Contemporary anxieties surrounding Artificial Intelligence strike me as curiously ahistorical.
Listening to much of the present discourse, one might reasonably conclude that humanity now stands uniquely imperilled by a technological development capable of eroding intellect, collapsing education, degrading human relationships, exhausting planetary resources, and ultimately rendering human thought itself obsolete. Yet such rhetoric reveals less about AI than it does about a recurring civilisational tendency: our persistent inclination to mistake cognitive transition for cognitive decline.

This fear is ancient.

It was not Aristotle but Plato (via his voiced Socrates) — in Phaedrus — who famously warned that writing itself might damage the human mind. Writing, Socrates argued, would weaken memory, encourage superficial understanding, and create merely the appearance of wisdom rather than wisdom properly understood. Knowledge externalised onto parchment, he feared, would diminish humanity’s capacity for genuine intellectual engagement.

In one important respect, he was correct.

Writing did redistribute aspects of memory beyond the individual mind. Human beings no longer needed to internalise vast oral traditions with the same rigour demanded by pre-literate cultures. Something was indeed lost.

Yet what emerged in exchange was civilisation on an entirely different cognitive scale.
Writing enabled philosophy, law, historical consciousness, mathematics, science, theology, literature, bureaucracy, and eventually the modern university itself. The delegation of memory beyond the human mind did not destroy intelligence; rather, it expanded the range and complexity of intellectual activity available to human societies.

The same underlying anxiety resurfaced millennia later with the calculator.

I belong to a generation old enough to remember when handheld calculators still possessed a faint aura of technological wonder. More importantly, I remember the educational panic which accompanied them. Serious voices argued that calculators would “ruin mathematics,” that students deprived of manual arithmetic would become intellectually dependent and mathematically illiterate.

Again, something subtler occurred.

The calculator did not abolish mathematics. It redistributed cognition within mathematics. Time and cognitive energy previously consumed by repetitive computation could now be redirected toward abstraction, modelling, statistical reasoning, and higher-order conceptual thought. The threshold at which students could meaningfully engage sophisticated mathematical ideas shifted downward in age while simultaneously expanding upward in complexity.

This is the pattern history repeatedly reveals, yet modern societies consistently forget:
technological tools rarely eliminate thought outright; more often, they relocate where thought occurs.

Writing redistributed memory.
Libraries redistributed knowledge storage.
Calculators redistributed computation.
Search engines redistributed information retrieval.

Artificial Intelligence may represent the next stage in this long process of technological mediation and cognitive redistribution.

And it is here, I suspect, that much of the contemporary discourse surrounding AI becomes intellectually impoverished.

Public discussion oscillates between two equally reductive poles. On one side sits apocalyptic fearmongering: AI as civilisational catastrophe, educational collapse, environmental apocalypse, and cognitive atrophy. On the other stands corporate techno-utopianism promising frictionless efficiency, personalised enlightenment, and inevitable progress — conveniently monetised through subscription platforms and proprietary ecosystems.
Neither position strikes me as remotely adequate.

There are unquestionably legitimate concerns about AI systems. Generative models consume substantial computational resources. Large-scale data centres place real pressure upon electrical grids, cooling infrastructures, and water systems. AI systems can fabricate information, reinforce biases, flatten nuance, and encourage forms of intellectual passivity when deployed uncritically. Corporate monopolisation of informational ecosystems should concern any serious educator or citizen.

Yet much contemporary commentary suffers from a striking absence of historical and technological proportion.

Modern civilisation already rests upon computational infrastructures of astonishing scale. Streaming platforms, cloud computing, global logistics systems, financial markets, recommendation algorithms, and social media architectures collectively consume immense quantities of energy daily. AI did not suddenly introduce computational consumption into society; rather, it intensified and accelerated pre-existing trajectories embedded within late-modern digital life.

Nor should we assume present inefficiencies represent permanent conditions.

