that plausibly generate the pattern and show how evidence could confirm or weaken that proposed mechanism. Chapter 01 · Dynamic Systems Thinking
- Is the performative vision simply wrong?
- No. Evidence of attainment and disciplinary knowledge can protect rigour and reveal inequity. The problem arises when the indicator becomes the whole purpose and narrows teaching to what is easiest to count. Chapter 02 · Visions and Purposes of Education
- Does trust mean avoiding difficult feedback?
- No. Trust can support demanding feedback when expectations are clear, reasons are visible and the learner is treated consistently and respectfully. The design reduces avoidable social threat rather than intellectual challenge. Chapter 03 · Values, Trust and the Predictive Brain
- Why is the label AI too broad for analysis?
- Different systems perform different functions and change learner activity in different ways. Name the capability, task, learner action and evidence instead of attributing a general effect to AI. Chapter 04 · Generative AI and Large Language Models
- Why is fluent output weak evidence of understanding?
- Fluency can be produced without the learner forming conceptual relations or being able to transfer them. Ask for reconstruction, application and reasons behind revision decisions. Chapter 05 · Human Understanding in an AI World
- Is every hands-on activity embodied learning?
- No. The bodily or material action must be connected to the target concept. Explain what the action makes perceptible and how reflection turns experience into learning. Chapter 06 · Embodied and Active Learning
- Does more data always improve an evaluation?
- No. More streams can add noise, intrusion and analytic ambiguity. Include a source only when its role in testing a named claim is clear and ethically justified. Chapter 07 · Multimodal Evidence and Neurotechnology
- Why is empathy not just one interview?
- One account can reveal an experience but cannot represent a whole group. Seek variation, examine context and avoid turning an identity category into a deficit explanation. Chapter 08 · Design Thinking for Educational Futures
How to prepare for the assessments
Revise by building a decision chain rather than memorising isolated terms. For each chapter, write the educational purpose, the central distinction, one new scenario, one alternative explanation and one form of evidence that could change your conclusion. Practise short responses that address every part of the prompt and end with a clear position.
For the group presentation, move visibly from analysis of the current educational activity to embodied-learning theory, a feasible redesign and a multimodal evaluation plan. For the poster, keep the learner need, mechanism, constraint, prototype and test aligned. Use generative AI only within the task-specific rules, protect personal information and acknowledge permitted use as required.
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