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Question register
Luke Nathan Hayes edited this page Oct 21, 2026
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Start here · Questions · Progress · People decide
Each question is a job we can work through. It explains why the answer matters, what useful work to bring back, which other choices it connects to and who needs to review it. AI assistants can investigate several areas at once; agreed choices give later work a dependable foundation.
Work labels describe preparation: open, researching, options ready, in review, answer recorded, or planned for later. A produced drawing or report is progress. Human approval is recorded separately.
AI-generated potential answers: The inline options draw on existing services, equipment, regional communities, councils, jurisdictions, businesses and plausible future concepts. Open a question for the alternatives, local differences and next checks. The user prompt and research method preserve the request; the source catalogue separates source facts from AI local-fit assessments. These options are prepared for discussion and professional review. An agreed answer is recorded through its own human decision.
The Council plan crosswalk maps the supplied management and recovery plans to these questions, with precise source pages and practical checks. Council plans and Aura integration sets out the proposed interfaces, comparison measures and approach to Council as capability develops.
| Work area | Questions | Where it helps |
|---|---|---|
| Everyday service and disaster work | 8 | Start with the jobs people need to complete and the support that makes those jobs dependable. |
| Weather-ready kiosks and places | 10 | Shape the cabinet, location, access and maintenance around real weather and the people using it. |
| Energy and backup power | 10 | Make useful power available through the required conditions, with a measured energy budget and people ready to maintain it. |
| Connections and messages | 10 | Move the right information through available paths and show clearly when a message has reached its next person. |
| Information people can trust | 6 | Keep the source, time, authority, permissions and corrections with information people act on. |
| Software and device care | 10 | Build the working parts that store, display, exchange and recover information across the network. |
| Personal Aura and learning | 6 | Help people navigate the network, keep control of personal information and learn the next practical step. |
| Place models and practice exercises | 6 | Use a digital model of a place to understand evidence, compare choices and practise demanding situations. |
| Community and operating responsibility | 6 | Work out who owns, maintains, reviews and supports the service, respecting local and cultural authority. |
| Budget and continuing support | 4 | Price the equipment and continuing work, and match suitable funding to a service people can sustain. |
| Tests and field learning | 6 | Turn ideas into checkable results through demonstrations, measurements, exercises and field experience. |
| Project connections | 8 | Define useful connections to the wider ecosystem while keeping the kiosk and resilience network moving. |
| Working together and human review | 6 | Give people and AI assistants clear jobs, connected records and a dependable way to make decisions. |
| Question | What we are working out | AI-generated potential answers and research | Work state |
|---|---|---|---|
| OPS-01 | What should the first complete version do for people each day and during a disaster, and where should its service limits sit? |
AI-generated option: Begin with an account-free local calendar, checked notices and service finding, then add a clearly explained disaster service for approved information, assistance pathways and practical learning. Agree success through completed tasks and service-owner participation. AI-generated future concept: Let a person's chosen Aura explain the same verified service record through voice, text or XR, adjusting detail to their needs. Shared public information remains available independently of personal accounts, advanced models and an internet connection. Research examples: Sunshine Coast: Disaster Hub helps Coast stay safe · Wellington Region: Community Emergency Hubs |
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| OPS-02 | Which locations and equipment roles should the first service area connect so people retain useful options when a location is unavailable? |
AI-generated option: Compare a small network across the island's settlements using everyday journeys, hazard exposure, access and shared dependencies. Give each candidate location an agreed purpose and alternate location, backed by a portable continuity kit. AI-generated future concept: Build a map that lets teams rehearse loss of a site, road or communication route. Auras could explain the remaining options, while a supervised mobile unit could provide a temporary public doorway at an agreed safe host. Research examples: Shoalhaven: Community Information Hubs Launched · Wellington Region: Community Emergency Hubs |
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| OPS-03 | What changing sequence of failures and recovery should a two-to-three-day practical exercise let people practise together? |
AI-generated option: Plan a practical exercise over two to three days, connecting grid loss, ferry interruption, communications gaps, changing weather, staff relief and recovery. Use separate storm, cyclone and fire branches, with timed challenges and observable decisions. Record what was simulated and the actual participant time. AI-generated future concept: Use an XR or desktop twin to generate branching challenges while an exercise controller authorises each change. Future AI/SI assistants could compare decisions across runs and suggest a fresh branch without turning a simulation into real operational instructions. Research examples: Managing Exercises Handbook |
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| OPS-04 | What should people be able to do in everyday, disaster, disconnected and low-energy operation, and who should authorise the changes? |
AI-generated option: Agree four visible service states: everyday, disaster, disconnected and low energy. Each state should explain available tasks, information age, connection limits, human responsibility and the next change trigger, keeping essential public guidance available throughout. AI-generated future concept: Let Aura explain why functions changed and forecast available service from measured energy and connection data. AI/SI could propose a change with supporting evidence; a narrowly delegated controller might apply only pre-agreed bounded actions and record the result. Research examples: Shoalhaven: Community Information Hubs Launched · Australia's AI Ethics Principles |
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| OPS-05 | Which help requests could an agreed receiving service handle, and how should responsibility continue if a person or connection becomes unavailable? |
AI-generated option: Offer help-request categories only where an actual receiving service agrees to handle them. Show distinct stages for saved locally, delivered, accepted, assigned and completed, with responsibility and the next contact route clear at every stage. AI-generated future concept: A personal Aura could assemble a person-approved request, track acknowledgements and help them choose another available route. A future logistics robot could receive a separately authorised delivery task after a human or delegated service accepts responsibility. Research examples: GIVIT: national disaster resources · Wellington Region: Community Emergency Hubs |
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| OPS-06 | What people, shifts, relief and qualified support would keep the service useful throughout the exercise? |
AI-generated option: Build a roster around actual availability, qualified duties, relief and handover, including periods when assistance is unavailable. Let people learn practical support roles while experienced practitioners retain tasks that require their competence and authority. AI-generated future concept: Use Aura-supported practice and XR walkthroughs to help a relief worker learn an agreed task before taking over. Robotics could handle repeatable inspection or transport work within tested limits, with a named supervisor and a practical manual fallback. Research examples: Wellington Region: Community Emergency Hubs · Managing Exercises Handbook |
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| OPS-07 | How should responsible people keep resource condition, availability, allocation and replenishment clear across disconnected locations? |
AI-generated option: Track each resource's custodian, location, usable quantity, reservations, condition and last check. Make allocations explicit, keep disputed or old counts visible and require physical confirmation before promising scarce supplies across disconnected locations. AI-generated future concept: With consent, sensors or scanned labels could help custodians update stock and condition. An AI planner could suggest replenishment and a future delivery robot could execute a bounded task after allocation, route and handover are authorised. Research examples: GIVIT: national disaster resources · Wellington Region: Community Emergency Hubs |
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| OPS-08 | How should the network guide people and continue service when a kiosk location becomes unsafe or unavailable? |
AI-generated option: Give each site a closure and continuity plan with a named decision role, last-confirmed availability and alternate public doorway. Share closure notices through remaining channels and explain uncertainty before directing people toward a replacement location. AI-generated future concept: Auras could compare signed availability reports and explain which options remain, with last-check times. A future portable robot or vehicle-based kiosk could relocate only after a competent person confirms the destination, route and operating scope. Research examples: Wellington Region: Community Emergency Hubs · Redland City Council: disasters and emergencies |
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| PHY-01 | What should the sheltered learning kiosk, fixed outdoor kiosk, portable continuity kit and replaceable field devices each do? |
AI-generated option: Consider four complementary roles: a sheltered learning doorway, a reviewed outdoor information point, a carryable continuity station and small replaceable relays or sensors. Share formats and spare modules so losing one housing still leaves useful local service. AI-generated future concept: Develop a common removable service module that identifies its housing and loads the matching power, access and weather profile. An Aura could guide an authorised swap while preserving the outgoing device's records. Research examples: How to prepare: nbn and emergency events · Rittal protective roof SZ 2475000 |
