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
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 |
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 futures02Visions and Purposes of Education
Performative and values-embedded visions, alignment and consilience03Values, Trust and the Predictive Brain
Prediction, interoception, relationships and defensible inference04Generative AI and Large Language Models
Capabilities, educational uses, opportunities, risks and evaluation05Human Understanding in an AI World
Fluency, interpretation, transfer, judgement and responsibility06Embodied and Active Learning
Bodies, objects, spaces, social action and reflective redesign07Multimodal Evidence and Neurotechnology
Triangulation, signal interpretation, ethics and false precision08Design Thinking for Educational Futures
Empathise, define, ideate, prototype, test and evaluateIt 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.
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.
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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