PREreview de Organizing Intelligence Over Time: Human–AI Collaboration as Joint Cognitive Development

This preprint proposes a conceptual framework for Human–AI collaboration as joint cognitive development. The framework draws on three main research traditions: human cognition, including issues of bounded rationality and metareasoning; Human–Automation and HCI studies of function allocation and modes of supervisory control; and modern AI systems, including agent memory and continual learning.

The way cognitive work is distributed between humans and AI in the current task has effects that extend to the distribution of practice, feedback, supervision, and experience. The way these experiences affect human skills and beliefs, as well as the memory and policies of the AI, and how the two systems coordinate their actions in the future, forms a developmental feedback loop.

Six research questions concerning joint learning, the validity of stored experience, supervision, and the longitudinal evaluation of Human–AI systems are proposed by the paper.

This is mainly a conceptual and integrative contribution. The paper brings together a number of different research areas and highlights an important issue for the long-term development of Human–AI systems: how the organization of cognitive work in present tasks can shape future Human–AI capabilities.

The paper provides a useful integration of hitherto rather separate streams of research and therefore highlights an important issue for the long-term development of Human–AI systems, i.e., the organization of cognitive work in present tasks. However, the developmental loop presented is still entirely theoretical and has not yet been empirically tested.

Major issues

• The paper is presented as a review, but actually it is closer to a position paper written from the perspective of a conceptual framework and research agenda. That is, a “joint cognitive development” framework is proposed and outlined in some detail, but no attempt is made to empirically test or study the proposed developmental loop of cognitive work.

• The argument relies substantially on non-peer-reviewed sources, including arXiv preprints and engineering reports or blog posts from organizations such as Anthropic, OpenAI, and DeepMind. These sources may contain useful information on current practices with AI systems, but the paper should be particularly cautious when citing reports by industries or other organizations for claims about learning, adaptation, or developmental mechanisms, as these claims typically require stronger scholarly evidence.

• The major causal claim of the paper is not adequately supported by the evidence presented. It is generally not made clear in the paper whether evidence is provided to support individual components of the framework for the organization of cognitive work, or whether evidence is provided to support the complete developmental loop of the proposed causal relationship. It would be helpful to distinguish more clearly between evidence that supports the individual components of the framework and evidence that supports the causal relationships between them.

• The manuscript combines old and new material, at times assigning similar weight to a classical reference and to a very recent preprint or other emerging work. Explicit treatment of the associated evidence and certainty would improve the scholarly rigor of this synthesis. The evidence and certainty of the sources used for the synthesis could be explained more clearly.

• Six very interesting research questions are proposed to test the framework in more detail. Some of the research questions are very ambitious and require a very complex experimental setup to test the framework. The paper would benefit from considering whether initial or pilot studies could be used to evaluate the feasibility of these research questions before attempting more complex experimental designs.

Minor issues

1. Many of the sections, including the introduction to the literature and connections to the modern AI concept, follow a similar structure. Therefore, there are several opportunities to differentiate the sections in order to improve readability and avoid excessive repetition.

2. The conceptual Figure 1 is relatively abstract and does not provide a lot of detail. A concrete worked example (e.g., the trace of a Human–AI interaction) for readers to understand the proposed developmental loop better would be helpful.

3. There is no clearly separated section for the limitations of the manuscript under review. Although limitations are acknowledged in various parts of the manuscript, compiling a list of the main limitations in a separate section would help to delimit the scope of the proposed framework.

4. The distinction between the two concepts, Human–AI co-evolution and joint cognitive development, could be made more clearly from other related concepts (co-adaptation, mutual adaptation, human-in-the-loop learning), and it would be good to clarify what is new about the proposed framework.

5. Some of the introductory sentences are long and comprise several conceptual statements. These could be reworked into shorter sentences without changing the content.

6. The statement on the use of AI-assisted technologies would benefit from more detail on the extent to which the author actually relied on AI to develop key arguments in the manuscript, as opposed to using search, organization, and language support functions. This comment is especially relevant given the manuscript’s discussion of AI-assisted knowledge production and judgment.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.