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| PHY-02 | What do we need to know about each possible site: permission to use it, flood levels, wind, rain, salt, public access, evacuation routes and getting people or parts there? |
AI-generated option: Prepare a site card before selecting a cabinet. Compare an accessible sheltered host with an exposed public position using island hazard mapping, observed drainage, salt and wind exposure, maintenance access and alternate safe destinations. Record landholder and cultural permissions separately. AI-generated future concept: Build a versioned place twin joining surveyed levels, exposure readings, access routes and maintenance history. XR walkthroughs could help residents compare proposed positions before physical mock-ups, with evidence dates visible. Research examples: Community hubs with energy backup systems · Storm tide mapping information |
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| PHY-03 | How will people of different heights, abilities and ages approach, reach, read, hear and use the kiosk in bright light, darkness and rain? |
AI-generated option: Offer one welcoming journey with readable text, reachable controls, clear paths and chosen audio assistance. Test a full-size mock-up with seated and standing users, wet hands, glare and darkness; retain a printed information surface and a useful assisted route. AI-generated future concept: Let a person temporarily bring their preferred language, reading scale or interaction method through an Aura or loan device. Keep the public controls usable independently and automatically clear session preferences afterward. Research examples: D.D.A. guide: The ins and outs of access |
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| PHY-04 | How will the computers, screens, batteries, radios and cables fit together while leaving room for people to use, clean and repair the kiosk? |
AI-generated option: Compare a public face with rear service access against separate public and technical cabinets. Give power, batteries, communications and computing defined spaces, labelled connectors and replacement clearances. Position antennas and solar only after testing access, balance, thermal behaviour and maintenance needs. AI-generated future concept: Give each tray a scanned parts passport and measured three-dimensional layout. XR instructions and a future service robot could locate the authorised replacement path while human technicians control isolation and return to service. Research examples: HOFFMAN wall-mount solar shields · Rittal protective roof SZ 2475000 |
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| PHY-05 | What weather and damage should each type keep working through, shut down safely for, or be moved away from? |
AI-generated option: Choose an explicit operating envelope for each physical type, followed by safe closure and withdrawal conditions. Treat the assembled kiosk, its openings and mounting as the test article. Separate weather operation, structural survival and availability of the information people need elsewhere. AI-generated future concept: Develop a shuttered public screen and retractable exposed accessories that enter a reviewed protective state before deteriorating conditions. An independent local controller would retain a closure notice and report whether movement actually completed. Research examples: Rittal protective roof SZ 2475000 · Storm tide mapping information |
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| PHY-06 | How will the equipment stay within its temperature and moisture limits during hot humid weather, intensive computing, energy-saving operation and smoke-related closure of vents? |
AI-generated option: Make heat and moisture measurable design inputs. Compare shaded, low-power equipment with a closed-loop cooling arrangement, including fan and cooling energy. Instrument the battery, computer, display and air spaces; practise reduced computing, closed vents and controlled restart during humid conditions. AI-generated future concept: Train a site-specific thermal model from measurements so an Aura can propose when to defer intensive AI or XR jobs. An independently tested local controller would enforce component limits if the model or network fails. Research examples: HOFFMAN wall-mount solar shields · Cerbo GX manual: configuration |
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| PHY-07 | How will separate equipment spaces, drains, cable openings and power shut-offs keep a fault in one part from spreading to people or the essential information service? |
AI-generated option: Separate faults by design: keep public controls, stored energy, power conversion and information service in reviewed compartments. Map every opening and shared cable, provide labelled isolation, and ensure one failed module can be removed while an independent notice layer explains available service. AI-generated future concept: Develop a small independently powered notice-and-status cartridge that remains readable when the main cabinet is isolated. A tested physical boundary would separate it from high-energy faults; a retained image would always carry its update time. Research examples: Permanently connected generators · Rittal protective roof SZ 2475000 |
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| PHY-08 | Which checks and repairs can authorised local people do at the kiosk, and which jobs need a specialist, a workshop or a replacement unit? |
AI-generated option: Build local repair capability around clearly bounded tasks: inspection, cleaning, checking status and swapping authorised low-voltage modules. Pair each task with isolation, tools, parts and a return-to-service check. Keep licensed electrical, structural and specialist repairs connected to confirmed qualified support. AI-generated future concept: Provide checked, versioned instructions through a local Aura with optional XR overlays. It would verify the equipment identity, show the authorised task boundary and capture observed completion, while escalation remains available when evidence differs. Research examples: Mallacoota Area Grid Storage community updates · Permanently connected generators |
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| PHY-09 | How will portable and replaceable field devices be carried, secured, watched, brought back and replaced as conditions change? |
AI-generated option: Pack portable capability as labelled modules with known weights, setup limits and a clear custodian. Practise moving the kit to an agreed shelter before conditions deteriorate, preserving records and directing people to the remaining service. Track equipment condition as carefully as location. AI-generated future concept: Explore a small cargo robot carrying standard continuity modules between approved safe locations. Begin with a supervised closed-site trial, defined payload and route limits, and a manual retrieval method before proposing any public operation. Research examples: Oyster3 GPS asset tracker · Pelican 1507 Air Case |
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| PHY-10 | What drawings, checks and test results will people review before a design moves from workshop learning to a field trial or an outdoor public installation? |
AI-generated option: Use one versioned design package for each physical type, tying drawings and exact parts to measured results. Progress from workshop mock-up to bounded field trial and then a public-installation proposal, with relevant professional comparisons, recorded differences and the responsible person's decision at each step. AI-generated future concept: Connect the physical equipment to a versioned digital twin so each replacement or altered setting prompts a targeted recheck. AI/SI could identify affected evidence and prepare a review pack while people retain the actual installation decisions. Research examples: Mallacoota Area Grid Storage community updates · D.D.A. guide: The ins and outs of access |
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| ENE-01 | How much power does every part use when starting, waiting, working and communicating, and how does that change through the day? |
AI-generated option: Start with a measured energy ledger for each device and operating state. Record startup peaks separately from daily watt-hours, including display brightness, radio transmission, cooling and conversion losses. Compare always-on rich service with an essential local service plus scheduled larger jobs. AI-generated future concept: Give each AI, XR and robotics job an estimated energy allowance and measured completion record. A local scheduler could offer a lower-energy route or defer work while reserving power for public information and request handling. Research examples: Finding new energy solutions for the town of Kalbarri · Cerbo GX manual: configuration |
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| ENE-02 | How much usable battery energy and reserve does each kiosk need for the agreed outage, including older batteries, hot or cold conditions and power-conversion losses? |
AI-generated option: Size storage from usable watt-hours and the agreed service schedule, then show reserve, ageing, temperature and conversion assumptions explicitly. Compare at least two battery sizes and a lower-power service mode. Treat a protective battery cut-off as the last boundary, with planned service reduction beforehand. AI-generated future concept: Explore independently protected storage for a modest notice and message relay, separate from intensive compute. A future module could display its measured remaining service allowance and preserve a dated notice if both supplies end. Research examples: Community hubs with energy backup systems · Lithium NG battery manual: operation |
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| ENE-03 | How much energy can the site collect and store during the chosen storm sequence, including shade, dirty or damaged panels, missed maintenance and days with no generation? |
AI-generated option: Use a time-based energy model with conservative storm sequences and measured site shading, then compare it with logged output. Include dirty panels, a damaged array, charging losses and several zero-generation days. Show both remaining service and the time needed to restore the reserve. AI-generated future concept: Develop a shared energy model that proposes charging windows across participating sites and movable storage. Future AI/SI could compare forecasts with battery observations and negotiate permitted transfers while local controllers enforce each site's reserve. Research examples: Finding new energy solutions for the town of Kalbarri · Cerbo GX manual: configuration |
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| ENE-04 | How should mains power, solar, batteries and low-voltage equipment be connected, protected and safely switched off at this site? |
