Category: Education

Clarifying Authorship and Responsibility in an Age of Intelligent Tools

Artificial intelligence has entered education not with the gradual discretion of earlier technologies but with striking speed. Nowhere is the resulting uncertainty more visible than in academically demanding contexts such as the International Baccalaureate Diploma Programme. Teachers, examiners, and students increasingly ask a deceptively simple question: when a student uses AI to prepare an Extended Essay, a Theory of Knowledge essay, or an Internal Assessment, is that plagiarism?

The honest answer resists simplification. AI use is not automatically plagiarism, yet neither can it be considered neutral. What is required is not prohibition born of anxiety, but a clearer understanding of authorship, intellectual responsibility, and the purpose of academic work itself.

Plagiarism, in its classical sense, involves presenting identifiable words or ideas belonging to another author without acknowledgement. Generative systems complicate this definition because they rarely reproduce specific texts verbatim. Instead, they produce new linguistic formulations shaped by patterns within patterns. The result may sound authoritative, but it is not normally traceable to a single identifiable source.

If a student uses AI to clarify expression, test an emerging idea, organise an argument, or overcome linguistic insecurity, the situation resembles forms of academic mediation long accepted in education: editorial guidance, discussion with teachers, even sophisticated grammar tools. None of these, in themselves, negate authorship. Intellectual ownership rests less in phrasing than in the origin and defence of the thinking.

In the context of the IB Diploma Programme — where independent inquiry and reflective judgement are central — this distinction acquires particular significance. If the research question, interpretative stance, and argumentative decisions are genuinely the student’s, AI functions as an instrument rather than an author. The decisive issue is not who produced the sentences, but who assumes responsibility for the ideas.

Yet the boundary cannot be ignored. When AI generates analysis or interpretation that the student neither understands nor critically assesses, the educational process is hollowed out. Even if the wording is technically original, intellectual labour has effectively been outsourced. That situation may not always fit a narrow legal definition of plagiarism, but academically, it risks compromising integrity.

Transparency, therefore, becomes essential. Concealing substantial AI assistance undermines the trust upon which academic evaluation depends. Equally significant is reliability. Generative systems can produce inaccuracies, invented references, or oversimplified interpretations. Incorporating such material uncritically does not necessarily constitute plagiarism, but it falls short of scholarly responsibility. Verification, discernment, and intellectual caution remain indispensable academic virtues.

Much of the anxiety surrounding AI reflects legitimate concerns, though it sometimes overshoots its target. The assumption that AI-assisted writing cannot represent authentic student work presupposes a rather romantic image of solitary authorship that has rarely existed in academic practice. Writing has always involved dialogue — with teachers, peers, editors, and prior texts. Tools evolve; the social nature of knowledge does not.

The deeper issue, therefore, is not technological but educational. If academic tasks reward procedural execution alone, delegation becomes tempting. If they require judgment — the capacity to weigh evidence, defend interpretation, and assume responsibility for conclusions — authorship remains irreducibly human.

Clear expectations do not constrain students; they protect their intellectual development. Conceptual clarity, however, must translate into operational judgement.

Questions That May Help Clarify Responsible AI Use

The following questions are not intended as regulation, but as prompts for judgment. They may help students, teachers and schools reflect on whether AI is supporting learning — or quietly replacing it.

• Does the research question and central line of argument genuinely originate from the student?
• Can the student clearly explain and defend every major claim without relying on the AI tool?
• Has AI been used primarily to clarify language, refine structure or test emerging ideas, rather than to generate substantive analysis?
• Did the student critically evaluate, revise or reject suggestions produced by the tool?
• Have all references, data and quotations been independently verified using reliable sources?
• Has engagement with required primary and secondary sources been maintained?
• Has any significant AI assistance been transparently acknowledged?
• If asked to discuss the work orally, without access to AI, would the student demonstrate clear understanding and independent reasoning?
• Would the quality of thinking — even if not the polish of expression — remain recognisably their own?

A final reflection may be decisive.

If artificial intelligence is present in academic work, the essential question is not whether a tool has been used, but what kind of thinking has taken place.

That question cannot be resolved by detection software or automatic rules. It requires judgement — exercised by students, teachers and institutions alike.