Early industrialisation was catastrophically wasteful. Early computers occupied entire buildings. Early mobile phones resembled military hardware. Over time — through engineering innovation, economic pressure, regulation, and social adaptation — such systems evolved toward far greater efficiency.

The same process is already visible in AI development:

• low-power AI architectures
• photonic and optical computing
• advanced liquid cooling systems
• model compression and quantisation
• localised “edge AI” computation
• renewable-powered data centres
• increasingly efficient inference systems

This does not guarantee benevolent outcomes. Technological progress is neither automatic nor morally neutral. Technologies are shaped by political choices, economic incentives, regulatory structures, and cultural values. Nevertheless, the assumption that current inefficiencies or dysfunctions represent final forms reflects poor historical reasoning. I lived in Japan, where the scientific brilliance of splitting the atom had horrific consequences on the civilian population!

The deeper transformation may ultimately prove educational rather than technological.
What AI potentially offers education — particularly within the humanities — is not the eradication of thought, but the redistribution of certain forms of lower-order cognitive labour in ways that may increase access to higher-order synthesis, interpretation, comparison, and critique.

For decades, humanities education has often operated under increasingly contradictory pressures:

• expanding curricular demands
• escalating administrative obligations
• standardised assessment regimes
• measurable-outcome cultures
• shrinking preparation time
• increasing differentiation requirements
• diminishing opportunities for interdisciplinary inquiry

The result has frequently been a pedagogy of coverage rather than a pedagogy of intellectual exploration.

This is where my own experience with AI becomes relevant.

Ironically, AI has not reduced my workload as a teacher. In several respects, it has increased substantially.

Prior to generative AI, many educators — through no personal failing — often relied on generic, pre-made resources broadly aligned with examination specifications. There were simply insufficient hours available to bespoke every lesson to the precise intellectual, linguistic, and curricular needs of a particular class cohort.

Now, however, I can spend hours constructing highly differentiated, academically grounded materials synthesised from:

• curriculum documents
• examiner reports
• academic criticism
• educational theory
• historiographical perspectives
• literary scholarship
• differentiated literacy supports
• assessment frameworks
• government publications
• comparative pedagogical models

I can upload hundreds of pages of material into AI systems and rapidly identify conceptual patterns, disciplinary tensions, recurring themes, assessment trends, and curricular alignments which would previously have required weeks of labour to synthesise manually.

Naturally, such synthesis still requires rigorous human verification, cross-referencing, and disciplinary judgement. Generative systems remain prone to hallucination, uneven source weighting, and flattening of nuance. The machine does not replace expertise.
AI is most powerful not in the hands of the uninformed, but in the hands of deeply knowledgeable practitioners capable of interrogating, refining, rejecting, and reshaping its outputs.
One must still know:

• which questions matter
• which sources possess credibility
• how disciplinary knowledge functions
• how curriculum architecture operates
• how to identify distortion and inaccuracy
• how to scaffold understanding appropriately
• how to transform information into meaningful pedagogy

The labour has not disappeared.
It has become more epistemic than clerical.
More selective.
More evaluative.
More synthetic.
More disciplinary.
More bespoke.
In many respects, AI allows me to devote less time to information accumulation and more time to pedagogical refinement.

Critics frequently characterise AI-assisted teaching as intellectual laziness. My experience suggests almost the opposite. The technology has enabled me to construct resources far more precisely tailored to the actual students in front of me, rather than relying upon generic one-size-fits-all materials downloaded from educational repositories.

The irony is difficult to ignore:
AI may ultimately facilitate more human-centred teaching precisely because it enables greater levels of differentiation, responsiveness, and intellectual adaptation.
The implications for the humanities are potentially enormous.

Mathematics adapted relatively successfully to calculators because the discipline eventually accepted that repetitive calculation was not the highest expression of mathematical thinking. Once aspects of computation became redistributed, pedagogy could move upward toward modelling, abstraction, systems thinking, and conceptual reasoning.
The humanities may now stand before an analogous moment.