AI-generated option: Ask the licensed electrical designer to define the complete supply boundary and its protection before selecting connections. Show mains, solar, storage, DC circuits and any generator changeover on one diagram, with labelled isolation and separate responsibilities for installation, inspection and authorised routine checks. AI-generated future concept: Develop a keyed, monitored low-voltage service port for reviewed compatible sources. A future controller would identify the source, enforce limits and log isolation; unknown equipment would require a new reviewed connection path. Research examples: Permanently connected generators |
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| ENE-05 | When should the kiosk change its power use, and how will it keep essential information available as battery charge and supply change? |
AI-generated option: Define normal, conserving, essential and protective-shutdown states with different entry and recovery thresholds. Keep core public information and request records on the essential budget; schedule rich displays, satellite sessions and larger AI jobs around available energy. Show the current state and its practical meaning. AI-generated future concept: Let an Aura offer alternative ways to finish a task: text locally now, audio next, or high-detail XR when energy returns. Public priorities would remain recorded and a simple local rule set would handle AI unavailability. Research examples: E Ink technology benefits · Cerbo GX manual: configuration |
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| ENE-06 | How will the kiosk start again after a flat battery, damaged stored data, repeated voltage drops or loss of a reliable clock? |
AI-generated option: Give the kiosk a recovery path that starts essential information first, checks stored records and then enables larger services. Preserve queued messages across repeated power interruptions, identify uncertain time clearly and compare a known-good recovery image with a replaceable preconfigured computer module. AI-generated future concept: Develop an independent low-power supervisor that compares expected startup stages with observed health. An Aura could explain the fault and propose the next permitted check while the supervisor controls a bounded restart policy. Research examples: Mallacoota Area Grid Storage community updates · Raspberry Pi EEPROM bootloader documentation |
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| ENE-07 | Which outside power sources could support the kiosk, who would connect them, and how will we confirm that the connection is ready when needed? |
AI-generated option: List external supplies as explicit offers with owners, usable capacity, connection method and activation evidence. Compare a precommissioned host backup circuit, compatible portable supply and planned charging rotation. Keep future microgrids or vehicle support as separate options until their equipment and authorised arrangements are demonstrated. AI-generated future concept: Explore participating building batteries and compatible vehicles as future sources through reviewed interfaces. A network coordinator could propose charging or supply rotations, with each custodian confirming availability and each site retaining its protected reserve. Research examples: Community hubs with energy backup systems · Permanently connected generators |
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| ENE-08 | What power-system checks and repairs can trained island staff carry out during ferry closure, and when will they call a qualified specialist? |
AI-generated option: Develop a local maintenance ladder with named primary and relief people: observe status, follow approved isolation, exchange permitted modules and escalate licensed work. Store illustrated instructions and compatible spares locally. Exercise the plan with ferry access closed and the usual technician absent. AI-generated future concept: Use a training twin for repeated isolation and replacement practice before touching equipment. Future robots could assist with approved lifting or inspection tasks; qualified people would confirm the task limits and return-to-service evidence. Research examples: Mallacoota Area Grid Storage community updates · Permanently connected generators |
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| ENE-09 | How will we test useful service through several days of storms and outages, including no generation, low reserve, a failed high-power computer and delayed repairs? |
AI-generated option: Prepare a 2–3-day practical test with people and equipment, using a traceable energy model to choose bench measurements and a bounded field exercise. Digital twin simulations can cover any chosen period, with simulated time and actual computer runtime recorded separately. Include zero generation, battery ageing allowances, lost cooling, delayed repair and failed high-power computing. Report delivered service, energy and temperature, keeping model results separate from measured performance. AI-generated future concept: Let AI/SI search the energy model for combinations that exhaust reserve early, then have people select meaningful physical tests. Future site twins could compare actual storms with those scenarios and suggest focused design improvements. Research examples: Mallacoota Area Grid Storage community updates · Cerbo GX manual: configuration |
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| ENE-10 | What checks, drawings and training records will accompany each power installation and later equipment change? |
AI-generated option: Create a commissioning passport tying each installation to its exact parts, firmware, wiring, settings and observed tests. Include usable reserve, qualified inspection, local training, maintenance ownership and exceptions. Every relevant equipment change should identify which checks need repeating and who decides the next operating scope. AI-generated future concept: Scan a replacement module to let an Aura prepare affected drawings, energy calculations and retest tasks. The records would preserve the old edition and require the responsible person's recorded release for the changed operating scope. Research examples: Community hubs with energy backup systems · Cerbo GX manual: configuration |
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| COM-01 | What information must each type of connection carry, how quickly should it arrive, who can send it and how will receipt be confirmed? |
AI-generated option: Give each information class a clear journey: urgent checked notices, help requests, equipment status, maps and richer media. Set size and delay targets before choosing links. Treat saved, transported, received, accepted and acted-on as separate states, with sender authority and privacy attached to each record. AI-generated future concept: Design one signed information record with text, audio and richer spatial layers. A local Aura could choose the layer that fits available power and bandwidth while retaining common identity, issue time, meaning and receipt status. Research examples: How to prepare: nbn and emergency events · Starlink Mini specification sheet, pages 3 and 8 |
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| COM-02 | How should kiosks and devices that pass messages between them connect so each settlement can still receive useful information when a link or major device fails? |
AI-generated option: Compare one well-supported hub with several independently useful settlement nodes. Give each node local information and queued records, then add measured radio links and more than one uplink route where feasible. Include a person carrying checked updates when every electronic path is unavailable. AI-generated future concept: Explore agreed service vehicles or future cargo robots carrying encrypted update bundles between settlements. A route planner could suggest transfers while custodians confirm safe journeys and the software exposes the age of each carried record. Research examples: How to prepare: nbn and emergency events · Starlink Mini specification sheet, pages 3 and 8 |
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| COM-03 | Where should antennas, links to the wider network and cables go so they work well, can be maintained and suit the site's weather conditions? |
AI-generated option: Select antenna positions from measured paths, structural assessment and maintainable cable routes. Compare a lower protected mounting with a higher exposed option, including sky view for satellite and a reviewed withdrawal plan. Treat connectors, earthing and surge protection as part of the installation. AI-generated future concept: Develop a low-height stowable antenna arrangement with position sensing and manual recovery. A future Aura could explain the expected coverage change when stowed, based on earlier measurements rather than assuming continued high-level coverage. Research examples: How to prepare: nbn and emergency events · Class licences for radiocommunications |
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| COM-04 | What will each connection carry, and how will records wait, retry and combine after an outage while keeping different reports visible? |
AI-generated option: Use durable queues with stable record identities, visible issue times and explicit update or cancellation references. Reconnection should preserve conflicting observations for review rather than silently replacing one. Compare retry schedules and transport sizes while protecting essential traffic from media or bulk sensor uploads. AI-generated future concept: Develop an Aura-assisted planner that sends essential structured records first and requests rich supporting material later. It could highlight contradictory road observations with their sources and dates while an authorised person decides the public update. Research examples: Common Alerting Protocol Version 1.2 · Meshtastic encryption |
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| COM-05 | How will a kiosk without internet check who is authorised to issue, change or withdraw a notice, including when a device or security key has been compromised? |
AI-generated option: Separate transport encryption from authority to publish. Give each authorised issuer a bounded role and signing identity, with offline-verifiable records, expiry and a planned withdrawal path. Show uncertainty when the kiosk cannot confirm current authority, while retaining the issuer's original wording and source reference. AI-generated future concept: Explore short-lived publication delegation for an Aura acting within a named person's or organisation's mandate. The signed record would preserve the delegator, permitted subjects, expiry and review trail, with clear public labelling of assisted drafting. Research examples: Meshtastic encryption · NIST's IoT Cybersecurity Capabilities Catalog |
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| COM-06 | Which sensors will help people make a useful decision, and how will we check their accuracy, age, location, failures and permission to share? |