A broader philosophical exploration of judgment and authorship in the age of intelligent tools is developed in The Renaissance Workshop and Human Judgment in the Age of Artificial Intelligence.

If American soft power weakens, a vacuum appears.
The real question is whether Europe understands the nature of its own strength — and whether education is prepared to sustain it.

In a recent reflection, I considered Europe’s responsibility in a world accelerated by technology. An editorial in Le Monde adds a geopolitical dimension to that same question.

Philippe Bernard concludes his analysis of Donald Trump’s posture towards alliances with a revealing observation:

“En sacrifiant le soft power américain, Donald Trump alimente l’impopularité de son propre pays (…) Mais il invite aussi les Européens à exercer leur propre soft power… ses valeurs humanistes et démocratiques, ses arts, sa culture, ses modes de vie, ses systèmes sociaux et ses libertés méritent d’être préservés, défendus et promus. L’offensive Trump pourrait y aider.” — Philippe Bernard, Le Monde, 15 February 2026

The argument extends beyond American politics. When a dominant soft power weakens, space opens. China may expand economically, yet its political and cultural attractiveness within democratic societies remains limited. The deeper question concerns Europe itself: does it recognise the nature of its own influence?

Europe’s strength has never been merely economic. It is civilisational. Humanism, the rule of law, artistic creation, institutional pluralism, democratic freedoms — these were built patiently over centuries. They are not abstractions. They are institutional achievements.

And they are transmitted through education.

Much of today’s strategic debate revolves around artificial intelligence, competitiveness and technological sovereignty. These matters are important. Yet Europe’s most durable strategic asset lies elsewhere: in normative leadership — in its capacity to shape standards, values and intellectual frameworks.

Soft power does not begin in diplomacy. It begins in schools and universities.

An education worthy of Europe must cultivate critical judgement, historical awareness, intellectual freedom balanced with responsibility, and the capacity to govern technology rather than be governed by it. Without such formation, values become decorative.

External pressure may clarify identity. But identity requires cultivation.

If Europe is to exercise its soft power, it will do so through the formation of free and responsible minds. That remains, fundamentally, an educational task.

The real challenge of artificial intelligence in education is not technical but human. The question is not how to optimise learning, but what kind of human beings education is meant to form.

The most pressing challenge posed by artificial intelligence in education is not technical. It is human.

Working in both the classroom and educational foundations, I increasingly observe that much of the debate revolves around optimisation: access, personalisation, efficiency, and scale. These are important considerations. Yet education has never been reducible to efficiency.

Education concerns judgment, culture and personal formation. It shapes the intellectual and moral capacities through which individuals participate in society. When technological innovation becomes the primary lens through which education is understood, we risk narrowing its purpose.

From a European humanistic perspective, this narrowing is particularly significant. Education in this tradition has never been confined to skills training. It is concerned with forming persons capable of freedom, responsibility and critical discernment. It is civilisational in scope.

Technology can assist this project. It can expand access to knowledge, support individual pathways and augment intellectual work. But it cannot determine the ends of education. That remains a human and cultural question.

Innovation becomes valuable only when it deepens understanding rather than substitutes for it, when it reinforces responsibility rather than displaces it, and when it strengthens critical judgment rather than bypasses it.

The question, therefore, is not whether technology will shape the future of learning. It already does.

The deeper question is whether we ensure that it remains ordered to the human purposes education is meant to serve.

Technology should serve education — and education should serve the formation of free and responsible minds.

A recent article by Ángel Jiménez de Luis in El Mundo reported the resignation of Mrinank Sharma, Head of AI Safety at Anthropic. Beyond the individual case, the episode reflects a broader symptom: even within the technology sector, a growing divide is emerging between enthusiasm for artificial intelligence and reflection on its consequences.

This is not an abstract debate. New generative models are already transforming entire professions — from programming and marketing to consulting and administrative work. Some estimates suggest that large-scale automation of cognitive and administrative tasks will occur in the coming years. What we are witnessing is not incremental change but a profound economic and social reconfiguration.

From an educational perspective, at least three urgent reflections follow.

