System boundary
Define what “Education, skills and lifelong AI literacy” includes, what it excludes and which people, tools, organisations or physical conditions form part of the operating system.
Open depth page →
Research agenda
Education, skills and lifelong AI literacy
A practical research brief for turning “Education, skills and lifelong AI literacy” into an evidence-led programme rather than a broad aspiration.Structured research briefEvidence developingReviewed 12 August 2026
Question & scope
Make the topic researchable before making it persuasive.
We study how people and intelligent systems share judgement, creativity and responsibility in work, education, public services and everyday decision-making.
This agenda topic is treated as a programme of work: define the system, identify dependencies, select evidence, test failure and recovery, and record the limits of every conclusion.
Reader outcome
A reader should leave with a defined system boundary, a usable evidence plan, visible failure conditions and a clear distinction between what is supported and what remains unknown.
Evaluation map
Examine the evidence, failure conditions and decisions behind the question.
Capability, evidence, failure and responsibility must be examined together.
Evidence threshold
Prefer direct experiments, operational records, standards and attributable primary sources. Current reference anchors include OECD, Stanford HAI, Oxford Internet Institute.
Open depth page →Failure and recovery
Test not only nominal performance but ambiguity, changing conditions, misuse, dependency failure, human intervention and the route back to a safe state.
Open depth page →Decision relevance
Connect findings to the field’s central proposition: The future of work is a design problem, not a substitution slogan. State what a reader can decide now and what remains premature.
Open depth page →Evidence anchors
Start from attributable public work.
These independent sources inform the research frame. Their inclusion does not imply collaboration, review or endorsement.
01OECD
Generative AI and the SME Workforce
Evidence on adoption, skills gaps and changing workforce needs.
Open canonical source ↗02Stanford HAIAI Index — Economy and work
A comparative evidence base for adoption and labour-market change.
Open canonical source ↗03Oxford Internet InstituteDigital society research
Interdisciplinary work on digital economies, wellbeing and inequality.
Open canonical source ↗Research protocol
Five steps from question to reviewable record.
The protocol is designed for maintained research: conclusions can strengthen, narrow or change when new evidence arrives.
- 01
State the claim, unit of analysis and operating context before selecting evidence.
- 02
Collect attributable primary sources and distinguish direct evidence from adjacent analogy.
- 03
Compare supporting and disconfirming results, including negative and null findings.
- 04
Test boundary conditions, recovery paths and human or institutional responsibility.
- 05
Publish the remaining unknowns, review triggers and reasons a conclusion may change.
FUURAA analysis
Judge the operating system, not the headline.
FUURAA’s current view is that “Education, skills and lifelong AI literacy” should be judged as a system property, not a headline capability. Progress becomes meaningful when claims can be tied to operating conditions, observable evidence, accountable human decisions and recovery when assumptions fail. The strongest next step is therefore not a larger claim, but a better-defined evaluation that another team can inspect and repeat.
Applicable boundaries
- A research brief is not a FUURAA product announcement, deployment claim or partnership statement.
- A source may inform the question without endorsing FUURAA’s interpretation.
- Evidence from one benchmark, laboratory or jurisdiction should not be generalised without testing transfer.
FUURAA Frontier Research™