AI-generated option: Start with sensors tied to decisions: battery reserve, cabinet temperature, water entry and link health. Add local environmental observations only when someone can maintain, interpret and act on them. Keep measured, estimated and simulated values distinct, with location, age, calibration and fault status visible. AI-generated future concept: Develop an Aura that compares redundant sensors, model predictions and human observations, then proposes a maintenance check when they diverge. Future robotic inspection could supply another labelled observation rather than silently turning a prediction into a fact. Research examples: Oyster3 GPS asset tracker · Cerbo GX manual: configuration |
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| COM-07 | How will people find information, ask for help and check whether a message arrived when mobile service, internet or their own phone is unavailable? |
AI-generated option: Provide several routes to the same essential information: kiosk controls, local Wi-Fi on a person's device, assisted access and a dated paper notice. Keep saved requests visible through disconnection, with separate confirmation when a responsible receiving service actually accepts them and an agreed immediate-danger route. AI-generated future concept: Let a chosen personal Aura carry a minimal encrypted task between a phone, kiosk and assisted station. People without a profile or device would retain the same public information and a practical human help route. Research examples: Community hubs with energy backup systems · D.D.A. guide: The ins and outs of access |
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| COM-08 | How will people and maintainers see whether a kiosk, connection, sensor, notice or request is current, working, waiting or uncertain? |
AI-generated option: Show people what the station can do now, when its information last changed and which messages are waiting. Give maintainers a separate detail view of links, queue age, battery and faults. When time is uncertain, preserve the last trusted check and explain uncertainty plainly. AI-generated future concept: Develop an Aura view that explains dependencies in everyday words: local notices available, mainland connection waiting, next check due. XR could help maintainers trace a fault while public users receive the same essential status in text or audio. Research examples: NIST's IoT Cybersecurity Capabilities Catalog · Cerbo GX manual: configuration |
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| COM-09 | What parts, saved settings, security access, tools and trained people will let us replace a device or repair a connection while the island is isolated? |
AI-generated option: Prepare replaceable, labelled network modules with verified settings, protected recovery credentials and compatible spares. Practise restoring one failed node without the normal maintainer or internet. Record the newly installed identity and check queued messages, publisher permissions and public status before calling the service restored. AI-generated future concept: Use a local twin to practise replacement and show the technician exactly which settings differ. A future assistant could prepare the configuration and checklist, while the authorised maintainer verifies the physical device and its observed network behaviour. Research examples: How to prepare: nbn and emergency events · NIST's IoT Cybersecurity Capabilities Catalog |
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| COM-10 | What field exercises will show that messages reach the right places, at useful speeds, and recover after several things fail together? |
AI-generated option: Build a repeatable field exercise that measures useful message delivery through combined failures, rather than signal bars alone. Test busy traffic, isolated settlements, a failed hub, one-way links, satellite loss and low energy, then reconcile updates and requests with all original meanings intact. AI-generated future concept: Let AI/SI propose failure combinations and compare alternative routes in a network twin before people choose physical tests. Future robotics could carry an exercise data bundle on a supervised safe route to measure mobile continuity. Research examples: Mallacoota Area Grid Storage community updates · How to prepare: nbn and emergency events |
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| DAT-01 | Which official and service-information owners should supply each essential information type, and how should updates reach the network? |
AI-generated option: Create a source agreement for every essential information type, beginning with Redland's official disaster information and the actual transport, utility and service owners. Record update rights, checking intervals, failed checks and the approved offline copy. AI-generated future concept: Give each Aura a source-aware explanation layer that can show the original message, its age and applicable area. Future automated source checks could propose corrections or escalation while issuers retain control of their warnings. Research examples: Redland City Council: disasters and emergencies · Sunshine Coast: Disaster Hub helps Coast stay safe |
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| DAT-02 | What information should each record carry so people can understand its source, timing, current meaning and later corrections? |
AI-generated option: Give each record a stable identity, source, issue and receipt times, applicable place, status and replacement links. Show information age and clock confidence plainly, and retain corrections so people can understand what changed. AI-generated future concept: A personal Aura could explain a record's history and why an older edition is no longer current. XR views could show the same provenance beside a map, with the ordinary text record always accessible. Research examples: Common Alerting Protocol Version 1.2 · Managing records created using artificial intelligence |
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| DAT-03 | Who should prepare, review, approve, prioritise and withdraw each kind of public information? |
AI-generated option: Use a publishing table that names who prepares, checks, approves, withdraws and corrects each information category. Bind every delegation to its scope and edition, with recoverable access and a visible record of accountable overrides. AI-generated future concept: Auras and advanced AI/SI could prepare alternate formats and identify missing sources, while a permissions service restricts publication to the exact approved content and scope. Urgent delegated actions could be pre-agreed and reviewed afterwards from their full logs. Research examples: Australia's AI Ethics Principles · Common Alerting Protocol Version 1.2 |
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| DAT-04 | How should records come back together after locations reconnect, keeping corrections, cancellations and outstanding work clear? |
AI-generated option: Keep an append-only change history so disconnected updates, cancellations and requests can be brought together without silently losing meaning. Automatically combine compatible changes and present genuine conflicts to the responsible person with their source histories. AI-generated future concept: Future Auras could prepare a readable conflict comparison and propose a resolution grounded in the signed records. A twin could replay reconnection sequences to expose double allocations before software is used with real supplies or requests. Research examples: Common Alerting Protocol Version 1.2 · Managing records created using artificial intelligence |
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| DAT-05 | What information should a person choose to share for help or personal AI guidance, and how should they control it afterwards? |
AI-generated option: Collect the least information needed for the agreed help task, keep optional Aura context under the person's control and separate public records from private casework. Explain recipients, retention, correction, export and deletion before sharing. AI-generated future concept: Explore a personal Aura-held profile that releases only the selected information for a particular task, with expiring permissions and a readable sharing receipt. Test how withdrawal affects cached copies, backups and already-completed actions. Research examples: Privacy and the use of commercially available AI products |
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| DAT-06 | How should local reports, media, event changes and accessible versions move from contribution to reviewed publication? |
AI-generated option: Offer a contribution-to-publication path with source type, permission, accessible versions, named review and withdrawal. Keep unconfirmed reports clearly labelled, preserve an original alongside any AI adaptation and let contributors see the response to their input. AI-generated future concept: An Aura could draft a translation, easy-read version or XR presentation with links back to its source and human review. Cultural custodians could set audience and reuse permissions that every format and downstream assistant carries forward. Research examples: AIATSIS Research Ethics Framework · Privacy and the use of commercially available AI products |
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| SW-01 | What software arrangement will deliver the complete everyday and disaster service, with essential functions available locally and personal accounts offered by choice? |
AI-generated option: Build a local service core holding approved public information, a private request queue and a staff publishing workspace. Offer personal Aura assistance as a separate choice. Internet connections would refresh and exchange records while essential local tasks continue through a disconnection. AI-generated future concept: Develop a versioned package of public information, approved lessons and recoverable settings that another authorised hub can host. Test portability between different machines before depending on it during an outage. Research examples: Kiwix Hotspots: an offline library over a local Wi-Fi network · About Kolibri: offline learning on devices and local networks |
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| SW-02 | How will messages and requests move between kiosks, portable devices and connection points as links fail and return? |
AI-generated option: Give each message an identity, origin, edition, priority, expiry and receipt history. Save it before transmission, retry without multiplying requests, and show waiting, delivered and accepted as different states. Compare a straightforward queue with delay-tolerant networking against the same outage cases. AI-generated future concept: Explore a portable device carrying authorised message bundles between separated hubs. A later robot or vehicle could perform that journey once capability, access, energy and responsibility are demonstrated; it would retain the same expiry and receipt rules. Research examples: RFC 9171: Bundle Protocol Version 7 · ODK Collect: managing draft, ready-to-send and sent forms |
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| SW-03 | How will an approved update reliably replace an older notice, including when a kiosk is offline or an update stops halfway? |
