Recontextualization

In the AI Story

The practical implementation of recontextualization involves four elements Lave's framework identifies as what tools cannot provide: legitimate peripheral participation (the gradual trajectory from newcomer to full practitioner), community membership (the social infrastructure of professional knowledge), the context of struggle (the friction-rich encounters through which tacit understanding is deposited), and the social production of meaning (the collaborative process through which standards are created, maintained, and evolved).

In software engineering practice, recontextualization means designated AI-free zones for specific categories of work — debugging, architecture review, system diagnosis — where the struggle of doing the work without AI assistance deposits contextual understanding the tool cannot provide. It means redesigning code reviews as situated learning events rather than quality gates. It means structuring mentorship around collaborative practice rather than supervision of output. The interventions are inefficient by output metrics, which is the point: the output can be produced by the tool; the understanding must be produced by the practice.

In education, recontextualization is more challenging because the institutional structures are more rigid. Assessment systems reward output over understanding, answers over questions, thin knowledge over thick. A teacher who grades questions rather than answers — who evaluates students on the quality of their inquiry rather than the quality of their output — is performing a recontextualization. But she must still prepare her students for a system that grades the opposite. The redesign of learning contexts ultimately requires the redesign of the institutional structures within which learning occurs.

The response is expensive. It is slow. It is inefficient by every output metric. It requires organizations to invest in interactions whose value is invisible in the short term and indispensable in the long term. It is also, Lave's framework insists, the only path that sustains the thick, situated, contextually embedded knowledge on which professional judgment depends. The alternative is a professional culture that is more capable on the surface and less prepared for the conditions where surface capability is insufficient.

Edo Segal's epilogue to On AI describes his own implementation: "situated zones" built into his development process, specific categories of work designated as AI-free not because the tool cannot do them but because the struggle of doing them without the tool deposits something the tool cannot. The zones are not permanent — they are stages, sequenced like the Liberian apprenticeship, designed to ensure that each engineer builds a minimum foundation of contextual understanding before the tools become her primary interface with the system.

Origin

The concept is developed in Chapters 8 and 9 of On AI, synthesizing Lave's framework of situated cognition with the specific institutional challenge posed by large language models. It draws on parallel prescriptive work in organizational learning (Wenger's Cultivating Communities of Practice) and on the broader tradition of deliberate practice design in expertise research.

Key Ideas

Recontextualization is design work. It requires deliberate institutional choices about which activities are AI-mediated and which preserve situated engagement.

The four elements AI cannot provide. Legitimate peripheral participation, community membership, the context of struggle, and the social production of meaning — these must be preserved by institutional design.

AI-free zones are not refusals. They are stages in a trajectory, designated on the basis of what contextual understanding the struggle deposits rather than what the tool cannot produce.

The costs are real and invisible. Recontextualization is inefficient by every output metric, which is precisely why organizations that optimize for output metrics tend not to invest in it.