For students whose linguistic fluency lags behind their conceptual capacity, AI can function as a bridge into higher-order intellectual engagement. It can model academic vocabulary, argumentative structures, historiographical comparison, disciplinary reasoning, and analytical frameworks previously inaccessible to many learners.

A student struggling to structure an essay can now examine multiple forms of argumentation dynamically. Historical interpretation can become comparative rather than merely descriptive. Literary criticism once buried within inaccessible academic journals can be rendered pedagogically usable without being entirely stripped of complexity.

In this sense, AI may do for aspects of the humanities what the calculator once did for mathematics:
lower the threshold for sophisticated intellectual engagement to become possible.
None of this eliminates the need for judgment, evidence evaluation, ethical reasoning, or disciplinary literacy. If anything, these capacities become more essential than ever. The genuine danger is not AI itself, but passive intellectual dependency combined with technological illiteracy.

The visible output of AI-assisted writing often obscures the invisible intellectual architecture underpinning it: decades of disciplinary reading, pedagogical experience, curricular familiarity, examiner knowledge, accumulated reflection, and scholarly engagement remain prerequisites for asking meaningful questions in the first place.

The machine accelerates synthesis.
It does not generate wisdom ex nihilo.
Socrates feared writing would weaken memory.
Instead, writing expanded civilisation.

We may yet discover that Artificial Intelligence — critically understood, ethically governed, and intellectually disciplined — represents not the abandonment of thought, but another transformation in how human thought extends itself into the world.

現代社会におけるAIへの不安は、実は歴史的に繰り返されてきた「新しい知的道具への恐怖」の延長線上にある。古代ギリシャでは、Plato が『パイドロス』の中で、ソクラテスを通じて「文字は記憶力と思考力を弱める」と警告した。しかし実際には、文字は哲学・科学・歴史・文学を発展させ、人類文明を大きく拡張した。同様に、電卓も「数学を破壊する」と恐れられたが、実際には反復計算を機械に委ねることで、より高度な数学的思考を若年層に可能にした。
AIもまた、人間の思考を消滅させるのではなく、「どこで思考が行われるか」を変化させているに過ぎない。特に教育現場では、AIは単なる時短ツールではなく、膨大な論文・教育資料・カリキュラム・試験分析を統合し、教師がより高度で個別化された教材を構築するための補助装置となり得る。従来であれば数十時間を必要とした教材設計が、数時間で可能になる場合もある。しかし、それは「楽をしている」のではなく、教師の労働が事務的作業から、より批判的・分析的・専門的な判断へ移行したことを意味する。
もちろんAIには誤情報、偏見、依存性、企業独占などの危険も存在する。だからこそ必要なのはAIへの盲目的な賛美でも拒絶でもなく、それを批判的・倫理的に活用する知的成熟である。AIは思考の終焉ではなく、人類が長い歴史の中で続けてきた「知の外部化」と「知的拡張」の新たな段階なのかもしれない。

P.S.Yes, AI was used in the drafting of this essay.
It nevertheless required several hours of writing and refinement, supported by decades of reading, teaching, examining, and sustained reflection underlying the ideas themselves.
Artificial Intelligence can accelerate articulation. It cannot generate intellectual life.
If these reflections are freely accessible, it is partly because such technologies now allow complex ideas to be expressed and refined within the practical constraints of ordinary professional life. The prose may be machine-assisted; the thinking most certainly is not.

P.P.S.As someone who loves the wit of Oscar Wilde, I think he may have seen it thus - 
One should perhaps be cautious when condemning the ethics of Artificial Intelligence while sipping coffee historically financed by empire and typing indignation upon devices assembled through globalised extraction networks.
Moral superiority, like most luxury goods, has rarely been sustainably sourced.

There is a moment, somewhere between the completion of a Junior Cycle History CBA and the subsequent SLAR meeting, when a teacher begins to understand that education reform is not really about documents, frameworks, or circulars. It is about systems colliding with reality. It is about what happens when ambitious pedagogical theory meets the uneven terrain of actual classrooms.

