Process-Constituted Intelligence: A Shared Criterion for Humans and Machines

Automation Without Understanding

The essay makes the case for treating mathematical capacity as a strategic asset on a par with semiconductor capability and proposes, among other measures, that AI systems performing consequential reasoning be required to expose their decision-critical claims in formal, machine-checkable form.

On (artificial) intelligence: from a concept of measurable human ability to automated evaluative order

This article argues that intelligence should not be understood as a concrete and delimited phenomenon or as a natural kind, but as a historically and culturally produced concept through which selected behaviors, capacities, and performances come to be classified as intelligent. It proposes to shift the question from what intelligence “really is” to how certain performances come to count as intelligent, how they are made measurable, and how those measurements acquire social, political, and technical force. The article’s contribution is to connect the historical operationalization of intelligence with the evaluative infrastructures through which AI reproduces and transforms it. Tracing a genealogy from modern rationality, psychometrics, and standardized testing to artificial intelligence, the article shows how intelligence became actionable through classification, comparison, measurement, and evaluation. Once test scores shaped access to education, credentials, work, and social recognition, measurement no longer merely described ability; it became part of the institutional conditions through which ability was recognized, rewarded, and made socially consequential. Testing thus helped produce feedback loops in which measured intelligence partly reflected the opportunities that testing itself had helped allocate. Intelligence was then linked to merit, qualification, and deservingness, allowing historically contingent criteria of achievement and selection to appear not as products of unequal opportunity, but as natural differences in ability. The article then shows how artificial intelligence inherits this operational history. AI became possible once intelligence had already been reformulated as performance that could be formalized, evaluated, and reproduced apart from the living human subject. Cybernetics, information theory, and early AI translated this conception into computational terms, while contemporary machine learning relocates it into data curation, task definition, model architectures, objectives, metrics, and benchmarks. Although deep learning departs from explicit rules and predefined symbolic representations, it does not escape operationalization: curated datasets and task-specific objectives shape the learned latent spaces through which relations become detectable, comparable, rankable, and optimizable. AI therefore crystallizes historically specific conceptions of intelligence by embedding them in technical systems of training, measurement, comparison, and evaluation. Its social consequences emerge when these evaluative infrastructures shape which performances become recognizable, rewarded, and normalized. The question is therefore which conceptions of intelligence are being technically reproduced, made authoritative, and extended through AI systems.

Human-Centered AI and Cognitive Adaptation: An Empirical Study of Human Responses to AIMediated Information Systems

Artificial intelligence now shapes much of how information reaches people, who read it, and what they make of it. The environments it creates are probabilistic, often fluent without being grounded, and curated by systems whose workings stay hidden. Most research on this shift has gone to AI literacy, explainability, and the mechanics of human–AI interaction. Far less has gone to a prior question: how does human cognition itself change to cope? The paper addresses that question with the Human Cognitive Adaptation Framework (HCAF). One hundred thirty-one adults who use AI-generated and algorithmically curated content daily completed a 25-item, six-point inventory covering five facets—orientation, dimensional literacy, ambiguity tolerance, coherence recognition and critique, and relational navigation. The research examined reliability, the correlations among facets, and dimensionality. The full scale was highly reliable (α = .902) and the data factored cleanly (KMO = .841). One factor dominated: its eigenvalue of 7.93 carried 31.7% of the variance, and parallel analysis retained a single factor. The facets correlated strongly with one another (r = .46 to .69), which reads as one adaptive response rather than five separate skills. Within that single capacity, people recognized synthetic coherence more readily than they tolerated the uncertainty recognition exposes, so adaptation is not uniform across its parts. The research used these results to develop the HCAF, which treats adaptation as the work of staying oriented, judging coherence, and holding up under instability, and draws out what follows for human-centered AI, AI literacy, education, and the design of AI-supported decision environments.

From Artificial Intelligence to Augmented Understanding: Applied Curiosity as a Social and Managerial Architecture for the NEXT? Generation

Artificial intelligence is usually framed as a progressively more capable system for prediction, classification, generation and automation. This article argues that capability and computational capacity alone is an inadequate criterion for the next generation of human–machine systems. In other words, Artificial Intelligence is the last frontier, and the NEXT? one.  The more consequential transition is from artificial intelligence (AI) to augmented understanding (AU): from systems that produce plausible outputs to arrangements that enlarge the capacity of people and institutions to encounter uncertainty, test impossibility, construct meaning and act with others. The article develops applied curiosity as the governing architecture for that transition. Applied curiosity is defined not as a personality trait or episodic desire for information, but as a disciplined social practice that opens the possibility of a complete exploration of Chances, and discrimination of Choices and an authorisation of Changes (cCC*) across four transitional movements of Belonging, Becoming, Bridging and Building. The framework integrates dialogical inquiry, personal construct theory, living-systems analysis, global-workspace perspectives, Peircean inquiry, fast–slow judgement and an R1–R16 transitional register. It distinguishes an LLM-based decision-support system from an AU relationship in which stakeholders retain authorship, contestability and responsibility. Sixteen research propositions and a practical design protocol are offered for organisational strategy, public narrative, stakeholder engagement and reflective governance. The central claim is that the NEXT? generation should not be defined by a larger model, a more autonomous agent or a more persuasive simulation of intelligence as a truer articulation of applied knowledge. It should be defined by whether human and institutional participants can achieve MORE: Meaning, Options, Relationships and Expression.

Epistemic Subordination: Generative AI and the Infrastructure of Knowledge

Generative AI encodes the majority's way of knowing as the default infrastructure of knowledge itself as the default infrastructure of knowledge itself, and law must learn to govern at that level of model training.

What We Govern as Intelligence: Artificial Intelligence and The Problem of Answerability