First, AI literacy can no longer be confined to instrumental use. Teaching students how to operate tools is insufficient. We must cultivate judgement, critical reasoning and a structured understanding of the social and ethical implications of these technologies.

Second, the locus of human value is shifting. The competencies that resist automation — ethical discernment, genuine creativity, collaborative intelligence, leadership, and the capacity to interpret meaning — are becoming central. Education must therefore strengthen precisely those dimensions that machines cannot replicate.

Third, educational institutions must become spaces for informed debate about technology and values, not merely sites for technological adoption. Integrating AI without considering its cultural and anthropological implications would be pedagogically superficial.

Perhaps the most telling signal in this episode is not technological but cultural. When AI safety specialists step back and turn to philosophy or poetry, the gesture is symbolic. It suggests that technical mastery alone does not resolve deeper questions about direction, responsibility and human purpose.

Education carries a historical responsibility at this moment. It must not only prepare individuals to coexist with artificial intelligence but also help preserve and cultivate what remains irreducibly human.

The real question is not whether AI will transform society. It is whether our educational systems will retain the clarity — and the courage — to shape that transformation with judgement rather than surrender to it.

In January 2026, Dario Amodei, CEO of Anthropic, published an essay titled The Adolescence of Technology. His central claim is disarmingly simple: artificial intelligence is advancing faster than our institutions, norms and collective capacity to govern it. We are living through a technological adolescence — rapid growth in capability, coupled with immature governance.

If that diagnosis is correct, education is no longer peripheral to the debate; it becomes central.

1. From tool use to critical technological literacy

Education cannot confine itself to teaching how to use AI systems. Students must understand, at a conceptual level, how these systems function, where their limitations and biases lie, and the economic, social and ethical consequences they entail.

Technological literacy must become critical literacy — embedded across the curriculum rather than confined to optional modules.

2. Educating for uncertainty and complexity

The impact of advanced technologies is systemic and difficult to predict. This requires an educational shift: greater systems thinking, greater decision-making under uncertainty, and greater engagement with open-ended problems in which trade-offs and unintended consequences are explicit.

The goal is not merely correct answers. It is sound judgement.

3. Preparing for economic transition

Amodei anticipates significant labour market disruption. Whether timelines prove accurate or not, the direction is clear: automation will continue to reshape professional life.

Education must therefore strengthen lifelong learning pathways, cultivate capabilities that are difficult to automate — creativity, communication, ethical reasoning, and leadership — and align curricula with evolving realities rather than static job descriptions.

4. Ethics and governance as core content

When technological capability outpaces governance, responsibility cannot remain peripheral.

Education must integrate technology with law, public policy and the humanities. Students should see themselves not only as users or engineers, but as stewards of complex systems whose consequences extend beyond efficiency.

5. Institutions must mature

If technology is in its adolescence, educational institutions must mature alongside it. That requires continuous professional development in AI literacy, greater curricular flexibility, and stronger interdisciplinary cultures. Institutional rigidity is becoming a strategic liability.

Conclusion

Amodei’s metaphor serves as both a warning and an opportunity.

If technology advances faster than our ability to govern it, education becomes the primary arena in which societies cultivate judgement, responsibility and wisdom.

The task is not simply to teach new tools. It is to form citizens capable of understanding, questioning and guiding technologies whose power increasingly rivals that of our institutions.

The question resurfaced this week after a report in Le Monde highlighted a growing scepticism towards university degrees among some Silicon Valley founders and investors. Critics argue that higher education has become too expensive, ideologically rigid, and increasingly disconnected from labour-market realities—particularly at a moment when generative AI appears to be reshaping entry-level technical roles and the cultural myth of the self-taught dropout-founder retains symbolic power.

Whether such claims are overstated or selectively framed, the underlying uncertainty deserves attention. We genuinely do not yet know how employers will value traditional academic credentials in a labour market where demonstrable skills, adaptability and experience may carry greater weight than formal qualifications alone.

Yet reducing higher education to a signalling mechanism or a short-term employability metric would be a serious mistake.

