Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy

Andresa Theodora; Nikolaos Tselios

Home/Archive/arXiv

arXiv · 2605.16296

Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy

Andresa Theodora·Nikolaos Tselios

View original source ↗Read source document ↗Export metadata ↓

Abstract

Artificial intelligence (AI) is increasingly reshaping lifelong learning by introducing new possibilities for personalized, flexible, and data-informed educational practices. In the field of adult education, AI has gained particular importance as learners are expected to continuously update their knowledge and skills in response to rapid technological, economic, and social change. This paper examines the role of AI in adult education policy, with a focus on both its opportunities and its challenges. It discusses how AI can support personalized learning, intelligent tutoring, learning analytics, and workforce development, while also contributing to greater accessibility, scalability, and policy responsiveness. At the same time, the paper highlights significant concerns related to the digital divide, data privacy, algorithmic bias, over-reliance on technology, and the readiness of educators and institutions to integrate AI effectively. Drawing on contemporary literature and international policy frameworks, the paper argues that AI should not be approached simply as a technological solution, but as a socio-technical and ethical issue that requires careful governance. It concludes that the successful integration of AI into lifelong learning depends on balanced adult education policies that promote inclusion, transparency, human-centered pedagogy, and responsible innovation.

Explore related subjects

cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Andresa Theodora, Nikolaos Tselios. 2026-04-15. Artificial Intelligence in Lifelong Learning: Opportunities and Challenges in Adult Education Policy. https://arxiv.org/abs/2605.16296

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation

Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates "a CEO in the United States," the prompt leaves demographic realization to the model. Existing group fairness definitions assume that sensitive attributes are given on the input side. Generative audits instead examine output-side demographic composition, yet the targets they compare it against are typically supplied rather than justified. The upstream question is what the target distribution should be. We formalize this missing-target problem for demographic-value-unspecified generation and decompose target construction into four commitments: the evaluative object, prior admissibility, allocation, and operationalization. In this framework, we admit the geographic prior under a geographic-membership interpretation for the declared public-world use. The occupational prior, under an incumbency interpretation, requires an independently defended objective such as workforce-composition fidelity. Instantiating this construction in AP-Bench, we find substantial distribution divergence from geography-derived targets, ranging from 0.508 to 0.606 on a 0-to-1 scale. Replacing each geography-derived target with an equal-category comparator, while holding generations and measurement fixed, produces model-specific mean absolute cell-level $\mathrm{JSD}_2$ changes ranging from 0.279 to 0.355. Target construction is therefore not a preliminary to fairness evaluation but a component of it. What we supply is not a universal target, but a framework that makes explicit the justification required before a distribution can serve as a fairness standard.

cs.CY↗

arXiv2026

Anticipatory Human Oversight of Agentic AI: A Philosophical Account

Human oversight is widely held to mitigate the risks of AI systems. Even for systems that produce discrete outputs at identifiable decision points, the realisation of human oversight as a reactive measure is empirically fragile, yet increasingly well understood. However, for agentic AI -- systems that plan, decompose goals, and execute multi-step actions over extended horizons -- reactive oversight reaches its structural limits: intervention on individual actions defeats the autonomy that motivates the deployment, while intervention on aggregate patterns is too coarse for harms whose cumulative consequences only become legible after the fact. This paper argues that reactive oversight must be complemented by an anticipatory mode: oversight exercised before the agent acts, by specifying the normative agenda that structures the space of permissible action and refining it iteratively through specification, runtime, and inspection. The two are complements -- the agenda's escalation conditions specify when reactive intervention is invoked. Drawing on Meaningful Human Control, we read anticipatory oversight as the operationalisation of distal-reason tracking. In addition, we argue that the proposed framework yields a specific responsibility architecture by design: occupying the anticipatory mode is the discharge of a role-grounded prospective obligation, and backward-looking responsibility takes the form of strict moral answerability -- rationalistic, relational, and holding regardless of fault, in virtue of the principal's prior opportunity for precaution. We develop bridging failure modes, address objections including moral luck and the illusion of control, and close with regulatory, architectural, and empirical implications

cs.CY↗

arXiv2026

Critical Data Studies in the Anthropocene

This chapter introduces the concept of the Anthropocene into critical data studies, a field that has, for the past decade, explored the entanglements between datafication and politics. With the scaling up of contemporary datafication alongside generative computing, it is essential to expand these debates, both theoretically and methodologically, to consider the logics of extraction and exploitation inherent in the politics of artificial intelligence. Critical data scholars are called to interrogate how dominant narratives around the materiality of contemporary datafication either obscure or reveal its environmental impacts, along other key questions such as who benefits from these logics. To illustrate this, this chapter brings two case studies from Spain and Chile and explores how the infrastructure of contemporary datafication intersects with existing power structures, influencing which social and environmental consequences are recognized, addressed, and neglected. Finally, it invites critical data scholars to keep exploring the politics of data, infrastructure, and the Anthropocene by blending discipline boundaries between science and technology studies, media, geography, and political ecology.

cs.CY↗