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Knowledge to build on.

Research, standards and practical sources on knowledge, human judgement and AI. Each source explains its relevance and the limits of what you can conclude from it.

60 resources · 11 topics · EN / NL

Five places to start

A short route: understand the proposition, compare established practice and test the claims.

  1. Understand the propositionArtiProof · whitepaper 1.2
  2. Compare established practiceISO 30401 · knowledge management
  3. Examine human judgementBainbridge · Ironies of Automation
  4. Trace the provenanceW3C · PROV-O
  5. Make claims testableCOS · Registered Reports

ArtiProof · AP-WP-01 / 02 / 03 / 04 / 05

Our whitepapers

Five whitepapers, with versions and direct PDF links. The sector and society papers are available in English and Dutch. Our own research propositions are clearly labelled as ArtiProof material.

Explore the five whitepapers →All publications and earlier editionsFor the general whitepaper, NL 1.1 is an earlier edition; the new sector and society papers each have Dutch and English versions 1.0.

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10 resources

Research paperStart here

Ironies of Automation

Lisanne Bainbridge · 1983 · EN

A classic analysis of the demands automation leaves to human operators.

Why this source?

Explains why people still need to understand, intervene and recover when automation encounters an exception.

What to bear in mind

Analysis of automation published in 1983, not a direct experiment on contemporary generative AI.

Established foundation

Access may be restricted ·

Research paper

AI as an Epistemic Technology

Ramón Alvarado · 2023 · EN

A philosophical framing of AI as technology used to perform epistemic operations.

Why this source?

Helps explain why AI's role in knowledge deserves specific attention.

What to bear in mind

Philosophical analysis, not empirical evidence for ArtiProof effectiveness.

Background

Link checked ·

Research paper

Generative AI at Work

Brynjolfsson, Li & Raymond · 2025 · EN

A field study of AI assistance among customer-support agents reports productivity gains, with differences across workers.

Why this source?

A positive counterweight: AI can help spread useful practice, so knowledge loss should not be assumed.

What to bear in mind

Staggered deployment in one business setting; neither a universal productivity forecast nor validation of ArtiProof.

Research paper

Generative AI without guardrails can harm learning: Evidence from high school mathematics

Bastani et al. / PNAS · 2025 · EN

A randomised school experiment separating AI-assisted practice from subsequent unassisted performance. Tutor safeguards mitigated the adverse learning effect.

Why this source?

Makes retained capability and the design of AI assistance testable questions.

What to bear in mind

One Turkish high school and short-term maths outcomes; not a general estimate for adult workplaces. The publisher records an author-affiliation correction.

Research review

Learning Versus Performance: An Integrative Review

Soderstrom & Bjork / UCLA · 2015 · EN

Explains why immediate task performance can diverge from lasting learning.

Why this source?

Supports assessing retention and transfer, rather than assuming successful assisted work proves independent capability.

What to bear in mind

Review of learning research, not a study of generative AI or ArtiProof.

Research paper

Not all cognitive offloading is equal

Zhu et al. · 2026 · EN

Distinguishes dependent and autonomous offloading to generative AI.

Why this source?

A useful counterweight to treating all AI assistance as lost capability.

What to bear in mind

Three-wave self-report study, mostly young Chinese students; perceived capability is not demonstrated skill. Associations do not establish causation.

Counterpoint

Link checked ·

Guidance

Richt bij besluitvorming betekenisvolle menselijke tussenkomst in

Algoritmekader / Nederlandse overheid · NL

Dutch public-sector guidance on meaningful human intervention in algorithmic decision-making.

Why this source?

Useful prompts about competence, information and authority to intervene.

What to bear in mind

Jurisdiction- and context-specific guidance; a human sign-off alone does not demonstrate effective oversight.

Research paper

To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making

Buçinca, Malaya & Gajos · 2021 · EN

An experiment testing interventions that require people to think before accepting AI advice.

Why this source?

Turns human oversight into a question about actual behaviour and interface design.

What to bear in mind

A 199-participant experiment in a specific task; reduced overreliance came with usability trade-offs and unequal benefits.

Research review

When Combinations of Humans and AI Are Useful: A Systematic Review and Meta-analysis

Michelle Vaccaro, Abdullah Almaatouq & Thomas W. Malone · Nature Human Behaviour · 2024 · EN

Synthesises experiments comparing humans alone, AI alone and combined systems; outcomes vary by task and comparison.

Why this source?

Challenges the assumption that adding human involvement automatically improves AI-supported performance and reinforces the need for appropriate comparators.

What to bear in mind

The studies cover particular tasks, systems and populations. Aggregate findings are not an effect estimate for ArtiProof or every current AI system.

About this selection

We select sources that clarify a concrete knowledge question, credit existing work or offer a serious challenge. Standards, practical advice, philosophical arguments and empirical studies carry different kinds of evidence. Inclusion implies no partnership, certification or validation of ArtiProof.

KopKen inspired part of this selection. This library has its own focus on organisational knowledge, evaluation and justified reliance. KopKen →

Checking and maintenance

Link checks concern access, not the truth of every claim. The commentary is an editorial summary, not a systematic review or full methodological assessment.

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