Actionable companion · Feedback & scaffolding

Learning with agency: The SocialAI school: a framework leveraging developmental psychology toward artificial socio-cultural agents

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Developmental psychologists have long-established socio-cognitive abilities as fundamental to human intelligence and development. These abilities enable individuals to enter, learn from, and contribute to a surrounding culture. This drives the process of cumulative cultural evolution, which is responsible for humanity's most remarkable achievements. AI research on social interactive agents mostly concerns the emergence of culture in a multi-agent setting (often without a strong grounding in developmental psychology). We argue that AI research should be informed by psychology and study socio-cognitive abilities enabling to enter a culture as well. We draw inspiration from the work of Michael Tomasello and Jerome Bruner, who studied socio-cognitive development and emphasized the influence of a cultural environment on intelligence. We outline a broader set of concepts than those currently studied in AI to provide a foundation for research in artificial social intelligence. Those concepts include social cognition (joint attention, perspective taking), communication, social learning, formats, and scaffolding. To facilitate research in this domain, we present The SocialAI school—a tool that offers a customizable parameterized suite of procedurally generated environments. This tool simplifies experimentation with the introduced concepts. Additionally, these environments can be used both with multimodal RL agents, or with pure-text Large Language Models (LLMs) as interactive agents. Through a series of case studies, we demonstrate the versatility of the SocialAI school for studying both RL and LLM-based agents. Our motivation is to engage the AI community around social intelligence informed by developmental psychology, and to provide a user-friendly resource and tool for initial investigations in this direction. Refer to the project website for code and additional resources: https://sites.google.com/view/socialai-school.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

In this section, we discuss the development of the ability to coordinate perspectives and view things from the objective perspective (a perspective independent from any individual) (Tomasello, 2019). The starting point is the ability to infer what another sees or knows. The earliest example of this is gaze following of six-month-olds (D'Entremont et al., 1997). Here, only one perspective is processed at the time. Joint attention (JA) emerges at around 9 months of age. Tomasello (2019) defines JA as consisting of two elements: triangulation (two participants attending to the same referent) and recursiveness (both participants being recursively aware that they are both sharing attention). JA is characterized by the dual-level structure of shared attention (on one level) and individual perspectives (on another level). Consequently, children start to align and exchange perspectives. Once children reach a sufficient level of linguistic competence, they start sharing attention to mental content in the form of linguistic discourse (at two to three years of age). The presence of conflicting perspectives in linguistic discourse (e.g. a disagreement about where some object is located) pushes children to resolve those conflicts, which they do by forming the “objective” perspective, and coordinating other perspectives with it.

Limitations and uncertainty · source passage

In this work, we outline and discuss several concepts from developmental psychology (mostly regarding the development before and around 9 months of age), which we believe to be most relevant for AI at the moment. Due to the magnitude of this field of research, many relevant cognitive concepts are either discussed very briefly (e.g. conformity, social norms, instructed learning) or left unmentioned (e.g. morality, fairness, sense of self). Furthermore, while we argue that the work of Tomasello and Bruner provides an interesting framework to guide AI research in social skill acquisition, many other perspectives could have been considered as well, e.g. Erikson (1993), Gopnik and Meltzoff (1997), or Heyes (2019).

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Grgur Kovač; Rémy Portelas; Peter Ford Dominey; Pierre-Yves Oudeyer. The SocialAI school: a framework leveraging developmental psychology toward artificial socio-cultural agents. Frontiers in Neurorobotics (2024-10-09). https://doi.org/10.3389/fnbot.2024.1396359

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.