Abstract Retirement represents a significant life phase; however, individual experiences and perceived quality of life in retirement are highly diverse. This study explored the subjective views of Slovak working and non-working retirees regarding the opportunities retirement brings, using Q methodology. A set of 36 statements covering key domains (retirement conceptualisations, perceived gains and losses, retirement-related concerns, self-efficacy, and attitudes toward working after retirement) was ranked by 56 participants aged 62 to 73. Factor analysis revealed five distinct perspectives: (1) Life as Before , viewing retirement as a continuation of working life; (2) Slower Pace , favoring both retirement and work at reduced intensity; (3) Freedom , emphasizing retirement as an opportunity for personal fulfillment; (4) Enjoying Retirement , highlighting the right not to work while maintaining skills and financial stability; and (5) Stressed and Frustrated , reflecting dissatisfaction with mandatory retirement and concerns about financial security and loss of meaning. These perspectives illustrate the multifaceted and heterogeneous nature of retirement experiences, demonstrating how retirees evaluate their quality of life and the opportunities available to them. Findings underscore the importance of flexible retirement policies and personalized support systems that acknowledge the diversity of resources, capabilities, and retirement trajectories among individuals.
Abstract The rapid diffusion of generative artificial intelligence (AI) has raised fundamental questions about its effects on critical thinking and cognitive development. Evidence from recent empirical studies suggests a dual pattern: structured forms of AI assistance can sustain users’ cognitive activity and enhance engagement and performance, whereas more substitutive forms of use are associated with reduced autonomous reasoning and poorer subsequent independent performance. This article argues that these findings can be understood by considering how cognitive work is distributed between users and AI systems and how this distribution may evolve over time. In particular, I examine a six-step sequence in which successful complementary assistance can increase trust and delegation; greater delegation can remain complementary or become increasingly substitutive; and increasingly substitutive interactions can reduce the autonomous exercise of critical thinking and potentially hinder the development or maintenance of critical-thinking capacities over time. The available evidence provides substantial support for the first five steps considered individually and substantial, though less direct, support for the sixth. The article concludes by discussing educational, design, and social implications, arguing that preserving cognitive agency requires fostering complementary rather than progressively substitutive forms of interaction.
Abstract Generative artificial intelligence has intensified longstanding philosophical questions concerning human cognition and epistemic autonomy. This paper argues that generative AI does not make human beings smarter as such; its epistemic significance depends on how human–AI interaction is structured and normatively governed. Drawing on Kant’s conception of maturity ( Mündigkeit ), theories of extended cognition, and recent debates on cognitive offloading, it distinguishes instrumental from structural offloading: the former can extend human judgment, while the latter risks replacing autonomous reasoning with algorithmic dependence. On this basis the paper proposes the concept of AI maturity as the capacity to use generative AI in ways that preserve epistemic autonomy and reflective judgment. Against the objection that such an ideal presupposes an untenably individualistic epistemology, the account is situated within social epistemology: autonomy is defined not as independence from external authorities but as answerability for the standards governing one’s own judgments. This yields explicit criteria for distinguishing legitimate epistemic dependence from heteronomy, and locates the specific risk of generative AI not in dependence as such but in dependence on a non-answerable and epistemically correlated source.
Abstract This article reframes human–AI cognition assemblages by situating algorithmic systems within the philosophical and empirical tradition of collective intentionality. Rather than asking whether generative AI augments or erodes individual cognitive skills, the paper proposes that algorithms participate in distributed networks of shared epistemological purposes, norms, and meanings. Drawing on Searle’s social ontology, Tuomela and Gilbert’s accounts of group agency, and Tomasello’s empirical assessments, the paper establishes collective intentionality as foundational to how humans think and act together. It then introduces algorithmic intentionality – the concept that contemporary algorithmic systems function as infrastructures of distributed participation towards the coordination of collective cognition. Rather than neutral tools or autonomous agents, algorithms emerge here as sociotechnical assemblages that mediate and reshape the structures through which shared understanding and distributed cognition emerges.
