PROJECT

Tokotoko — Te Reo Language Learning Application

DATE

2024—2025

ROLE

Design, development, research

TYPE

Language learning app, UX design, research

Tokotoko language learning application

Tokotoko is a Māori language learning platform that reimagines how people engage with Te Reo Māori through interactive, story-driven design. The project combines UX research, linguistic pedagogy, and data-driven feedback systems to create an app that prioritises cultural context, learner agency, and visual clarity. I collaborated on the concept design, UX structure, and visual communication system.

The name Tokotoko refers to the carved orator's staff—a symbol of speaking authority—aligning with the project's aim to empower learners to speak confidently and respectfully. Tokotoko moves beyond conventional flashcard or gamified models by embedding Te Reo within cultural narratives, idiomatic expressions, and everyday conversational settings. Each learning sequence is anchored by short, situational dialogues that integrate audio, transcription, and contextual explanations.

The interface emphasises clarity and immersion. Lessons appear as scrollable "story threads" combining text, audio, and imagery, with gesture-based interactions to reveal translations or grammar notes. A minimal palette and typographic hierarchy support accessibility in both light and dark modes. The app also features a kōrero view, where learners can record and compare their speech with native pronunciation.

  • Co-designer and researcher
  • Developed UI prototypes and design system
  • Structured the learning flow and interface components
  • Supported backend planning for progress tracking and vocabulary database

Discovery. Cultural probe kit, diaries, and photo prompts to capture daily language touchpoints. UX surveys and interviews to prioritise barriers and motivators.

Prototype. Interactive transcript player, vocabulary capture, and spaced repetition. Early playlists and difficulty tagging. Low-latency UI tests on mobile.

Evaluate. Usability tests on comprehension, retention, and affect. Compare playlist vs. algorithmic feed. Measure time-on-task and return rate.

Iterate. Refine familiarity model, add social features, and extend content taxonomy for contexts, registers, and domains.

A lightweight web stack optimised for media and annotation. Focus on transparent data models and exportability.

  • Web app: Next.js + TypeScript
  • Transcripts: WebVTT with word-level timing and bilingual alignment
  • Storage: Postgres (Supabase) with RLS for user data control
  • Review engine: spaced repetition with item difficulty and lapse handling
  • Analytics: privacy-preserving event pipeline for learning metrics
  • Exports: CSV/JSON and Anki-ready decks

Dashboards focus on comprehension and exposure rather than vanity scores.

  • Familiarity graph: tracks coverage of high-frequency vocab and multiword phrases
  • Context heatmap: domains used this week (home, mahi, kai, travel)
  • Retention bands: new, learning, mature, and due items
  • Journey map: playlist paths and branching choices

Tokotoko serves as both a pedagogical and cultural tool, encouraging engagement with Māori language revitalisation through design. The platform is being developed in collaboration with educators and linguists to ensure linguistic accuracy and cultural integrity, with future plans to expand into an open-source learning framework for indigenous languages.

  • Next.js, TypeScript
  • Postgres/Supabase
  • WebVTT, FFmpeg
  • Figma for UX
  • Python notebooks for analysis