Aug 2026· AI & SOCIETY· 0 citations· 33 references
TL;DR
The findings reveal that coaches report using GenAI extensively for structured, information-intensive tasks while maintaining control over relational and interpretative work, which can be understood as boundary work preserving human authority over domains of tacit expertise.
Abstract
The emergence of generative AI (GenAI) within human-centered guidance professions marks a transformative shift in society that transcends mere improvements in technical efficiency. With large language models (LLMs) becoming increasingly embedded in professional practices centered on care, trust, and relational knowledge, critical questions arise about how algorithmic systems reshape human agency, autonomy, tacit expertise, and the ethical foundations of professional relationships. This study examines these questions through coaching, a socio-cultural practice characterized by tacit expertise, empathetic engagement, and care obligations. Through a survey with 205 self-identified GenAI-using coaches, this research explores how algorithmic systems mediate professional practice, what tensions emerge between human-centered values and algorithmic logic, and what this may reveal, within the boundaries of this exploratory sample, about emerging patterns in human–AI relations. The findings reveal that coaches report using GenAI extensively for structured, information-intensive tasks while maintaining control over relational and interpretative work. This pattern can be understood as boundary work preserving human authority over domains of tacit expertise. Significantly, frequent GenAI users report higher scores on self-reported ethical behaviors and beliefs, yet the majority rely on proprietary LLM platforms, highlighting structural constraints that shape technological choices. These patterns highlight critical societal questions about the valuation of tacit expertise, trust formation in AI-mediated relationships, and design justice issues in AI development. The study contributes to understanding how AI integration may reconfigure, and not just augment, the ethical foundations and cultural understanding of professional practice, with urgent implications for shaping AI-mediated futures for the common good.
This research examined how public relations professionals conceptualize, enact, and advocate for responsible AI (RAI) during a period of rapid technological change. Drawing on 22 semi-structured interviews with experienced U.S.-based communications professionals and using constructivist grounded theory (Charmaz, 2006), the study examined how professionals navigate such core ethical values of truth, trust, and transparency while adopting AI and counseling their organizations. Findings reveal that responsible AI in public relations is not given or simply adopted but actively constructed through everyday professional judgment, human oversight, and organizational influence. The study finds that responsible AI practice depends on the interdependence of AI literacy and ethics literacy, with public relations professionals translating both into advocacy, governance, disclosure judgments, and counsel. The study introduces the AI-Ethics Literacy Model for Human Advocacy[trademark] (AELHA[trademark], pronounced “ah-LEE-ha”) as its original conceptual contribution. AELHA encodes a foundational relationship: AI Literacy + Ethics Literacy = Human Advocacy. The model proposes that neither competency alone is sufficient for responsible AI practice and that their combination enables public relations professionals to advocate not only for organizational publics, but for humanity as the emergent public of the AI age.
This article develops a sociology of generative knowledge for the age of AI. It treats contemporary systems as hybrid actors within sociotechnical networks and reframes classical anchors – Mannheim’s situatedness, Merton’s norms, and Latour’s distributed agency – for model-mediated inquiry. Generative knowledge is defined as knowledge organized to produce further knowledge through iterative, tool-mediated, and socially embedded processes. On this basis, the paper advances epistemic stewardship as a practical orientation that sustains human agency while harnessing computational acceleration. Stewardship is operationalized through transparency-by-design, provenance and traceability, calibrated trust, independent verification and red teaming, structured challenge routines, inclusivity in data and participation, and proportional delegation to machines. The account clarifies gains in discovery and education alongside risks from opacity, automation bias, feedback loops, and cognitive drift, and it specifies institutional reforms: standardized disclosure artifacts, replication triggers for AI-assisted claims, revised authorship taxonomies, and equitable access to compute, benchmarks, and community-governed datasets. A research agenda follows, calling for comparative evaluations of stewardship designs, longitudinal studies of hybrid practice, and field-specific protocols that link evaluation to adoption thresholds. The result is a framework that integrates social theory with design and governance guidance, aiming to convert acceleration into certified advance while keeping responsibility legible and contestable.
Paolo Granata· Journal of Emerging Perspect...· 0 citations
It is argued that this understanding of human-GAI engagement, as explained through epistemological beliefs, lays the foundation for alternative approaches to teaching and assessment, student interactions, professional development, and AI governance and policy, while noting that the framework remains an exploratory heuristic requiring empirical validation.
S. Strydom· Journal of Applied Learning...· 0 citations
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines. This paper presents a conceptual evaluation of the extent to which GenAI redistributes students'and teachers'ability to act in classrooms to produce knowledge, validate each other's claims, and create evidence of student learning while collaborating with and competing against humans. This evaluation draws on various theoretical paradigms, including Distributed Agency, Self-Determination Theory, Society 5.0, and Technology Integration Paradigms, including TPACK and SAMR. While all of the theoretical paradigms evaluated are relevant to the role of agency within education mediated by AI, none of them address the ongoing disparity regarding equitable distribution of power, ownership of the data used to mediate interaction, and accountability in relation to human-mediated interactions. As such, this paper introduces the Ecological Co-Agency Framework, which defines agency in terms of relational, regulatory, and pedagogical processes, conditioned by a defined commitment to human accountability for epistemological claims.
It is argued that social work requires a more nuanced, philosophically informed engagement with AI, one that recognises technology as neither neutral nor inevitable, but as socially shaped, value-laden, and co-constitutive of practice.
Large language model (LLM)-based applications are becoming increasingly integrated into everyday practices of communication, learning, and creativity. Their widespread adoption has intensified debates in AI ethics concerning how their societal significance should be understood and evaluated. Existing approaches to AI ethics have developed important concepts and governance frameworks for evaluating the consequences of AI systems, particularly in relation to their design, deployment, and identifiable impacts. These approaches have proven indispensable for analyzing harms, assigning responsibility, and guiding governance. However, the widespread incorporation of LLMs into everyday practices also raises questions about more gradual and cumulative transformations that emerge through repeated human–LLM interaction. Drawing on John Dewey’s pragmatist account of inquiry, habit formation, and moral reconstruction, we distinguish between consequences, identifiable outcomes that can be evaluated within existing normative and regulatory frameworks, and effects, the cumulative transformations of the habits and conditions through which inquiry, judgment, and action are organized over time. We argue that interactions with LLM-based applications give rise to both consequences and effects, and that these should be understood as complementary rather than competing analytical perspectives. Building on this distinction, we use Dewey’s conception of habits to examine how repeated engagement with LLMs may contribute to transformations in three domains: epistemic inquiry, affective-relational self-understanding, and cultural meaning-making. Rather than proposing an alternative to existing AI ethics, we argue that attending to effects complements current approaches by directing ethical inquiry towards the cumulative transformations that emerge through everyday human–LLM interaction.