Having now almost completed my first full year teaching History in Ireland, I find myself increasingly reflective — and increasingly cautious — about the distance between policy aspiration and classroom implementation. I say this not as someone hostile to reform. Quite the opposite. Much of what the Irish education system wants to achieve is admirable: critical thinking, student agency, inquiry-based learning, collaborative reflection, and authentic assessment. These are worthwhile goals.

But after working through the Junior Cycle Classroom-Based Assessment (CBA) process in practice, I am left wondering whether the system has underestimated something fundamental: reform cannot succeed without first confronting the skill shortfall that exists across the educational structure itself.

And that shortfall is not simply student-based.

The Promise of Reform
On paper, the Junior Cycle reforms are intellectually coherent. Moving away from rote memorisation towards skills-based learning reflects wider international educational trends. Students are expected to investigate, interpret, evaluate sources, communicate findings, and reflect on their learning process. In History, the CBA framework attempts to position students as “young historians” rather than passive recipients of information.
Again, theoretically, this makes sense.

The problem is that successful inquiry-based education presupposes a series of underlying competencies that are often unevenly developed: literacy, organisational ability, independent research skills, time management, critical thinking, and increasingly, digital literacy.
Many students do not yet possess these foundational capacities to the degree reform assumes.

Nor, if we are honest, do many schools possess the structural conditions necessary to support them fully.

The Myth of Automatic Skill Transfer
One of the great assumptions embedded within modern curriculum reform is that generic “skills” transfer naturally across subjects. Students are expected to conduct research, evaluate evidence, collaborate effectively, reflect metacognitively, and communicate formally — often without explicit, systematic instruction in how to do these things. 

Principals and teachers are frequently left attempting to navigate multiple — and at times contradictory — circulars, frameworks, and policy documents, often without clear operational guidance. What is perhaps most striking is the absence of sustained subject-specific professional training in many of the areas discussed here.
Indeed, I was genuinely astonished to discover that, as a Leaving Certificate English teacher, there is no comprehensive national course dedicated to unpacking the syllabus itself: its learning outcomes, assessment philosophy, marking criteria, and practical classroom implications. Unless a teacher becomes an SEC examiner, much of the system relies upon individual interpretation. In practice, many teachers are left trying to make informed educational guesses about what curriculum documents actually mean and, more importantly, how those meanings should be translated into coherent learning experiences for students.
But skills are not magically acquired through exposure.

A student who struggles to structure a paragraph in English will not suddenly produce a sophisticated historical reflection because the assessment rubric asks them to. A student unfamiliar with evaluating online sources cannot meaningfully engage in “independent inquiry” merely because a CBA descriptor references it.

What I increasingly observed throughout the CBA process was not laziness, but cognitive overload.

Students were often trying to simultaneously:

• understand the historical topic,
• learn research methodology,
• navigate digital tools,
• organise evidence,
• interpret success criteria,
• manage deadlines,
• and communicate findings coherently.

For stronger students, this was manageable. For others, it became profoundly difficult.

The irony is that reforms intended to increase equity may unintentionally widen performance gaps if foundational skills are not systematically taught beforehand.

The Quiet Digital Divide
Perhaps the most underestimated issue of all is digital literacy.

Educational policy frequently speaks as though today’s students are “digital natives.” This is one of the most misleading assumptions in contemporary education. Students may be comfortable consuming digital content, but many struggle with the actual functional literacies required for academic work:

• file management,
• formal document creation,
• research verification,
• citation practices,
• information evaluation,
• collaborative digital workflows,
• and even basic organisational habits.

In practice, I found myself teaching not only History but also how to rename files properly, structure documents, verify websites, use Canva meaningfully rather than decoratively, and distinguish between research and copying.

This is not a criticism of students. It is evidence of systemic transition. Schools have embraced digitally-mediated assessment faster than many systems have properly prepared students — or teachers — for it.

