Ahmet Küçükuncular

Artificial intelligence (AI) is increasingly entangled with teachers’ daily labor – from learning management systems and adaptive platforms to generative assistants and algorithmic evaluations. Drawing on Marx’s theory of alienation and labor process theory, this chapter examines how AI reconfigures teachers’ autonomy, authorship, practical wisdom, and relations with students and institutions. I develop an analytic framework that maps four forms of alienation (from product, process, others, and self) to common AI uses in schools and universities. Using illustrative vignettes from recent studies and policy cases, I show how AI can both intensify control through performance dashboards, scripted curricula, and automated surveillance, and enable craft restoration via planning support, differentiation at scale, and accessible feedback. To move beyond the binary of automation versus augmentation, I propose design and governance principles – transparency, contestability, situated consent, professional oversight, and teacher-led evaluation – that center pedagogical judgment. Overall, I argue that responsible AI in education is not simply a matter of technical accuracy or fairness but of protecting teachers’ capacity to care, create, and exercise discretion.

Digital sovereignty claims are reshaping how cyberspace is governed. This conceptual article develops a two‑level framework that distinguishes transnational, multi‑stakeholder cyberspace governance from jurisdiction‑bound digital sovereignty, and evaluates both through three ethical lenses – human rights, public‑interest/utility, and accountability/democratic legitimacy. The analysis maps key tensions (sovereignty vs. global openness, privacy vs. surveillance, innovation vs. control) and examines how AI amplifies them. It identifies risks from disproportionate state surveillance, platform concentration, and the marginalisation of non‑Western and community perspectives. In response, the article advances actionable principles: a “minimum commons” to preserve interoperability of core internet functions; cyber due process for state measures and platform enforcement (legality, necessity, proportionality, reason‑giving, and appeal); proportionate‑by‑design content and security rules; inclusion mechanisms and research access to support oversight; and risk‑tiered, human‑supervised AI governance. The argument is that ethical governance requires not only regulatory instruments but a reframing of sovereignty as stewardship operating within – rather than above – the cooperative fabric of the global internet.

Artificial intelligence is increasingly presented as a tool for improving efficiency, autonomy, and innovation in education. But what does AI mean for teachers as workers? Beyond the Blackboard offers a critical examination of how artificial intelligence reshapes teaching labour, professional identity, and autonomy. Drawing on empirical research and critical theory, the book explores how AI driven systems influence teachers' control over their work, their relationship to educational outcomes, and their sense of meaning and fulfilment within institutional settings. Grounded in Marxist theories of alienation and contemporary debates in AI ethics, the book analyses education not simply as a site of technological adoption, but as a workplace increasingly shaped by algorithmic management, performance metrics, and data driven governance. It challenges optimistic narratives that frame AI as inherently empowering, instead asking how structural conditions shape teachers' lived experiences of technological change. Written in an accessible but academically rigorous style, Beyond the Blackboard will be of interest to scholars, postgraduate students, educators, and policymakers concerned with artificial intelligence in education, teacher autonomy, and the ethical implications of educational technology. The book contributes to ongoing discussions in educational philosophy, AI ethics, and the political economy of education by foregrounding labour, power, and alienation in the age of intelligent systems.

Calls that pair ethical and conscious AI with ethical and conscious robots may feel natural. Many contemporary robots use machine learning, and many AI systems are described in agentive terms. Yet the pairing can hide a conceptual shortcut. It quietly suggests that AI ethics and robot ethics are the same moral question applied to different shells. My claim in this opinion piece is modest but consequential: treating robotics and AI as a single moral category encourages avoidable category mistakes about where moral agency sits, where harms arise, how responsibility is attributed, and what consciousness claims could plausibly mean in deployed systems.