AI-generated option: Publish complete identified notice editions, carrying their issuer, approval, validity and replacement history. Check the whole new edition before switching the screen. Explain when the last confirmed information is old, and retain cancellations so an older copy cannot quietly reappear. AI-generated future concept: Let authorised hubs exchange compact edition summaries and identify a missing cancellation. A newer claim would trigger verification against the recognised issuer; agreement among devices would provide evidence of distribution rather than publishing authority. Research examples: Common Alerting Protocol Version 1.2 · Mender Artifacts: compatible, signed and resumable software editions |
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| SW-04 | How will authorised staff sign in, hand over responsibility and recover access after a device or access key is lost? |
AI-generated option: Give staff named roles and individual access, with a practical backup and recorded shift handover. Prepare lost-device and absent-administrator recovery before deployment. Set explicit limits for offline permissions and explain when a disconnected kiosk has yet to receive an access withdrawal. AI-generated future concept: Explore a narrowly scoped temporary credential for an agreed relief shift, with a visible expiry and an independent recovery holder. Test clock uncertainty and delayed withdrawals before accepting it for sensitive actions. Research examples: NIST SP 800-63B-4: authenticator event management |
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| SW-05 | How will shared equipment keep public information, private assistance, staff work and a person's optional Aura separate? |
AI-generated option: Provide separate public, private-visitor and staff workspaces, with a clear session exit and tested removal of temporary data. A person could keep their Aura on their own device or open a chosen temporary session. Public screens would use only material approved for publication. AI-generated future concept: Explore a visitor-approved continuation token that moves a task from public touch screen to private phone, headset or assisted room. Show exactly what travels and expire the token without leaving a reusable public-session identity. Research examples: Solid FAQ: personal data sharing, storage and copies · Web Content Accessibility Guidelines 2.2 |
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| SW-06 | How will a person's request for supplies or assistance reach a responsible relief operator and show what has actually happened? |
AI-generated option: Create a private request with an understandable receipt and separate milestones for saved, sent, received, accepted, handed over and closed. The agreed receiving service would own acceptance and escalation. A waiting request would explain current contact options and its last confirmed status. AI-generated future concept: Explore a personal Aura tracking an approved request's receipts and preparing a reminder or alternate contact proposal. People would retain the choice to disclose more information or approve a changed recipient. Research examples: ODK Collect: managing draft, ready-to-send and sent forms · RFC 9171: Bundle Protocol Version 7 |
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| SW-07 | Which equipment arrangements will deliver the essential service within measured power, storage, heat, accessibility and response-time limits? |
AI-generated option: Compare a compact local-information computer with a more capable protected mini-PC using identical tasks and measured energy, heat and response. Size optional AI work separately. Retain the tested public information and request functions as the system changes its energy settings. AI-generated future concept: Trial an arrangement where the kiosk requests optional richer Aura processing from a local shared computer when power and connection permit. Public information remains local; the trial would measure context permissions, delays and energy savings. Research examples: Kiwix Hotspots: an offline library over a local Wi-Fi network · Web Content Accessibility Guidelines 2.2 |
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| SW-08 | How will the island's kiosks and portable equipment stay connected through the routes available at each proposed site? |
AI-generated option: Map each site's actual local and outside connections, then assign routes for small urgent notices, private requests and larger learning material. Keep a working local doorway at separated sites. Choose relay positions and fallback journeys from measured trials and agreed access. AI-generated future concept: Investigate a movable relay carried by an authorised person, vehicle or future robot when a fixed link is lost. Trial safe positions, battery use and actual traffic capacity before including it in the service promise. Research examples: Robots to the rescue in hazardous environments · RFC 9171: Bundle Protocol Version 7 |
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| SW-09 | How can a trained relief operator restore, replace or update a kiosk while the usual maintainer, internet and freight are unavailable? |
AI-generated option: Prepare a labelled recovery kit with a known-working software edition, compatible spares, approved settings, protected backups and illustrated instructions. Rehearse restoration with a relief operator. Record which visitor records survive, the time taken and when qualified support is needed. AI-generated future concept: Develop optional overlays pointing to labelled replaceable parts, with the same approved lesson available as text, pictures and audio. Test whether the overlay helps an unfamiliar operator while maintaining qualified-work boundaries. Research examples: Mender Artifacts: compatible, signed and resumable software editions · About Kolibri: offline learning on devices and local networks |
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| SW-10 | What equipment-health and activity record will help staff spot a quiet failure and understand what happened during an outage? |
AI-generated option: Show staff the last successful check of each essential function, its age and what remains unknown. Combine power, link, storage and clock observations with request and notice status. Preserve a local event history that helps the next operator diagnose an outage without exposing visitors. AI-generated future concept: Explore an assistant that summarises changed conditions and proposes the next permitted checks. Its brief would link original observations and uncertainty; staff would confirm interpretation and any delegated maintenance action. Research examples: OGC SensorThings API Standard · NIST AI Risk Management Framework 1.0: core functions |
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| AUR-01 | What useful navigation can a first-time visitor receive from a personal Aura, the proposed AI assistant, while choosing how much personal context to share? |
AI-generated option: Offer guest navigation first, with an optional personal Aura helping someone find notices, services and the next useful step. Let the person choose the context shared for that task. Show the source, its age and a clear path to human assistance and session exit. AI-generated future concept: Explore an Aura explaining the same task through speech, text or XR using the person's chosen context. Evaluate comprehension, privacy, permissions and energy before claiming richer interaction improves the experience. Research examples: Web Content Accessibility Guidelines 2.2 · Kiwix Hotspots: an offline library over a local Wi-Fi network |
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| AUR-02 | How will the light-to-ultra-high-definition Aura journey be described in terms people can understand and compare? |
AI-generated option: Describe Aura's proposed light-to-ultra-high-definition levels through the context a person chooses, tasks supported and evidence of useful results. Compare them on shared examples. Display quality, computing location and emergency energy settings would each remain separate choices, with GAJRA Earth as the values starting point. AI-generated future concept: Explore an ultra-high-definition Aura combining personally chosen history, simulations and specialist agents, with inspectable sources and reversible sharing choices. Treat advanced AI/SI performance as a hypothesis measured for each task and model edition. Research examples: llama.cpp: local and hosted language-model inference · NIST AI Risk Management Framework 1.0: core functions |
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| AUR-03 | How will the personal Aura operating system help someone find the next useful action as everyday services, transport and warning conditions change? |
AI-generated option: Make an action card stating what the person can do, the issuing source and check time, and any confirmation still needed. The Aura could explain the card and compare options using chosen context. Official instructions and service availability would retain their actual source and status. AI-generated future concept: Explore a personal assistant comparing a ferry suspension or service closure against the person's chosen needs, then preparing a revised plan for confirmation. Show assumptions and withhold any claim that an unconfirmed alternative is available. Research examples: Translink open data: schedules and real-time feeds · Common Alerting Protocol Version 1.2 |
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| AUR-04 | How can just-in-time learning help a resident or relief operator learn the next permitted task and recognise when a qualified person is needed? |
AI-generated option: Prepare short task lessons linked to the actual equipment and approved procedure: prerequisites, demonstration, practice, check and handover. An Aura would help someone find the next permitted step and recognise when to pause for qualified assistance. Observed practice would establish usefulness. AI-generated future concept: Develop a spatial rehearsal of a permitted task, with an Aura adapting explanation to the learner's chosen needs. Compare real task performance after the lesson; simulated success would remain distinct from qualification and work permission. Research examples: About Kolibri: offline learning on devices and local networks · Web Content Accessibility Guidelines 2.2 |
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| AUR-05 | How will a person's sharing choices travel with their records and AI work between a device, local hub and outside service? |
AI-generated option: Attach understandable sharing choices to each record and derived item, naming purpose, recipient, copies, expiry and exit arrangements. Preview transfers between personal device, local hub and outside service. Track copies and AI derivatives explicitly, because withdrawing future access alone cannot recall information already received. AI-generated future concept: Explore an assistant comparing a proposed transfer with the person's recorded choices and producing a plain-language preview. Restricted cultural material would follow its custodians' actual conditions; the assistant's interpretation would remain open to review. Research examples: Solid FAQ: personal data sharing, storage and copies · NIST AI Risk Management Framework 1.0: core functions |
