The paper is conceptual and programmatic rather than empirical: it offers concepts for future research and classroom design, not evidence that the approach improves test scores, attainment or other learning outcomes. The proposal begins with a change in what learners use to think. In Papert’s account, the Logo turtle was an “object to think with”: it responded to symbolic commands, and learners externalized their intentions through a cycle of coding, observing results and debugging errors.

Generative systems, Levin, Semenov and Gorsky argue, can instead function as bounded “agents to think with,” interpreting natural-language requests, generating alternatives and sometimes asking clarifying questions. Their responses are not conscious, intentional or morally accountable, but they introduce contextual variation into interactions that were previously organized around explicit instructions. This does not make formal computation obsolete.

Instead, it creates a hybrid environment in which learners, generative models, conventional software, symbolic tools and empirical observations can jointly shape an inquiry. Peirce’s semiotic distinction provides the framework’s technical foundation. A symbol acquires meaning through convention, an icon resembles what it represents, and an index is connected to its object by an existential or causal relation.

The authors distinguish three analytically separable forms of indexicality. Intralinguistic contextuality occurs when an expression is conditioned by other expressions; interactional indexicality occurs when its significance depends on a particular history of utterances and actions; and empirical indexicality occurs when signs connect to objects or events through observation, measurement, embodiment or a causal trace. A text-only language model considered in isolation has limited empirical grounding, but a complete human–AI configuration may acquire it through documents, software execution, sensors, measurements and participants’ contact with the world.

Meaning is therefore not inherently absent from generative AI; its connection to the world varies with the technical system and the situation in which it operates. To compare learning environments, the paper introduces three conceptual axes. The semiotic axis runs from predominantly symbolic to indexical meaning-making; the psycho-epistemological axis from learning regulated mainly by the individual to learning co-regulated through people and tools; and the pedagogical axis from instructionist transmission to constructionist making.

The authors position classical computer-assisted instruction near the symbolic, individual and instructional corner, Logo near the constructionist edge, collaborative coding as more dialogic, and AI-supported studios near the indexical, dialogic and constructionist corner. The numerical coordinates are illustrative author judgments rather than measurements. They also propose that movement toward situated meaning, dialogue and learner authorship may each support motivation, mapping these tendencies onto competence, relatedness and autonomy.

The authors explicitly describe that motivational relationship as a plausible, untested hypothesis that could also be affected by teacher personality, prior experience, affective state and social context. Dialogue has a central place in the proposed pedagogy because generative systems could make sustained, individualized conversation more economically feasible than conventional classrooms allow. The envisioned arrangement includes a learner, a teacher and an AI extension of the teacher.

The authors particularly favor using small or very small language models alongside larger systems because their visible errors can become objects of joint analysis rather than obstacles hidden behind a polished answer. A program with a detectable fault, for example, or a mathematical claim that requires scrutiny, can become material for inquiry. The proposed sequence is hypothesis, evaluation, justification and integration.

AI may help generate candidates, execute tools or reveal possible consequences, but learners should still compare claims with evidence and alternatives, explain why one explanation is preferable and connect the result to existing knowledge. The framework treats fluent output as only one part of learning and warns against substitutive cognitive offloading that removes the work through which judgment develops. The paper also reorders the traditional sequence of instruction.

Rather than always presenting a general rule before a concrete case, abductively initiated inquiry begins with a surprising event, a specific problem or an incomplete situation and asks what might explain it. Abduction generates plausible hypotheses; induction examines patterns and counterexamples; deduction derives consequences and tests whether an explanation holds. Generative AI can make alternatives and conjectures inexpensive to produce, but the authors reject any direct equation of probabilistic continuation with Peircean abduction.

Their stronger claim concerns the human–AI activity: a learner can treat an output as a candidate explanation, compare it with others and verify it through calculation, observation, executable code or proof. General theory remains essential, but it may emerge as a provisional stabilization of inquiry rather than as the instruction that begins it. The universal and the situated become recursively related, with new cases able to reopen a proposed generalization.

Programming remains the framework’s main model because its educational environments tightly couple three semiotic layers. A process can be seen iconically as a turtle’s visible path, expressed symbolically through words and formal grammar, and described indexically as an intention communicated to an AI system. Historically, programming education moved from rule-centered encoding, coding and retrospective debugging toward Papert’s constructionist culture, in which experimentation and debugging became central to understanding rather than merely correcting mistakes after execution.

Natural-language prompting and generative tools now shift more of the work toward contextual articulation of intention, while verification can involve a mixture of dialogue, conventional code, symbolic tools and empirical checks. The authors call this a hybrid-indexical era rather than pure indexical computation because symbolic execution, distributional generation, empirical reference, induction and deduction continue to coexist. Historically, this builds on Logo and Scratch rather than replacing their lessons with an entirely new educational tradition.

A central concept in the proposal is navigational thinking. In problems whose relevant representation is not yet stable, a learner must first discover what counts as the problem and which directions are promising before decomposition, formalization or execution becomes possible. Navigation reads the local structure of that problem space and adjusts course as evidence or unexpected output appears.

The authors stress that this is not a rigid sequence: abduction reframes possible directions, computation tests their consequences, and empirical or inductive evaluation can reopen the search. Interaction with generative systems also encourages what they call artistic navigation, extending Papert’s interest in bricolage. Prompting becomes a form of improvisational composition in which learners iteratively adjust wording, tone and context while reading subtle cues in a system whose response cannot be fully specified in advance.

This resembles the shift from an object that executes instructions to a partner that contributes contextual interpretation, although the learner must retain authority over goals, evidence and decisions. The framework remains deliberately unfinished. The authors identify unresolved questions about whether Papert’s constructionism was inherently indexical, how learning environments should be assessed along the three axes, whether the proposed motivational relationship holds across populations, and when a complete AI system has genuine causal contact with the world.

They also call for developmental research on what children, adolescents, university students and adults may appropriately delegate to AI, how independent reconstruction can be preserved and how process documentation can support feedback without becoming indiscriminate surveillance. Their conceptual conclusion is that schools cannot pretend generative systems are absent, but neither prohibition nor uncritical adoption adequately addresses the changed conditions of learning. If the framework survives testing, its practical aim will be a learner able to use AI for situated exploration while retaining responsibility for evidence, reasoning, independent reconstruction and judgment.

Subject of Research: A conceptual framework for learning with generative AI based on indexicality, constructionism and navigational thinking.

Article Title: The new constructionism. Papert after Peirce: indexicality, navigational thinking, and learning in the age of generative AI

Article References: Levin, I., Semenov, A., & Gorsky, M. (2026). The new constructionism. Papert after Peirce: indexicality, navigational thinking, and learning in the age of generative AI. Frontiers in Education, 11, 1902338. https://doi.org/10.3389/feduc.2026.1902338

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1902338

Keywords: generative AI, constructionism, indexicality, navigational thinking, education, programming pedagogy, abductive reasoning, human–AI learning, epistemic agency, computational thinking, Papert, Peirce