Teacher Workload and the Hidden Labour of Reform
What also becomes apparent during the CBA process is the extraordinary amount of invisible labour now expected of teachers.

The rhetoric of formative assessment and ongoing feedback sounds entirely reasonable until one confronts the realities of the timetable. A teacher seeing classes for 200 minutes a week while managing multiple year groups quickly discovers that meaningful process-based assessment requires enormous time investment:

• conferencing,
• monitoring drafts,
• guiding inquiry,
• differentiating support,
• documenting progress,
• preparing for SLAR moderation,
• and ensuring consistency of judgement.
• 
  

None of this is impossible. But it is resource-intensive.

Educational reform often speaks the language of transformation while still operating within structures built for older models of teaching.

That tension matters.

Between Idealism and Reality
Despite all this, I remain cautiously supportive of many aspects of the Irish reforms. I have seen moments during the CBA process in which students genuinely took ownership of their learning in ways that traditional examinations rarely allow. Some produced thoughtful, creative, genuinely impressive work. Others demonstrated historical curiosity that would never emerge through textbook-driven recall alone.

But reform requires honesty.

If Ireland genuinely wishes to move toward inquiry-based, skills-driven education, then systems must confront the foundational gaps more directly. Skills cannot remain abstract aspirations hidden inside curriculum documents. They must become explicitly taught, systematically scaffolded, and realistically resourced.

Otherwise, we risk creating a system where the language of educational progress masks uneven implementation on the ground.

As I finish my first year teaching History in Ireland, I find myself increasingly convinced that the central challenge facing Irish education is no longer primarily curriculum design. The frameworks are already ambitious.

The real question is whether the system has fully reckoned with the practical realities of turning policy into sustainable classroom practice.

That conversation, I suspect, is only beginning.

アイルランドで歴史を教え始めて1年目を終えようとする中で、私はCBA（教室ベース評価）を通して、教育改革と現場実践の間に大きな隔たりがあることを実感した。探究型学習や批判的思考を重視するJunior Cycle改革の理念自体には賛成である。しかし実際には、多くの生徒が文章構成力、調査能力、時間管理、そしてデジタル・リテラシーといった基礎的技能を十分に身につけていない。また、教師側にも膨大な形成的評価や記録管理が求められ、制度が想定する理想と学校現場の条件との間に緊張が存在する。特に「デジタル・ネイティブ」という前提は危うく、多くの生徒は情報整理や資料検証に苦労している。改革を成功させるには、技能を暗黙に期待するのではなく、体系的に教え支援する必要があると感じている。

​There is a growing tendency in contemporary discourse—particularly among younger cohorts—to position artificial intelligence as an environmental threat of singular urgency. Claims that “AI is destroying the planet” or that a single query consumes “a bottle of water” circulate widely, often repeated with moral conviction but little evidential grounding. While such claims are not entirely without basis, their current form reflects something more troubling: a collapse of proportional and causal thinking in the face of complex technological systems.
Artificial intelligence does have environmental implications. That is not in dispute. What is at issue is the manner in which those implications are being interpreted, exaggerated, and, in some cases, misapplied. The problem is not simply misinformation, but the erosion of the intellectual habits required to evaluate it.

Infrastructural continuity: AI did not arrive in a vacuum
Artificial intelligence does not represent a rupture in technological history. It is an intensification of an already expansive digital infrastructure. Long before the emergence of generative AI, global systems of cloud computing, streaming media, algorithmic search, and e-commerce were dependent upon large-scale data centres operating continuously across the globe.

As the International Energy Agency (IEA, 2024) reports, data centres consumed approximately 415 terawatt-hours (TWh) of electricity annually—around 1.5% of total global demand—prior to the widespread adoption of generative AI systems. This is not a marginal figure. It indicates that the environmental footprint of digital infrastructure was already both real and substantial.
To suggest, therefore, that artificial intelligence has introduced environmental strain where none previously existed is historically and analytically untenable.