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| AUR-06 | How will a person decide which actions their Aura may read about, draft, propose or carry out on their behalf? |
AI-generated option: Give each Aura action a stated scope: read, draft, propose or execute. Match execution to the person's existing authorisation, the actual recipient and an expiry. Show a preview and completion receipt, with cancellation and a recorded reason when changed circumstances require a new decision. AI-generated future concept: Explore a shared task card that an Aura, advanced AI/SI or robot can interpret, with bounded tools and observable completion. Trial expired and repeated actions in simulation before allowing the card to trigger real equipment or external services. Research examples: NIST AI Risk Management Framework 1.0: core functions · NIST SP 800-63B-4: authenticator event management |
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| TWIN-01 | What dated local digital twin—a computer representation of a person, place or system—will support useful kiosk navigation and outage planning? |
AI-generated option: Begin with a dated, inspectable representation of the places and systems needed for kiosk service: access, facilities, routes and dependencies. Add personal twins only through each person's choices. Keep measurements, inferred features, gaps, permissions and corrections visible in every edition. AI-generated future concept: Explore a layered XR view through which residents can examine a route, source date and proposed improvement, then submit a correction for review. Extend detail where a practical task demonstrates the value of richer modelling. Research examples: Our Changing City: Wellington's public climate-adaptation digital installation · NSW Spatial Services: mapping and the Emergency Management Digital Twin |
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| TWIN-02 | Which demanding exercises will show how the network serves people through combined, prolonged failures? |
AI-generated option: Build a 2–3-day practical exercise with people and equipment, walking through interruptions to power, communications, ferries, supplies and available staff. Begin with storms, cyclones and fire. Digital twins can simulate any chosen length of disruption; record simulated time separately from actual computer runtime. Clearly identify practice events and record simulation, bench measurements and field demonstrations as distinct evidence. AI-generated future concept: Explore a shared scenario where people and Auras practise handovers and compare changing resource choices. Keep advanced-agent performance and simulated robot tasks as measured exercise assumptions, with readable participation available outside XR. Research examples: Common Alerting Protocol Version 1.2 · NSW Spatial Services: mapping and the Emergency Management Digital Twin |
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| TWIN-03 | What checks will show when a computer model is useful for a real decision and where its limits remain? |
AI-generated option: Give every model a purpose, input edition, assumptions, uncertainty and stated decision use. Compare it with an understandable baseline on cases held back for checking. Publish errors and useful limits alongside successful results, then seek relevant professional assessment before applying it to consequential decisions. AI-generated future concept: Explore separate Auras or advanced AI/SI agents proposing counterexamples and alternative assumptions. Keep their findings traceable, then evaluate against held-out evidence; agreement between agents would remain a research result awaiting external checking. Research examples: NIST AI Risk Management Framework 1.0: core functions · OGC SensorThings API Standard |
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| TWIN-04 | How will digital exercises show the connections between information, transport, supplies, power, equipment and available people? |
AI-generated option: Represent the dependencies behind each promised task, including qualified people, access, fuel, charging, cooling, freight and spares. Replay a failure through those connections and show the service retained. Connect measured asset observations to the scenario while preserving uncertain availability and human responsibility. AI-generated future concept: Explore a supervised robot inspection or delivery role inside the exercise, including its charger, links, maintainer and safe recovery. Compare it with a person-led alternative using measured performance when available and stated assumptions beforehand. Research examples: Robots to the rescue in hazardous environments · NSW Spatial Services: mapping and the Emergency Management Digital Twin |
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| TWIN-05 | How will people clearly recognise current operations, older saved information, historical playback and practice exercises wherever they encounter them? |
AI-generated option: Carry purpose and time labels inside the content itself: current confirmed operation, saved information, historical playback or exercise. Use separate release destinations and permissions for practice. Repeat the label in screenshots, audio, exports and XR so context survives copying or a disconnected device. AI-generated future concept: Explore a scene header and source panel that remain visible when a twin is shared or replayed. A personal Aura could explain what changed between editions while preserving the difference between observed conditions and a scenario. Research examples: Common Alerting Protocol Version 1.2 · Web Content Accessibility Guidelines 2.2 |
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| TWIN-06 | How will drills and actual outages turn people's observations into reviewed improvements to the service and its learning material? |
AI-generated option: After each drill or outage, connect people's observations with the actual event record, expected service and differences. Name a reviewer for each proposed improvement. Preserve the original evidence, approve a specific change where needed, update the lesson or design and check the result again. AI-generated future concept: Explore an assistant preparing a timeline from authorised logs and participant notes, marking uncertain ordering and conflicting accounts. People would correct the account before it informs a proposal, training record or public summary. Research examples: ODK Collect: managing draft, ready-to-send and sent forms · NIST AI Risk Management Framework 1.0: core functions |
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| GOV-01 | What operating model would let Ready S.E.T. Co-op bring people, equipment, learning and continuing service together? |
AI-generated option: Develop Ready S.E.T. Co-op through an organising group, a clear service purpose and a staged formation pathway. Compare interim hosting and co-operative models while specifying membership, assets, employment and agreements for the proposed kiosk service. AI-generated future concept: Prepare councils, teams and committees to work with Auras, XR, advanced AI/SI and robots through explicit mandates. Participation tools can grow while the rules identify actual members, voting rights, responsible people and the limits of delegated automated work. Research examples: Queensland: holding a co-operative formation meeting · Australia's AI Ethics Principles |
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| GOV-02 | How should people who would use or be affected by the service help shape it and see the result of their contribution? |
AI-generated option: Invite residents, visitors, workers, community groups and people with varied access needs to try practical examples. Keep a public response record showing what changed, what remains contested and the next opportunity to influence the design. AI-generated future concept: Use accessible XR walkthroughs and Aura-assisted participation to let people explore a proposed kiosk in their own time. Summaries should distinguish each person's contribution from AI synthesis and give them an easy way to correct representation. Research examples: Digital Performance Standard: measure service success · Privacy and the use of commercially available AI products |
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| GOV-03 | Whose cultural guidance and permission should shape each place record, story, language item, map layer or lesson? |
AI-generated option: Begin with relationships and permission from the relevant Quandamooka people for each knowledge item, place layer or lesson. Record who can decide, the agreed audience and uses, review arrangements and a practical withdrawal process. AI-generated future concept: Explore custodian-controlled permissions that accompany cultural material into Aura responses, XR scenes and learning resources. Future automated tools could check audience and reuse conditions before access, while custodians retain interpretation, release and review choices. Research examples: AIATSIS Research Ethics Framework |
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| GOV-04 | What should each host and equipment owner agree so a kiosk can be installed, used, maintained and handed over well? |
AI-generated option: Use a clear host agreement for each site and an asset record for each item. Bring access, ownership, installation, maintenance, insurance, public use, closure and exit together so responsibility can continue through changes. AI-generated future concept: A shared digital twin could show approved site layouts, equipment history and maintenance tasks to authorised workers. Future inspection robots could submit observations, while qualified people assess findings and owners decide repairs or site changes. Research examples: Shoalhaven: Community Information Hubs Launched · Wellington Region: Community Emergency Hubs |
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| GOV-05 | How should each community verify voluntary contribution and choose the reciprocal recognition it values? |
AI-generated option: Recognise one verified elapsed hour of voluntary human contribution as one C-Hour. Each community chooses reciprocal recognition; keep records correctable, count time once across projects and maintain separate records for money, paid work, machine time and outcomes. AI-generated future concept: A personal Aura could help someone keep a private contribution diary and submit only the agreed evidence. A privacy-preserving cross-project check could flag overlapping time for human correction without creating a public movement or personal-profile ledger. Research examples: Privacy and the use of commercially available AI products · Australia's AI Ethics Principles |
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| GOV-06 | How should people raise a concern, review an incident or conflict of interest, and keep the service accountable through changes? |
