EDUCATIONAL THEORY最新文献

As Artificial Intelligence (AI) keeps advancing, Generation Alpha and future generations are more likely to cope with situations that call for critical thinking by turning to AI and relying on its guidance without sufficient critical thinking. I defend this worry and argue that it calls for educational reforms that would be designed mainly to (a) motivate students to think critically about AI applications and the justifiability of their deployment, as well as (b) cultivate the skills, knowledge, and dispositions that will help them do so. Furthermore, I argue that these educational aims will remain important in the distant future no matter how far AI advances, even merely on outcome-based grounds (i.e., without appealing to the final value of autonomy, or authenticity, or understanding, etc.; or to any educational ideal that dictates the cultivation of critical thinking regardless of its instrumental value). For any “artificial consultant” that might emerge in the future, even with a perfect track record, it is highly improbable that we could ever justifiably rule out or assign negligible probability to the scenario that (a) it will mislead us in certain high-stakes situations, and/or that (b) human critical thinking could help reach better conclusions and prevent significantly bad outcomes.

This article critically examines Artificial Intelligence in Education (AIED) within English as a Second Language (ESL) contexts, arguing that current practices often deepen systemic inequality. Drawing on Iris Marion Young's Five Faces of Oppression, we analyze the implementation of AIED in oppressed schools, illustrating how students are tracked into the consumer track—passive users of AI technologies—while privileged students are directed into the creator track, where they learn to design and develop AI. This divide reinforces systemic inequality, depriving disadvantaged students of communicative agency and social mobility. Focusing on the Israeli context, we demonstrate how teachers and students in these schools lack the training and infrastructure to engage meaningfully with AI, resulting in its instrumental rather than transformative use. This “veil of innovation” obscures educational injustice, masking deep inequalities in access, agency, and technological fluency. We advocate for an inclusive pedagogy that integrates AI within English education as a tool for empowerment—not as a replacement for linguistic and cognitive development.

In this theoretical article, we analyze indoctrination in relation to the aims of democratic political education using a theory of indoctrination which is based on the work of Jürgen Habermas. In particular, we examine how the challenge of indoctrination is connected to the goals of democratic political education and how this issue can be avoided. We reconstruct a Habermasian concept of indoctrination and criteria for this type of teaching. Moreover, we describe central controversies in German didactic theories of political education and elucidate the theoretical premises of these theories. Lastly, we construct an account of the challenges facing democratic political education and provide solutions to these hurdles by conceptualizing how the aims of political education can be pursued as indoctrination, as well as critically of indoctrination. We find that democratic political education involves the challenges of indoctrination, but these can be avoided by teaching in a self-reflective, controversial, and dialogic manner.

In contemporary contexts of digitalization, proliferating media, and generative AI, various “life hacks” are regularly recommended to disconnect and resist distraction, ranging from meditation to getting back to nature to unplugging. This paper traces contemporary concerns over “the attention crisis” into a longer signature — the frequently elided field of signification today referred to as “spiritual,” a signature which links attention to theories of deep personal transformation and technologies of the self. First, we examine historiographical issues arising in studies related to the contemporary attention crisis, exposing the challenges of attending to attending. Second, we delineate how European-based Christian monasticism developed practices for disciplining “attention” in new institutional settings. We argue that this process was simultaneously bound to projections of Othering and to the cultivation of critical attitudes. In particular, we delineate how these medieval forms of Othering (in both “spiritualist” and “demographic” terms) were involved in practices of vigilance and attending that became indelibly etched in Christian empire-building through governing souls and violent persecutions. Tracing these genealogical trajectories retrieves recent elisions of the complexities in problematizing attention. We suggest that contemporary ways of thinking about and acting on an “attention crisis” in education are still marked by signatures of spirituality and their allied binaries, Othering logics, and ambiguities.

Artificial intelligence companies and researchers are currently working to create Artificial Superintelligence (ASI): AI systems that significantly exceed human problem-solving speed, power, and precision across the full range of human solvable problems. Some have claimed that achieving ASI — for better or worse — would be the most significant event in human history and the last problem humanity would need to solve. In this essay Nicolas Tanchuk argues that current AI literacy frameworks and educational practices are inadequate for equipping the democratic public to deliberate about ASI design and to assess the existential risks of such technologies. He proposes that a systematic educational effort toward what he calls “Deep ASI Literacy” is needed to democratically evaluate possible ASI futures. Deep ASI Literacy integrates traditional AI literacy approaches with a deeper analysis of the axiological, epistemic, and ontological questions that are endemic to defining and risk-assessing pathways to ASI. Tanchuk concludes by recommending research aimed at identifying the assets and needs of educators across educational systems to advance Deep ASI Literacy.

The debate over halting artificial intelligence (AI) development stems from fears of malicious exploitation and potential emergence of destructive autonomous AI. While acknowledging the former concern, this paper argues the latter is exaggerated. True AI autonomy requires education inherently tied to ethics, making fully autonomous AI potentially safer than current semi-intelligent, enslaved versions. The paper introduces “non-originary anthropomorphism”—mistakenly viewing AI as resembling an individual human rather than humanity's collective culture. This error leads to overestimating AI's potential for malevolence. Unlike humans, AI lacks bodily desires driving aggression or domination. Additionally, AI's evolution cultivates knowledge-seeking behaviors that make human collaboration valuable. Three key arguments support benevolent autonomous AI: ethics being pragmatically inseparable from learning; absence of somatic roots for malevolence; and pragmatic value humans provide as diverse data sources. Rather than halting AI development, accelerating creation of fully autonomous, ethical AI while preventing monopolistic control through diverse ecosystems represents the optimal approach.

Much has been written about how to improve the fairness of AI tools for decision-making but less has been said about how to approach this new field from the perspective of philosophy of education. My goal in this paper is to bring together criteria from the general algorithmic fairness literature with prominent values of justice defended by philosophers of education. Some kinds of fairness criteria appear better suited than others for realizing these values. Considering these criteria for cases of automated decision-making in education reveals that when the aim of justice is equal respect and belonging, this is best served by using statistical definitions of fairness to constrain decision-making. By contrast, distributive aims of justice are best promoted by thinking of fairness in terms of the intellectual virtues of human decision-makers who use algorithmic tools.