Acceleration without invention
What artificial intelligence does introduce is not a new category of environmental impact, but an intensification of an existing one. The same IEA projections indicate that electricity demand from data centres may more than double by 2030, with AI workloads constituting a significant proportion of that growth. Parallel analysis from the Brookings Institution (2024) estimates that AI-related computational demand is increasing at approximately 30% annually, positioning it as a primary driver of incremental energy consumption within the sector.
The distinction here is critical. Artificial intelligence has not created the environmental burden associated with digital infrastructure; it is accelerating it. To conflate these two claims is to substitute rhetorical force for analytical clarity.

Scale, systems, and the fallacy of the individual act
A persistent feature of public discourse is the tendency to locate environmental responsibility in individual behaviour. In the case of AI, this manifests in the assertion that a single user query constitutes a meaningful ecological harm. This is a category error.

Environmental impact in digital systems is structural rather than episodic. A single interaction is negligible; the cumulative effect of billions of interactions, supported by energy-intensive infrastructure, is not. This distinction is neither novel nor controversial. It underpins analysis across environmental science, from plastic pollution to transport emissions.
Research emerging from the Massachusetts Institute of Technology (MIT News, 2025) notes that while generative AI processes can be more energy-intensive than conventional web queries, their significance arises only when considered at scale. The fixation on individual use, therefore, obscures the very phenomenon it seeks to explain.

The degradation of research into rhetoric: the “water per query” claim
Few examples better illustrate this collapse than the now-ubiquitous claim that each AI query consumes “a bottle of water.” This assertion stems from a 2023 study by researchers at the University of California, Riverside (Li et al.), which examined the water footprint of AI systems, including both direct cooling requirements and indirect water use associated with electricity generation.

The study itself is careful, conditional, and explicitly concerned with variability. Water usage is shown to depend on geographical location, cooling technologies, energy sources, and temporal demand. It offers aggregate, system-level estimates, not fixed per-interaction costs.
The popular formulation of this research, however, strips away these conditions and presents a contingent estimate as a universal constant. This is not simplification; it is distortion. It represents the transformation of empirical research into a moralised slogan—one that is easily repeated, but analytically empty.

When faulty reasoning becomes ethically consequential: AI and Female Genital Mutilation
The consequences of this degraded reasoning extend beyond environmental discourse. In some educational contexts, students have begun to assert that artificial intelligence is linked to practices such as FGM. Such claims are not merely incorrect; they are indicative of a deeper epistemological failure.

FGM is a centuries-old human practice, rooted in complex configurations of social normativity, gender regulation, and cultural continuity. It predates modern technological systems entirely. To suggest that AI “contributes” to such a practice is to collapse fundamental distinctions between cause, correlation, and communicative medium.
At most, digital technologies—including AI—may facilitate the dissemination of information about FGM, whether in the form of advocacy, education, or, indeed, misinformation. But dissemination is not causation. To conflate the two is to abandon the basic criteria by which claims are evaluated.

More troubling still is the ethical implication. By attaching a serious human rights issue to an unrelated technological narrative, such claims risk trivialising the practice itself. They convert a historically and culturally embedded phenomenon into a rhetorical device, thereby obscuring both its origins and its ongoing realities.
This is the point at which poor reasoning ceases to be merely inaccurate and becomes actively irresponsible.

Relative scale and the problem of misplaced emphasis
Even under conditions of accelerated growth, data centres are projected to account for less than 3% of global electricity demand by 2030 (IEA, 2024). This situates them within the broader environmental landscape without elevating them to a position of primary causality. Sectors such as transportation, heavy industry, and agriculture continue to exert far greater influence on global emissions.

This observation does not license complacency. It does, however, demand proportionality. To isolate artificial intelligence as a primary environmental antagonist is to misrepresent the distribution of impact and to misdirect analytical attention.

Why is this collapse in reasoning occurring
The persistence of these claims is not accidental. It is structurally produced.
Contemporary information ecosystems reward:

• brevity over nuance
• certainty over conditionality
• moral signalling over analytical precision

Complex, probabilistic research findings are compressed into simplified, emotionally resonant claims that circulate rapidly across digital platforms. Within educational contexts, these claims are often encountered prior to, and sometimes in place of, formal disciplinary frameworks for evaluating evidence.