AI-generated option: Provide an accessible route for concerns, incident review and conflicts of interest, with a named receiver and escalation path. Record what happened, the response and improvements, while protecting private and culturally restricted information. AI-generated future concept: Offer an Aura-assisted concern form that helps a person explain an issue without forcing a personal AI profile. Advanced AI/SI could identify recurring patterns in approved de-identified records, with accountable people checking interpretations and corrective actions. Research examples: Australia's AI Ethics Principles · Privacy and the use of commercially available AI products |
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| FIN-01 | What would the whole first service cost to establish, support and replace, and how well is each estimate evidenced? |
AI-generated option: Build a dated whole-service cost inventory, including equipment, delivery, installation, staff, connectivity, content, training, insurance, maintenance and replacement. Compare options over an agreed planning period, with quotes, assumptions and the responsible payer visible. AI-generated future concept: An Aura-assisted cost twin could connect measured energy use, faults, staff effort and parts consumption to the budget. Future AI/SI could propose savings or shared purchasing while people approve commitments and verify that service quality is retained. Research examples: Shoalhaven: Community Information Hubs Launched · Digital Performance Standard: measure service success |
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| FIN-02 | What continuing income, operating budget and replacement reserves would keep the agreed service dependable? |
AI-generated option: Plan continuing service with a funded operating budget and explicit replacement reserves. Compare membership support, service agreements, donations, earned work and grants as distinct possibilities, keeping confirmed income separate from prospective sources and access commitments. AI-generated future concept: Use an Aura-supported budget dashboard to forecast replenishment and maintenance from actual service use. Future automation could reduce routine administration, with measured savings and human review before changing staffing, public access or reserve contributions. Research examples: Strengthening Rural Communities: Prepare & Recover · Digital Performance Standard: measure service success |
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| FIN-03 | Which funding opportunities fit particular parts of the service, and what evidence would an applicant or supporting organisation need? |
AI-generated option: Match separate work packages to verified funding rules and actual applicant readiness. Explore Redland community funding and FRRR resilience funding, resolve equipment eligibility questions and confirm any auspice, public benefit and co-funding arrangements before relying on them. AI-generated future concept: Aura-assisted grant research could flag changed rules and assemble a source-linked draft from approved evidence. A shared commitments register could prevent duplicate funding of the same cost while people check the application, relationships and reporting obligations. Research examples: Redland City Council: Community Support Grant · Strengthening Rural Communities: Prepare & Recover |
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| FIN-04 | What stage budget and review criteria should guide the next build and any later pilot commitments? |
AI-generated option: Fund the next learning milestone as a defined stage, with an evidence list and a spending limit. Move from design comparison to bench demonstration and a supervised pilot when each stage's responsible reviewers assess the actual results. AI-generated future concept: An AI/SI assistant could keep a live dependency and budget map that shows which evidence opens the next authorised job. Auras could explain trade-offs to participants while commitment authority remains tied to the actual person and approved scope. Research examples: Managing Exercises Handbook · Digital Performance Standard: measure service success |
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| TEST-01 | What operating conditions and measurable results should the first version demonstrate? |
AI-generated option: Agree acceptance measures around successful public tasks, information age, message handling, recovery and operator workload. Test ordinary use and disruption under stated conditions, recording actual observations and each limit that the next version should improve. AI-generated future concept: A twin could generate repeatable demand bursts while an AI evaluator compares logs with expected outcomes. XR trials could reveal crowding or confusing layouts; observed participant results and equipment measurements would remain the basis for performance claims. Research examples: Digital Performance Standard: measure service success · Managing Exercises Handbook |
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| TEST-02 | How should a candidate release demonstrate reliable local operation, safe updates and recovery from faults or a compromised account? |
AI-generated option: Prove a candidate release can start offline, reject unsuitable updates and recover to a working edition. Rehearse delayed connections, uncertain time, frozen screens and lost credentials with test accounts, recording the observed result and recovery steps. AI-generated future concept: An AI/SI test team could search for failure combinations within a bounded laboratory and propose patches for review. A future device controller could detect a frozen screen, switch to an approved fallback and log exactly why it acted. Research examples: Managing Exercises Handbook · Australia's AI Ethics Principles |
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| TEST-03 | What evidence should support each endurance and continuity claim, from simulation through bench work to a site exercise? |
AI-generated option: Support each endurance claim with its own evidence: model assumptions, bench measurements and site observations. In the practical exercise over two to three days, include changing energy, weather, staff, fuel, supplies, connectivity and recovery. Record actual test duration and participant time; battery capacity alone does not establish service continuity. AI-generated future concept: A living twin could compare measured behaviour with its forecast and show the resulting uncertainty. Advanced AI/SI could suggest the next high-value measurement, while qualified reviewers assess whether the evidence supports a particular installation or operating claim. Research examples: Shoalhaven: Community Information Hubs Launched · Managing Exercises Handbook |
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| TEST-04 | How should people demonstrate useful access, understanding and learning across ordinary, assisted and reduced-capability service? |
AI-generated option: Ask varied participants to complete useful tasks through touch, text, audio, assistance and offline service. Observe understanding, successful completion, confidence and workload, then revise the design from what people actually do and explain. AI-generated future concept: Compare ordinary and XR or Aura-assisted versions of the same task, including a person declining personal AI. Future adaptive teaching could offer just-in-time practice, with users controlling context and observers checking learning rather than only engagement. Research examples: Digital Performance Standard: measure service success · Privacy and the use of commercially available AI products |
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| TEST-05 | What evidence and decisions should bring the service to a supervised field pilot? |
AI-generated option: Prepare a supervised pilot packet connecting every readiness requirement to actual evidence and its responsible reviewer. Include host permission, engineering, staffing, information agreements, privacy, cultural conditions, maintenance and an agreed return to the previous service. AI-generated future concept: Let a shared twin and Aura-guided walkthrough explain readiness evidence to participants and reviewers. AI/SI could detect missing dependencies, while the actual qualified professionals, authorities, hosts and receiving services provide decisions within their respective responsibilities. Research examples: Managing Exercises Handbook · Australia's AI Ethics Principles |
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| TEST-06 | What results should guide the decision to grow, improve, pause or retire a capability? |
AI-generated option: Review everyday usefulness, disaster tasks, reliability, access, privacy, learning, staffing and whole-service cost together. Compare results with agreed thresholds and record the reasons to grow, improve, pause or retire each capability and support people through the change. AI-generated future concept: An AI/SI learning system could identify trends across approved evidence and simulate improvement options. Auras could explain the alternatives to participants, with decisions preserving user choice, professional assessment and the history of why a capability changed. Research examples: Digital Performance Standard: measure service success · Managing Exercises Handbook |
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| ECO-01 | How will approved calendars and notices keep everyday activity, cancellations, closures and transport changes in step across kiosks? |
AI-generated option: Give recurring events and individual dates stable identities, with local timezone, confirmation, cancellation and replacement history. Connect the Events Engine to an approved release stream for the notice network. Offline screens would explain the saved edition and remove or qualify expired information. AI-generated future concept: Explore an assistant showing an authorised organiser which linked notices, transport advice and roster entries may need review after a cancellation. It would draft updates for their actual owners and report which screens have received approved editions. Research examples: RFC 5545: iCalendar · RFC 9171: Bundle Protocol Version 7 |
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| ECO-02 | How will community media, stories and notices reach the right screens while urgent service messages take priority? |
AI-generated option: Use an approved media release record naming the creator, rights, audience, accessible versions and expiry. Everyday stories and notices would run through a defined screen schedule, with reviewed urgent-message priority. Offline copies and permission withdrawals need a clear refresh and removal process. AI-generated future concept: Explore optional XR storytelling and Aura-assisted explanations using material its rights holders and cultural custodians have approved. Keep public warnings understandable outside XR, and carry the same release identity into every presentation. Research examples: Xibo Emergency Alert widget and CAP connector · Web Content Accessibility Guidelines 2.2 |
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| ECO-03 | How will ferry, bus, road and accessible-travel updates give people current, traceable guidance? |