The COVID-19 pandemic exposed acute tensions in the United Kingdom’s National Health Service (NHS) between its egalitarian self-image and the utilisation of overtly utilitarian tools such as QALY-based cost-effectiveness and prognosis-driven triage. This paper offers a systematic philosophical diagnosis of those tensions through the lens of refined utilitarianism, a consequentialist theory that grounds moral rules in their long-run welfare effects while requiring genuine social approbation and cultural fit. After reconstructing the classical act-/rule-utilitarian debate and engaging canonical critics (Rawls, Scanlon, Dworkin and Williams), the paper distinguishes refined utilitarianism from both rule utilitarianism and prioritarianism, then tests the theory against UK healthcare practice pre- and post-pandemic. Using documentary analysis of NICE technology appraisals, BMA emergency guidance, and statutory equality duties, it is shown that the NHS operates a two-tier code: egalitarian, need-based rules in conditions of routine capacity, and outcome-maximising rules, tempered by fairness safeguards, during acute scarcity. This structure closely matches refined utilitarian predictions: apparently non-utilitarian norms (equal access, prohibition of direct age discrimination, ‘rule of rescue’) are retained because they enhance trust, compliance and therefore aggregate welfare, while harsher maximisation moves (frailty scoring, ICU reallocation, staff testing priority) are publicly justified and time-limited to preserve legitimacy. The analysis answers familiar objections, impracticality of hedonic calculus, neglect of minorities, integrity threats, by showing how socially embedded rules mitigate each problem without surrendering the aim of greatest overall benefit. The paper concludes that UK healthcare is best interpreted as utilitarian in a refined sense, and that refined utilitarianism provides a practicable, normatively attractive template for future resource-allocation frameworks in public health emergencies and beyond.

The COVID-19 pandemic exposed acute tensions in the United Kingdom’s National Health Service (NHS) between its egalitarian self-image and the utilisation of overtly utilitarian tools such as QALY-based cost-effectiveness and prognosis-driven triage. This paper offers a systematic philosophical diagnosis of those tensions through the lens of refined utilitarianism, a consequentialist theory that grounds moral rules in their long-run welfare effects while requiring genuine social approbation and cultural fit. After reconstructing the classical act-/rule-utilitarian debate and engaging canonical critics (Rawls, Scanlon, Dworkin and Williams), the paper distinguishes refined utilitarianism from both rule utilitarianism and prioritarianism, then tests the theory against UK healthcare practice pre- and post-pandemic. Using documentary analysis of NICE technology appraisals, BMA emergency guidance, and statutory equality duties, it is shown that the NHS operates a two-tier code: egalitarian, need-based rules in conditions of routine capacity, and outcome-maximising rules, tempered by fairness safeguards, during acute scarcity. This structure closely matches refined utilitarian predictions: apparently non-utilitarian norms (equal access, prohibition of direct age discrimination, ‘rule of rescue’) are retained because they enhance trust, compliance and therefore aggregate welfare, while harsher maximisation moves (frailty scoring, ICU reallocation, staff testing priority) are publicly justified and time-limited to preserve legitimacy. The analysis answers familiar objections, impracticality of hedonic calculus, neglect of minorities, integrity threats, by showing how socially embedded rules mitigate each problem without surrendering the aim of greatest overall benefit. The paper concludes that UK healthcare is best interpreted as utilitarian in a refined sense, and that refined utilitarianism provides a practicable, normatively attractive template for future resource-allocation frameworks in public health emergencies and beyond.

The integration of AI into education is often framed as a neutral or beneficial response to pressures of efficiency, scalability, and personalisation. In this paper, I challenge that framing by examining how educational AI reshapes teaching as a form of labour. Drawing on Karl Marx’s theory of alienation, I offer a conceptual analysis of how AI mediated systems reorganise pedagogical work in ways that risk estranging teachers from the products of their labour, the labour process itself, their species being, and their relationships with students and colleagues. Rather than treating AI in education as a monolithic phenomenon, I differentiate between generative tools, automated assessment, learning analytics, and administrative systems, showing how each participates differently in processes of standardisation, surveillance, and managerial control. I situate educational AI within wider dynamics of platform capitalism, datafication, and audit culture, arguing that alienation is not an inevitable outcome of technology but a contingent effect of ownership structures, governance arrangements, and institutional imperatives. I conclude by outlining policy, design, and philosophical interventions aimed at reducing alienation, while acknowledging the limits of reform within marketised education systems. In doing so, I reframe AI in education as a political and ethical question about labour, authority, and the purpose of teaching, rather than a purely technical innovation.

This study critically investigates the ethical and structural implications of artificial intelligence (AI) integration into higher education through Karl Marx’s theory of alienation. Drawing upon empirical data from a survey of 395 educators in Northern Cyprus, an illustrative context characterised by nascent AI adoption, the research identifies significant experiences of alienation among educators arising from AI-driven transformations of academic labour. Alienation from the product of academic labour, marked by diminished sense of ownership and authorship over teaching outcomes, emerged as particularly pronounced. Further, alienation from educational processes, professional identity, and interpersonal relations also surfaced prominently, highlighting the multifaceted pressures imposed by algorithmic management practices and standardisation in higher education settings. Positive perceptions of AI were significantly correlated with reduced alienation across all dimensions, although such optimism alone could not eliminate structural tensions inherent in AI implementation. The nuanced and fragmented nature of educators’ attitudes toward AI underscores the necessity for nuanced, context-sensitive governance approaches. The findings underscore the urgent need for higher education institutions to adopt ethical governance frameworks that prioritise transparency, participatory decision-making, professional autonomy, and robust ethical oversight. In doing so, universities can better harness AI’s educational potential while preserving the professional integrity, creative agency, and relational ethics at the heart of academic practice.

