AIEd Knowledge BaseAIEd KB
On this page

Research Article

Generative AI as a Mediational Agent: Rethinking Learning in Sociocultural Theory

created 2026-08-24 · updated 2026-08-24 · sociocultural learning, learning theories, generative ai, constructivist, distributed cognition, pedagogy

Synthesis: Warschauer, Tate, and Ritchie (2026) argue that Generative AI challenges a foundational distinction in sociocultural theories of learning: the separation between mediational means and social interaction. Whereas traditional tools such as language, writing, and educational technologies mediate human activity while learning arises through social participation, generative AI both mediates activity and produces context-sensitive, contingent contributions that shape ongoing interaction. Because treating AI as a mere tool underestimates its interactional influence, while treating it as a collaborator wrongly attributes intentionality, accountability, and community membership, the essay proposes the concept of the mediational agent — a responsive but non-accountable system that mediates human action while contributing explanations, critiques, questions, and suggestions. This reconceptualization shifts attention from technological capability to forms of participation, motivating "human-first" habits that preserve learners' judgment, agency, and responsibility in AI-mediated learning.

The essay opens from nearly a century of sociocultural theory organizing learning around two complementary but analytically distinct sources: mediational means that transform human activity and social interaction that drives development through participation with teachers, peers, and communities. Over decades this distinction has been refined rather than abandoned, as work on distributed cognition and cognitive partners showed that technologies reorganize participation and redistribute cognitive work. Yet one assumption has remained stable: technologies transform learning by mediating human activity but do not themselves contribute contingent turns to unfolding interaction. Generative AI places pressure on this assumption because large language models generate novel, context-sensitive contributions — explaining, critiquing, suggesting, questioning, and revising — that emerge dynamically rather than from scripted routines. This positions generative AI in an ambiguous space that is neither a traditional tool nor a social participant in the ordinary sense of learning theory.

The resulting conceptual responses fall into two broad camps. Some scholars extend sociocultural accounts by treating generative AI as the latest and most sophisticated mediational tool, emphasizing how it augments human cognition. Others emphasize its conversational and adaptive qualities, describing it as a collaborator, teammate, dialogic partner, or tutor. Both capture important aspects of AI-mediated activity, yet each leaves something unexplained: treating AI solely as a tool underestimates its interactional influence, while treating it as a collaborator risks attributing intentionality and accountability that remain uniquely human. The conceptual challenge, the authors argue, is not deciding whether AI is a tool or a partner but understanding how systems that simultaneously mediate and generate interactional contributions fit within a framework built on that distinction.

The Mediational Agent

The paper proposes mediational agent as a distinct conceptual category. By this they mean a system that simultaneously mediates activity and generates context-sensitive contributions that influence the unfolding of interaction without possessing intentionality, social membership, or accountability. Like traditional mediational means, generative AI reorganizes how learners write, reason, solve problems, and communicate. Unlike them, it contributes novel utterances, explanations, critiques, questions, and suggestions that become part of the activity itself, contingent on learners' evolving inputs.

At the same time, mediational agents differ fundamentally from human participants. They do not occupy positions within communities of practice, share histories, pursue goals, or assume responsibility for the consequences of their contributions; their apparent participation emerges from probabilistic language generation rather than intentional engagement. The concept therefore identifies a hybrid form of participation rather than a midpoint on a continuum between tools and people. Generative AI performs functions that historically belonged to opposite sides of a conceptual distinction — the novelty lies not in its intelligence but in its participation. This matters for Pedagogy because activity systems that include mediational agents are organized differently: learners must continually decide whether to accept, reject, revise, interrogate, or redirect AI-generated contributions, making those judgments part of the learning activity itself.

Learning with Mediational Agents

If generative AI functions as a mediational agent rather than a tool or collaborator, then the central educational challenge is not learning to use AI effectively but learning to participate productively with responsive yet non-accountable contributors. The educational question shifts from What can AI do? to How should learners engage with systems that both mediate action and shape interaction? This reflects a tension between product and process: generative AI is exceptionally effective at improving immediate performance, yet learning depends on processes such as generating ideas, wrestling with uncertainty, evaluating alternatives, and revising understanding. Systems designed to reduce cognitive effort therefore create a developmental paradox — the same capabilities that improve immediate performance can reduce opportunities for intellectual growth when they replace rather than support learners' own thinking.

As a response, the essay proposes human-first habits of participation, framed not as a comprehensive pedagogy but as dispositions that help learners maintain authorship of meaning. These include establishing the primacy of human cognition by developing ideas before turning to AI; engaging AI purposefully to extend, challenge, or refine thinking; exercising supervisory agency by directing, constraining, and evaluating AI participation; sustaining epistemic vigilance that never confuses fluent language with truth; and cultivating reflective self-regulation that monitors what has genuinely been learned. These habits are not generic digital competencies — their expression depends on disciplinary practice, so supervisory agency and epistemic vigilance take different forms across historical inquiry, software engineering, scientific modeling, and literary interpretation.

What Changes for the Learning Sciences

Viewing generative AI as a mediational agent redirects attention toward different questions about learning itself. Rather than asking whether AI improves outcomes or increases productivity, researchers are encouraged to examine how patterns of participation change when responsive but non-accountable systems become part of everyday activity. Longstanding questions take on new forms. Studies of Scaffolding have focused on support provided by teachers, peers, or designed environments, but mediational agents generate support dynamically without participating in the social relationships that historically gave scaffolding its developmental significance. Likewise, research on distributed cognition examines how cognitive work is shared across people and artifacts; generative AI raises the distinct question of how cognitive responsibility should be distributed when artifacts contribute to reasoning itself.

The concept also suggests new directions for empirical work — examining how learners negotiate authority, responsibility, and judgment during interaction with mediational agents, making supervisory agency, epistemic vigilance, and reflective Regulation central objects of analysis rather than secondary concerns. The paper concludes that generative AI does not require abandoning sociocultural theory but reconsidering one of its most enduring conceptual distinctions. The value of the concept will rest not on whether the term endures but on whether it helps the field notice phenomena that existing categories leave unexplained and ask productive new questions about learning in AI-mediated environments. The overarching challenge for education is ensuring that learners remain authors of meaning within environments in which technologies increasingly contribute to its production.

Connected Concepts

Connected Articles

Citation

(2026). Generative AI as a Mediational Agent: Rethinking Learning in Sociocultural Theory. EdArXiv preprint.

SourceHistoryEditChat

Embed this page

Copy the code below to embed a chromeless version of this page in a learning management system or other website. The embedded view hides the site header, navigation, and footer.