The result is a form of reasoning in which:

• proximity is mistaken for causation
• scale is ignored
• and conditional findings are treated as universal truths

This is not simply a failure of knowledge. It is a failure of method.

The emerging AI divide
A further dimension, frequently overlooked in moral critiques of AI, concerns inequality of access. As UNESCO (2024) has noted, disparities in access to digital tools and AI literacy risk producing a new form of structural inequality—an “AI divide.”

In educational settings, this divide is already observable. Students with access to devices, connectivity, and guided instruction develop competencies that others do not. In such a context, the wholesale rejection of AI on moral grounds does not mitigate inequality; it reinforces it.

The ethical question, therefore, is not whether AI should be used. That question has already been answered in practice. The more pressing issue is whether access to its benefits, and the knowledge required to use it effectively, will be equitably distributed.

Conclusion: the restoration of analytical discipline
The environmental impact of artificial intelligence is real. It is measurable, increasing, and worthy of serious consideration. It is also neither singular nor unprecedented. It must be understood as part of a broader technological system whose scale predates AI and whose growth is now being accelerated by it.

More concerning than the technology itself is the manner in which it is being discussed. When conditional research findings are transformed into absolute claims, when communicative proximity is mistaken for causal relationship, and when moral urgency substitutes for evidential reasoning, the result is not informed debate but epistemic confusion.

If students are unable to distinguish between evidence, mechanism, and scale, they will not merely misunderstand artificial intelligence. They will lack the intellectual tools needed to evaluate any complex claim presented to them.

And that is a far more serious problem than AI itself.
​
人工知能（AI）が「環境を破壊している」という言説は広がっているが、その多くは証拠の単純化や誤解に基づいている。AIは突然現れた技術ではなく、既存のデータセンターやクラウド基盤の延長線上にある。確かにAIは電力需要を加速させているが、それは新たな問題の創出ではなく、既存システムの拡大である。個々のAI利用（例えば一回の検索）が環境に大きな影響を与えるという考えは誤りであり、問題は大規模な利用とインフラ全体にある。また、「一回の利用で水一本分消費する」といった主張は、条件付き研究を過度に単純化したものである。さらに、AIとFemale Genital Mutilationを結びつけるような議論は、因果関係の混同であり、深刻な人権問題を矮小化する危険がある。本質的な課題は、AIそのものではなく、証拠・仕組み・規模を区別できない思考の崩壊にある。教育においては、こうした複雑な問題を正しく評価する分析力の育成こそが求められている。

There comes a point in teaching Leaving Certificate English where the familiar approach begins to feel insufficient. The structure is reliable — theme, imagery, quotation, explanation — but with certain poems, particularly those of Eiléan Ní Chuilleanáin, that linear, “paint by numbers” method begins to falter. Translation is one such poem.
​
On first encounter, it resists clarity. Students describe it as “jagged,” “hard to follow,” even “unfinished.” And they are right — but not in the way they think. The difficulty is not a flaw in the poem; it is the poem’s method. At that moment, I found myself needing to step outside the standard exam framework and return to my own academic grounding — specifically, the work of Michel Foucault.

What Foucault offers is not a set of answers, but a way of seeing: that identity is not simply expressed, but produced; that institutions do not merely contain individuals, but shape what can be known, said, and even thought about them. In this light, Translation becomes far more than a difficult poem about a particular historical moment. It becomes an exploration of how people are rendered visible — or invisible — within systems of power.

