Good questions deserve clear answers.

Lurna is a mastery-led learning product for Australian primary school children. It focuses practice on specific skills and gives parents a clear view of what their child is working on.

Lurna uses the child's responses and the prerequisite skills behind each topic to choose an appropriate next step. A skill is revisited when the evidence shows that more practice would help.

Lurna organises learning around Australian Curriculum skills. Early access will begin with selected primary mathematics content and expand carefully as each area is reviewed.

Short, focused practice is the aim. The right routine depends on your child, and parents should favour consistency and confidence over long sessions.

Mastery means a child has shown reliable understanding of a skill, not simply completed a set amount of time or a single activity. Lurna uses that evidence to decide when to move forward.

Parents will see the current focus, demonstrated strengths, skills that need more support, and useful next steps. The goal is clarity without turning learning into a scoreboard.

Lurna is designed to collect only the information needed to provide and improve the learning experience. Parent visibility and clear explanations of data use are part of the product design.

Lurna may use AI to support learning features, but it should remain clearly identified as software, use task-focused language, and operate within parent-visible safeguards.

The planned parent controls let families choose how much reward feedback is visible while keeping the same underlying learning standard.

Apply with a parent name and email. We will contact families as suitable early-access places become available and explain what is included before they begin.

Learning content is organised by curriculum skill and prerequisite relationships. Coverage is expanded only as content and assessment behaviour are reviewed.

No. Lurna is intended to provide focused practice and clearer learning evidence, while teachers retain responsibility for instruction, context, and professional judgement.

Lurna looks at response evidence across a skill and its prerequisites. This helps distinguish a current-topic error from an earlier building block that may need support.

Completion records that an activity ended. Mastery asks whether the learner has demonstrated dependable understanding across suitable opportunities before progressing.

Teacher-facing features are still being developed. The intended direction is concise skill-level evidence that can support, rather than replace, teacher judgement.

The product is being designed around data minimisation, clear AI identity, task-focused interactions, appropriate access controls, and visibility for the adults responsible for a child.