I defend a diachronic constraint on justification as a necessary condition for knowledge. In my view (JTB + D), a belief is knowledge-apt only if its justification is maintainable over a context-sensitive interval Δ under ordinary avenues of evidence-accrual, including reliable memory, testimony, and communal inquiry, with no accessible undefeated defeaters arising within that interval. This temporal, process-sensitive requirement mitigates Gettier-style luck by targeting “snapshot” justification that would easily collapse under minimal further scrutiny (as in Fake Barn County), while avoiding infallibilism and over-intellectualism. I calibrate Δ by stakes and domain volatility to avoid vagueness and moving goalposts, distinguish responsive stability from mere habituation, and show how the account handles no-new-evidence scenarios without undermining ordinary memorial and testimonial knowledge. Conceptually, the proposal integrates internalist and externalist insights as it preserves reason-responsiveness over time and serves as an actual-world temporal analogue of safety, not a standalone fourth ‘dimension’. I engage canonical cases and acknowledge Zagzebski’s challenge: the view does not promise full Gettier immunity, but it raises the bar for counterexamples in ordinary environments. The result is a principled, parameterised refinement of the justification condition that better captures knowledge as an enduring, responsibly supported true belief.

This study critically examines the integration of artificial intelligence (AI) into education through the lens of Marx’s theory of alienation, engaging with contemporary critiques of digital capitalism and academic labour. Drawing on an exploratory survey of 395 educators in Northern Cyprus, a context of early-stage AI adoption, the paper identifies four distinct forms of alienation exacerbated by AI: from the product of academic labour, from the educational process, from professional identity (species-being), and from interpersonal relations. Findings suggest that while educators who view AI more positively tend to report lower levels of alienation, particularly with respect to their pedagogical outputs, this association is tentative due to the low reliability of the AI perception scale (Cronbach’s α = 0.42). The results, therefore, serve as hypothesis-generating rather than conclusive. Situating the empirical findings within broader critiques by Noble, Hall, Preston, and Komljenovic, the study highlights how algorithmic governance, commercial platform logics, and data-driven performance regimes threaten teacher autonomy, creativity, and relationality. The paper concludes with a call for participatory governance, ethical oversight, and human-centred design to ensure that AI integration supports, not supplants, educators. In doing so, it contributes to critical debates on the ethical sustainability of digital education under conditions of intensifying automation.

The prospect of conversing with animals, once the stuff of fable, is drawing closer with the rise of AI systems capable of decoding nonhuman communication. From Baidu’s patented translator prototypes to bioacoustic machine learning initiatives, the technical frontier is advancing rapidly. Yet with these breakthroughs come urgent ethical questions. What does it mean to speak with a nonhuman species, and what obligations follow from that dialogue? This paper explores the moral landscape of AI-mediated interspecies communication, examining its potential to advance animal welfare, illuminate nonhuman minds, and challenge anthropocentric hierarchies. It interrogates whether language confers moral standing or merely reveals it, and considers how concepts such as autonomy, consent, privacy, and dignity might apply to animals when communication is technologically enabled. The analysis draws on animal ethics, AI governance, and political theory to argue for proactive, principled guardrails that prioritise animals’ interests over human convenience. Ultimately, the paper contends that such technology, if governed ethically, could help recalibrate our relationship with other species, not by bestowing personhood, but by listening more justly to what was already there.

This study critically investigates the ethical and structural implications of artificial intelligence (AI) integration into higher education through Karl Marx’s theory of alienation. Drawing upon empirical data from a survey of 395 educators in Northern Cyprus, an illustrative context characterised by nascent AI adoption, the research identifies significant experiences of alienation among educators arising from AI-driven transformations of academic labour. Alienation from the product of academic labour, marked by diminished sense of ownership and authorship over teaching outcomes, emerged as particularly pronounced. Further, alienation from educational processes, professional identity, and interpersonal relations also surfaced prominently, highlighting the multifaceted pressures imposed by algorithmic management practices and standardisation in higher education settings. Positive perceptions of AI were significantly correlated with reduced alienation across all dimensions, although such optimism alone could not eliminate structural tensions inherent in AI implementation. The nuanced and fragmented nature of educators’ attitudes toward AI underscores the necessity for nuanced, context-sensitive governance approaches. The findings underscore the urgent need for higher education institutions to adopt ethical governance frameworks that prioritise transparency, participatory decision-making, professional autonomy, and robust ethical oversight. In doing so, universities can better harness AI’s educational potential while preserving the professional integrity, creative agency, and relational ethics at the heart of academic practice.