The poem opens in what appears to be a recognisable institutional setting — a laundry, a place of labour, routine, and control. Under a traditional reading, this might lead quickly to historical contextualisation. But a Foucauldian perspective reframes the space: this is not merely a location, but a disciplinary environment, one that produces silence, uniformity, and compliance. The women within it are not simply present; they are constituted as subjects within its structures.
​
From this silence, a voice begins to emerge — “sharp as an infant’s cry.” It is a striking image, not because it clarifies meaning, but because it disrupts it. The voice is raw, immediate, and impossible to ignore, yet it is not fully formed. It lacks the coherence we expect of language. Here, Foucault’s insight becomes useful: the voice exists, but the system has already shaped the conditions under which it can be heard. What we are witnessing is not full expression, but partial emergence.

This partiality deepens in the poem’s treatment of language itself. The phrase “washed clean of idiom” is particularly revealing. Idiom carries culture, identity, and nuance — to be stripped of it is to be stripped of self. The speaker is left with a “temporary name,” an identity imposed rather than chosen. In Foucauldian terms, this is language functioning not as expression, but as classification. The individual is no longer a speaker, but a subject defined by the system.

The physical environment reinforces this process. In one of the poem’s most unsettling images, the labour of washing becomes corrosive rather than cleansing: “rotten teeth of soap” and “every grasp seemed melted.” The body itself becomes a site of control, worn down by repetition and exhaustion. What should purify instead erodes. Foucault describes such processes as the production of “docile bodies” — individuals shaped and subdued through routine, discipline, and labour. Ní Chuilleanáin renders this not as theory, but as lived experience.

As the poem moves forward, the imagery shifts beneath the surface. The women become “ridges under the veil, shifting” — barely visible, concealed, and unsettled. This is where the reading becomes almost archaeological. The past is not presented clearly; it must be inferred from traces, fragments, disturbances in the surface. Foucault’s “archaeology of knowledge” speaks to this: truth is not directly accessible, but must be reconstructed from what remains. The women are present, but not fully recoverable.

By the time we reach the poem’s closing movement — “I rise and forget” — we might expect resolution. There is, after all, a rising, a return. But Ní Chuilleanáin denies us closure. The act of rising is paired with forgetting. Identity is not restored; it is altered, incomplete. Even the act of recovery is shaped by the structures that erased it in the first place.

This is where the poem reveals its full complexity. It is not simply about giving voice to the silenced. It is about showing how that voice can never be fully recovered, because it has been shaped, constrained, and partially erased by the very systems we are trying to understand.

For students, this can be a difficult shift. The Leaving Certificate often rewards clarity, structure, and resolution. Yet here is a poem that offers none of these easily. The challenge, then, is not to simplify the poem to fit the exam, but to help students articulate why it resists simplification.

In practical terms, this means moving away from asking, “What is the poem about?” and towards asking, “Why can’t we fully understand it?” It means recognising that the poem’s fragmentation mirrors the fragmentation of identity it depicts. It means allowing students to sit with uncertainty, and then guiding them to express that uncertainty clearly and coherently.

Ultimately, stepping outside the linear approach was not a departure from exam preparation, but a deepening of it. By drawing on a Foucauldian framework — even implicitly — students can move from description to analysis, from surface meaning to conceptual understanding.

And perhaps most importantly, they come to see that some poems are not puzzles to be solved, but experiences to be interpreted — where meaning is not given, but constructed, and never fully complete.

✍️ 日本語要約（約700文字）このブログでは、エイレーン・ニ・フーリハーンの詩「Translation」を教える際に、従来の「型にはめた」読解方法では不十分であることを論じている。代わりにフーコー的視点を取り入れることで、この詩が単に意味を伝えるのではなく、いかにして権力や制度が個人の声やアイデンティティ、さらには「知り得ること」そのものを形作り、制限しているのかを明らかにしていると捉えることができる。詩の断片的で曖昧な構造は、沈黙させられた存在の不完全で不安定な回復を反映しており、むしろその「わかりにくさ」こそが本質である。したがって、生徒には詩を単純化して理解するのではなく、その曖昧さと向き合い、それを言語化する力を養うことが求められる。これは試験対策としても有効であり、表面的な要約から一歩進んだ概念的理解へと導くものである。