A university degree is not solely about what students know at graduation. It concerns what they become through the disciplined process of study. When intellectual standards are demanding, and learning environments remain open and rigorous, universities cultivate capacities that are difficult to automate: critical reasoning, sustained argumentation, epistemic humility, curiosity, collaboration, resilience and the ability to navigate ambiguity.

These are not ornamental virtues. They are structural competencies for uncertain, AI-centred economies.

There is also a deeper human dimension. The university remains one of the few institutions explicitly designed not merely to transmit knowledge or train workers, but to help individuals interpret the world, challenge inherited assumptions, refine their judgement and articulate a sense of purpose. That form of formation does not always map neatly onto quarterly hiring data, yet societies have historically relied on it.

We do not know whether companies will reward degrees as they once did. But if students approach their studies seriously, and if professors defend the academic mission with clarity and courage, higher education can still offer something that is increasingly scarce: not merely credentials, but the cultivated capacity to think, to reason and to act with discernment.

As schools and universities prepare for admissions in the 2026–2027 academic year, one question remains open: what will students choose?

Although we cannot yet determine how preferences will shift in the coming cycle, recent data from Spain and France offer useful signals. Patterns from the previous admissions round reveal not only disciplinary preferences but also how institutional structures shape educational demand.

Across both countries, students tend to favour fields perceived as professionally reliable. Health-related programmes dominate — through PASS in France and through highly competitive entry thresholds in Spain — while Law, Psychology, Business/Economics and STEM disciplines continue to attract sustained demand.

Yet the architecture of each national system significantly shapes how these preferences manifest.

France’s centralised platform, Parcoursup, makes student choices nationally visible and creates strong concentration effects. PASS applications, for instance, dwarf most other pathways, reinforcing national hierarchies of prestige and selectivity.

Spain’s decentralised admissions model, by contrast, spreads demand across autonomous regions and institutions. Programme-level competition is increasingly evident, with double degrees and engineering pathways often demanding particularly high entry thresholds. Rather than a single national hierarchy, differentiation is driven by institutional reputation and programme design.

Three observations follow.

1. Employment rationality.

Students gravitate towards fields with clear labour market signals — health, law, engineering, and business. Even before entering university, young people are acutely aware of economic uncertainty and future employability.

2. STEM prestige on the rise.

Mathematics, engineering and data-related pathways increasingly attract high-performing students. Technological transformation appears not to deter applicants but to reinforce the perceived value of quantitative and technical disciplines.

3. Systems shape demand.

Centralisation reinforces national hierarchies (France), whereas decentralisation fosters institutional competition and programme differentiation (Spain). The same generational concerns are filtered through distinct structural incentives.

Taken together, these patterns offer insight into how young Europeans imagine their economic futures in a context shaped by artificial intelligence, demographic pressures and evolving labour markets. Preferences are not merely academic; they reflect broader perceptions of security, prestige and opportunity.

Generative AI is not diminishing the value of education. It is shifting the locus of value.

For over a century, secondary schooling rewarded technical execution: writing structured essays, summarising texts, performing calculations, translating languages and applying standard procedures. Generative systems now perform many of these tasks with speed and fluency. The educational premium, therefore, moves elsewhere — towards capacities that are far more difficult to automate.

The centre of gravity is shifting from execution to judgement.

Early international signals point in three consistent directions.

1. From “doing” to “deciding”

Students must not only solve problems but also formulate tasks, evaluate outputs, define criteria and justify choices. The Brookings Institution has described this emerging demand as the cultivation of “judgment in ambiguity” (2024).

In an AI-augmented environment, knowing how to proceed is no longer enough. Students must know when, why and under what assumptions they proceed.

2. From product to process

Assessment is gradually moving towards traceability: hypotheses formulated, alternatives considered, revisions made, and reasons articulated. The OECD (2023) identifies this shift as essential for what it terms “future-proof cognitive resilience.”

The emphasis is less on the polished final product and more on the intellectual pathway that produced it.

3. Disciplinary thinking + AI (not either/or)

AI does not replace Mathematics, Biology or History. It raises the conceptual bar within them. The International Baccalaureate has already incorporated AI literacy into academic honesty and assessment guidance (IBO 2023–2025 updates), signalling that disciplinary integrity must coexist with technological fluency.

The implication is structural.