AI, we’re told, will set us free from the toil of labour. Alexander Sidorkin calls this ‘liberatory alienation’ – the idea that automation will take over the drudgery of work, AI will handle the menial, and humans will ascend to a new age of creativity and self-fulfilment . But peel back the techno-optimist paint, and you’ll find the same old chains – only now algorithmically polished. Let’s not kid ourselves: under corporate rule, AI isn’t unshackling anyone; it’s shackling them tighter. Your labour isn’t liberated; it’s surveilled, siphoned, and scrutinised into oblivion. In familiar Silicon Valley jargon, the chains aren’t gone; they’re just upgraded to version 2.0.

"Into the Abyss" is a poignant novella that explores the journey of a high school student grappling with existential questions and the fragility of life. The story begins with the protagonist leading a typical suburban life, feeling disconnected from the routine of school and family. The arrival of Jamie, a new student, brings a breath of fresh air, sparking deep conversations about life's meaning and purpose. Their friendship deepens as they navigate high school's social dynamics, sharing philosophical discussions and feeling a strong bond. However, beneath this friendship and routine, a sense of restlessness and a hunger for something more meaningful grow within the protagonist. Tragically, the narrative takes a dramatic turn when a devastating earthquake strikes, causing widespread destruction and loss of life. This event serves as both a literal and metaphorical upheaval, deeply affecting the protagonist's worldview. The earthquake’s aftermath is marked by communal grief, introspection, and a profound reevaluation of life's priorities. As the protagonist and Jamie struggle with existential questions heightened by the catastrophe, their friendship becomes a source of comfort and understanding. Yet, beneath the surface, Jamie grapples with deeper, darker thoughts, eventually leading to a tragic conclusion - Jamie's suicide. This shocking turn of events plunges the protagonist into a profound existential crisis. The indifference of Jamie's family and the world towards this loss exacerbates the sense of isolation and despair. The protagonist is forced to confront the nihilistic implications of their philosophical inquiries, questioning the meaning of life in the face of such indifference and tragedy. In the end, the protagonist is left to navigate a world irrevocably changed, struggling to find a way to live with the questions and uncertainties that Jamie's death has brought to the forefront. The novella concludes with the protagonist continuing their journey of self-discovery and existential exploration, despite the profound impact of loss and the haunting realization of life's fragility and the universe's indifference.

This article examines the urgent need for a unified philosophy of incarceration as humanity prepares for the challenges of Mars colonization. Through a comparative analysis of Earth’s diverse criminal justice models – highlighting the punitive approach of the United States and the rehabilitative model of Norway – it reveals the limitations of traditional penal philosophies when applied to the unique conditions of space. The distinctive demands of extraterrestrial colonization, including isolation, resource scarcity, and the critical necessity for communal cohesion, call for a paradigm shift towards restorative justice frameworks. These frameworks emphasize rehabilitation, community service, and social reintegration over punitive measures. The paper argues for a globally harmonized approach to criminal justice that is responsive to the constraints of space environments while embodying humanity’s highest ideals of justice and dignity. This vision aims to ensure the security, well-being, and harmonious coexistence of future extra-terrestrial societies, proposing innovative and humane strategies for addressing crime and punishment beyond Earth.

This paper argues that ethical AI cannot be fostered in a vacuum, challenging the perspective that AI ethics research should be isolated from technological advancements and industry collaborations. It refutes the argument presented by Gerdes (Discov Artif Intell. 2022;2(25)), which suggests that industry involvement inherently undermines the integrity of AI ethics research. Through an exploration of historical and contemporary examples of successful academia-industry collaborations, the paper advocates for a synergistic approach that harnesses industry resources and insights to advance ethical AI development. Emphasising the importance of diverse funding, the value of industry insights, and the impracticality of separating AI ethics from computer science, the paper contends that a collaborative, transparent, and inclusive model of AI ethics research is essential for developing practical, relevant, and ethically sound AI technologies aligned with societal values and norms.