AI-generated option: Begin with the published island timetable and confirmed operator notices, showing their different meanings and age. Translink currently lists island schedules without real-time data. Show confirmed cancellations and contact routes; develop alternatives through actual operator confirmation and accessible-travel checks. AI-generated future concept: Explore supervised and later autonomous mobility as a separate service interface, carrying operating permission, availability, accessibility and confirmed booking states. Model power, communications and weather dependencies before adding any vehicle to the public service offer. Research examples: Translink open data: schedules and real-time feeds · NIST AI Risk Management Framework 1.0: core functions |
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| ECO-04 | How will offers, supply information and requests connect residents with responsible relief operators while respecting household privacy? |
AI-generated option: Keep an offer, checked available stock, a person's request, operator acceptance and completed handover as separate records. Share household details only with the agreed recipients. Connect Shared Table and other supply proposals through named custodians, consent, usable quantities and clearly confirmed participation. AI-generated future concept: Explore assistants preparing possible matches from consented needs and confirmed offers, then presenting them to the responsible operator. Future robot delivery would add its own permission and observed handover evidence before closing a request. Research examples: ODK Collect: managing draft, ready-to-send and sent forms · Solid FAQ: personal data sharing, storage and copies |
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| ECO-05 | What information will show that backup power and essential-service equipment are ready to use when people need them? |
AI-generated option: Use a readiness record that joins connected and commissioned equipment with recent load tests, usable capacity, replenishment, access and qualified cover. Display the last confirmation and responsible person alongside measurements. Link kiosk energy settings to these observed limits and the service tasks they support. AI-generated future concept: Explore a local model that proposes times for optional AI, media and charging around measured reserves. Any equipment-control link would have separately agreed permission, protection and qualified review before actuation. Research examples: OGC SensorThings API Standard · NIST AI Risk Management Framework 1.0: core functions |
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| ECO-06 | How can repair, making, materials and training partnerships strengthen the kiosk's equipment and skills over time? |
AI-generated option: Connect repair and learning through a task record identifying the equipment edition, fault, permitted first checks, parts, tools and qualified support. Keep compatible local spares and practised handovers. Broader making and materials projects would contribute through specific tested parts, lessons and agreed supply arrangements. AI-generated future concept: Explore a makerspace validating a replacement component and an authorised robot assisting inspection or parts movement. Compare material quality, fit, maintainability and qualified-work requirements before using a new part in an operational kiosk. Research examples: About Kolibri: offline learning on devices and local networks · Mender Artifacts: compatible, signed and resumable software editions |
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| ECO-07 | What hosting and stewardship arrangement would let Ready S.E.T. Co-op or another agreed host support a kiosk at a suitable site? |
AI-generated option: Prepare a separate host agreement for each candidate site, covering permission, ownership, hours, access, cultural relationships, upkeep, power, costs, incident roles and exit. Ready S.E.T. Co-op's proposed role would develop through formation and agreed mandates, with each host retaining its actual responsibilities. AI-generated future concept: Explore hosts offering different services such as public information, private assistance, portable relief or training. A common record would make confirmed hours, equipment and continuity arrangements discoverable, while each additional role receives its own agreement. Research examples: Our Changing City: Wellington's public climate-adaptation digital installation · NSW Spatial Services: mapping and the Emergency Management Digital Twin |
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| ECO-08 | How will a shared connection register help people see the kiosk's place in the wider ecosystem and the next useful partnership? |
AI-generated option: Maintain one readable connection register naming each project, current artefact, proposed exchange, owner, permission, offline fallback and acceptance evidence. Show how everyday events, media, mobility, supplies, energy, repair and optional Auras support the kiosk. Broader ambitions would connect through explicitly staged interfaces. AI-generated future concept: Explore Auras navigating an agreed service graph across the cultural node, Verse Box, Aura Genesis, P4A and the Sensorium. C-Hours recognise verified voluntary human hours with community-chosen reciprocity, no monetary equivalent, sale or transfer; money, paid work, machine time and outcomes remain separate. Research examples: NSW Spatial Services: mapping and the Emergency Management Digital Twin · RFC 5545: iCalendar |
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| WRK-01 | How will people and AI assistants turn a useful question into evidence, choices and a recorded human decision? |
AI-generated option: Use one traceable path from question to sources, alternatives, proposal, review, authorised work and observed result. Keep AI recommendations visibly labelled and preserve a person's actual decision, its scope and the evidence they considered. AI-generated future concept: Auras could prepare pre-reads and XR walkthroughs, while specialist AI/SI agents compare alternatives in parallel. Their contributions would retain source and task identities; councils, teams and committees would decide through their agreed human membership and delegations. Research examples: Managing records created using artificial intelligence · Australia's AI Ethics Principles |
options ready |
| WRK-02 | How will every question, drawing, test and decision keep a clear identity as the project grows? |
AI-generated option: Give questions, drawings, releases, sources, tests and decisions stable identifiers and version histories. Link them through a common register, with content fingerprints for review packets and readable explanations so future contributors can reconstruct the work. AI-generated future concept: A future Aura could follow the evidence graph from a kiosk symptom to its design decision and test history. AI/SI agents could compare editions and explain changed assumptions before proposing updates for a person's review. Research examples: Managing records created using artificial intelligence |
options ready |
| WRK-03 | How will we share jobs between AI assistants and people while keeping one clear owner for each piece of work? |
AI-generated option: Assign each job a clear owner, question IDs, file boundaries, source versions and expected result. Let people and AI assistants work in parallel, with one integration owner bringing the pieces together and a handover at each boundary. AI-generated future concept: Extend task cards to Aura, SI and robot work with explicit permissions, resource limits, supervision, stop conditions and receipts. A coordinator could schedule dependent work while actual people retain authority over new commitments and sensitive decisions. Research examples: Australia's AI Ethics Principles · Managing records created using artificial intelligence |
options ready |
| WRK-04 | How will a human decision stay connected to the exact proposal and the responsibilities of its reviewer? |
AI-generated option: Record a person's decision against the exact proposal, evidence, role and scope they reviewed. Preserve conditions, reasons, disagreement and validity, and seek a new decision when a material bound part changes. AI-generated future concept: A person's Aura could compare the packet with their stated priorities and explain changes before they decide. Future signed personal delegations could allow narrowly bounded routine actions, with revocation and a clear record of what was done. Research examples: Australia's AI Ethics Principles · Managing records created using artificial intelligence |
options ready |
| WRK-05 | How will the repository and GitHub Wiki stay in step as evidence and decisions change? |
AI-generated option: Build the public wiki from versioned source records, check links and identities, then record the exact source edition and publication result. Keep recoverable backups and make an interrupted update easy for the next person to finish. AI-generated future concept: Aura-assisted publishing could show a plain-language change summary and stage updates automatically after agreed checks. Future AI/SI maintainers could detect drift and prepare repairs, with access limited to the specific authorised publishing task. Research examples: Managing records created using artificial intelligence · Australia's AI Ethics Principles |
options ready |
| WRK-06 | How will each page welcome a person who is new to the project and learns by doing? |
AI-generated option: Explain each page through a useful everyday task, plain words and a clear next action. Invite people with varied experience to try it, then improve wording and navigation from their questions and observed results. AI-generated future concept: A personal Aura could offer voice, translation, visual explanation or XR practice at the reader's chosen depth. Future adaptive teaching could use local task outcomes to suggest learning while public text remains complete and personally unprofiled access remains available. Research examples: Digital Performance Standard: measure service success · Australia's AI Ethics Principles |
open |
Growing capability together · Planning and design underway · People decide · Repository
Aura Disaster Resilience
- Start here
- The whole system
- About and topic slugs
- First choices
- All-weather design
- Questions
- Progress
- Working together
- People decide
- Professional review
- AI assistant jobs
- Council integration
- Council plan crosswalk
- Guardian IMS and QIT Plus
- Ecosystem
- Agreed direction
- Evidence
- Source library
- AI research and prompt
- Research sources
- Useful words/glossary
- Rights and discovery
Work areas
- Everyday service and disaster work
- Weather-ready kiosks and places
- Energy and backup power
- Connections and messages
- Information people can trust
- Software and device care
- Personal Aura and learning
- Place models and practice exercises
- Community and operating responsibility
- Budget and continuing support
- Tests and field learning
- Project connections
- Working together and human review