If this trajectory continues, upper secondary education may begin to resemble university sooner than expected: less reproduction, more argumentation; less execution, more criteria; fewer tasks to complete, more decisions to defend.

The schools that will thrive are not those that merely teach students to “use AI,” but those that develop students capable of thinking with AI without outsourcing their own thinking.

For two decades, we have spoken of the “attention economy”. Platforms competed to capture our attention, maximise engagement and monetise distraction.

We are now entering a more subtle phase.

Emerging research describes what is increasingly called the intention economy: a technological environment in which artificial i not merely react to our behaviour but anticipates, shapes and commercialises our future decisions. Predictive systems learn to identify emerging preferences before they are consciously articulated. Desire becomes data. Intention becomes an asset.

The shift is significant.

Attention concerns what we look at.

Intention concerns what we choose.

In educational contexts, the implications are profound. Students are not fully formed consumers; they are individuals in the process of constructing values and direction. An ecosystem that places economic value on anticipating and influencing its emerging intentions introduces a new layer of vulnerability.

Teaching digital skills is therefore no longer sufficient. Competence in using tools does not guarantee autonomy in forming desires.

The more pressing question is both meta-cognitive and ethical:

How do I decide what I want?

How do I recognise when my preferences are being nudged or pre-shaped?

How do I distinguish between authentic aspiration and algorithmically amplified impulse?

These are not technical questions. They are anthropological.

Education must create deliberate spaces for reflection on the architecture of digital environments. Not merely how platforms function, but how they shape perception, preference formation and self-understanding. Students need conceptual tools to interpret influence, not merely to navigate interfaces.

This reframes the educator’s role.

In the intention economy, teachers are not only transmitters of knowledge or facilitators of skills. They are agents of empowerment, responsible for helping students cultivate reflective will — the capacity to pause, examine motives, and decide deliberately rather than react predictably.

Algorithmic systems segment, predict and optimise.

Education must form people capable of resisting automaticity.

If the attention economy risked distraction, the intention economy risks something deeper: the gradual outsourcing of desire.

Education cannot remain peripheral to this transformation.

Its task is not merely to prepare students for a digital world but to ensure they remain sovereign within it — capable of taking their intentions seriously rather than allowing them to be silently shaped.

In a recent article in El Mundo, British historian Niall Ferguson advances a deliberately stark proposal for the future of higher education. He suggests that the university of tomorrow may need to be divided into two complementary spaces.

The first he calls the “cloister”: a technology-free environment inspired by medieval monasteries, where reading, writing, debate and sustained problem-solving occur without digital mediation. The second is the “spaceship”: a space where artificial intelligence is used intensively and students learn to interact fluently with advanced technologies.

The proposal is architectural, but its core is pedagogical.

Ferguson’s central insight is that asking good questions requires prior formation. Intellectual depth does not arise from immediate access to answers. It is cultivated in silence, through effort, and through the disciplined confrontation with difficulty.

The cloister, in this sense, is not a nostalgic retreat. It is a preparatory space. It recognises that judgement precedes automation.

Artificial intelligence can expand inquiry. It cannot replace the habits that make inquiry meaningful. Without prior immersion in texts, arguments and conceptual struggle, AI risks becoming not a tool of amplification but a mechanism of shortcuts.

The distinction recalls the classical idea of paideia: education as the formation of the whole person through rigorous engagement with language, logic and moral reasoning. In a culture increasingly structured by algorithmic immediacy, defending such formative spaces becomes countercultural.

The spaceship, by contrast, acknowledges reality. Students must learn to navigate and shape technological systems. Fluency in AI interaction will be indispensable, but it must rest on intellectual foundations laid elsewhere.

The deeper question concerns sequencing.

Do we expose students first to optimisation or to formation?

Do we train them to generate or to think?

Ferguson’s proposal suggests that universities must resist the temptation to collapse these stages. If everything becomes a spaceship, the cloister disappears — and with it, the conditions for meaningful questioning.

In that sense, the debate is not about technology versus tradition. It is about safeguarding the formative conditions that make technological mastery responsible rather than reactive.

The future university may indeed require both spaces.

But without the cloister, the spaceship has no pilot.