Sessions
All sessions run on Day One as extended three-hour workshops across two blocks. Choose one and stay with it — the format is designed for depth. Day Two’s Open Gallery lets you revisit any session you missed.
Sessions at a glance
AI & Tools Track
Sessions in this track are workshop-style and practical. You will leave with something you built, configured, or tested yourself.
Build Your Own AI Research Assistant
Daniel
You don’t need to be a developer to build a tool that works for your research. This session walks you through designing and deploying a simple AI assistant tailored to your own workflow — literature review, data coding, writing support, or something else entirely.
Deploy Your AI Agent: From Prototype to Practice
Vita
Building an AI agent is one thing. Getting it to work reliably in a real research or teaching context is another. This session covers the practical decisions that sit between a proof-of-concept and something you can actually use — and trust.
Evaluating AI Agents: How Do You Know It’s Working?
Srecko
If you’re using an AI agent in your research or practice, how do you know it’s doing what you think it’s doing? This session introduces evaluation frameworks for AI agents — from basic output checks to more systematic approaches for assessing quality, consistency, and alignment with your goals.
Coding Qualitative Data with AI: Faster, Smarter, Still Rigorous
Ryan & Linxuan
AI tools are changing how qualitative researchers work with data — but not always in the ways people expect. This session examines what AI can and can’t do in qualitative coding, with hands-on practice and an honest discussion about rigour, transparency, and what you still need to do yourself.
Learning Sciences, Data & Methods Track
Sessions in this track are substantive and research-facing. They address conceptual and methodological questions at the heart of learning sciences and educational research.
The State of Qualitative Research: Perspectives from CRESI
CRESI
An overview of current directions in qualitative educational research, drawing on the work and expertise of CRESI researchers. What questions are driving the field? What methods are gaining traction? What debates are worth paying attention to?
Multimodal Data in the Classroom: What to Capture and Why
Andrew
Classrooms generate more data than ever — physiological signals, log data, discourse, video. But more data is not always better data. This session examines the decisions that matter: what to collect, how to integrate across modalities, and how to stay grounded in the learning question you’re actually trying to answer.
Leading in Academia: Building Partnerships with Schools and Government
Maria, Bec & Shane
Research that matters usually involves partners outside the university. This session draws on direct experience of building and sustaining partnerships with schools and government — what works, what doesn’t, and what you need to put in place before you start.
Tracing Self-Regulation: Analysing Process Data in Learning Research
Flora
Self-regulated learning is one of the most studied constructs in educational psychology — and one of the hardest to measure well. This session focuses on process data approaches: how to move beyond self-report, what temporal and sequential analyses can reveal, and what the data can and cannot tell you about how learners actually regulate.
Designing Your Research Instrument: From Construct to Question
Phuong & Oscar
A research instrument is only as good as the thinking behind it. This session walks through the process of moving from a theoretical construct to a set of items or measures that actually capture what you intend — with attention to common failure points and how to avoid them.
What Are We Actually Measuring? Validity, Constructs, and the Hard Problem of Learning Data
Flo & Abhinava
Measurement in education is harder than it looks. This session takes seriously the question of what it means to measure learning, engagement, anxiety, or wellbeing — examining construct validity, the gap between what we measure and what we care about, and what good measurement practice looks like in practice.
Data Ethics in Education: Consent, Privacy, and the Datafication of Learning
TBC
As education becomes more data-rich, the ethical stakes rise. This session examines the practical and philosophical dimensions of data ethics in educational research — informed consent, data sovereignty, the risks of surveillance, and what it means to do data-intensive research responsibly.
Mixed-Effects Modelling for Education Researchers: A Practical Introduction
TBC
Educational data is nested — students within classrooms, classrooms within schools, measurements within students. Mixed-effects models are built for exactly this structure. This session introduces the core ideas, works through applied examples using real data, and leaves you with enough to get started in your own analysis.