Co-Creative Continuity in Long-Term Naturalistic Dialogue

In human–AI co-creation, work may continue despite errors or interpretive disagreements when the AI incorporates a human-initiated repair and returns to the same joint task. The presence of a single error or repair therefore cannot, by itself, be treated as a breakdown of co-creation. This study is an exploratory, descriptive qualitative observational study of short, multi-turn episodes purposively extracted from long-term naturalistic dialogue between a single user and ChatGPT. Taking the preceding study (Hinata, 2026), which described cases in which co-creative interaction was maintained, as a background reference, the present study compared cases in the following sequence: establishment of a shared task state, misalignment, the first explicit repair initiation, actual uptake in the immediately subsequent AI response, and reconnection to the joint task. The primary direct comparison involved three episodes: Recovery Contrast A and Primary Cases A and B. In the recovery contrast, the interaction reconnected to the original joint task immediately after the first repair initiation, and the work continued. In the two primary cases, by contrast, the AI responded to the repair initiation and apologized or acknowledged a misunderstanding, yet did not immediately return to the current task or option mapping that had been shared up to that point. Additional correction or a restatement of the shared task state by the user was required. An AI apology, statement of understanding, or acknowledgment of a misunderstanding was not treated in itself as evidence that repair had been completed or that the interaction had returned to the task. These judgments were based on the actual object, mapping, procedural step, and subsequent action. Following the additional intervention, return to the joint task was confirmed in Primary Case B. In Primary Case A, reorientation to the current task was confirmed, but because a separate problem intervened, resumption of task execution for the analysis of the work could not be determined. These data suggest that co-creative continuity can be described not in terms of whether an error occurred, but in terms of observable outcomes: whether the interaction reconnected to the same joint task after the first repair initiation and how the subsequent interactional sequence unfolded. This study does not propose a new cause of breakdown or a new theory of repair. Rather, it aims to distinguish the outcome immediately following the first repair initiation, the impact on joint work, and the threshold at which the user subjectively perceives a breakdown, thereby delineating a local boundary in cases of naturalistic use.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-14
DOI
https://doi.org/10.5281/zenodo.22754940
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
preprint
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Co-Creative Continuity in Long-Term Naturalistic Dialogue

Lucy Hinata
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
preprint

Co-Creative Continuity in Long-Term Naturalistic Dialogue

Lucy Hinata
preprint en

Abstract

In human–AI co-creation, work may continue despite errors or interpretive disagreements when the AI incorporates a human-initiated repair and returns to the same joint task. The presence of a single error or repair therefore cannot, by itself, be treated as a breakdown of co-creation. This study is an exploratory, descriptive qualitative observational study of short, multi-turn episodes purposively extracted from long-term naturalistic dialogue between a single user and ChatGPT. Taking the preceding study (Hinata, 2026), which described cases in which co-creative interaction was maintained, as a background reference, the present study compared cases in the following sequence: establishment of a shared task state, misalignment, the first explicit repair initiation, actual uptake in the immediately subsequent AI response, and reconnection to the joint task. The primary direct comparison involved three episodes: Recovery Contrast A and Primary Cases A and B. In the recovery contrast, the interaction reconnected to the original joint task immediately after the first repair initiation, and the work continued. In the two primary cases, by contrast, the AI responded to the repair initiation and apologized or acknowledged a misunderstanding, yet did not immediately return to the current task or option mapping that had been shared up to that point. Additional correction or a restatement of the shared task state by the user was required. An AI apology, statement of understanding, or acknowledgment of a misunderstanding was not treated in itself as evidence that repair had been completed or that the interaction had returned to the task. These judgments were based on the actual object, mapping, procedural step, and subsequent action. Following the additional intervention, return to the joint task was confirmed in Primary Case B. In Primary Case A, reorientation to the current task was confirmed, but because a separate problem intervened, resumption of task execution for the analysis of the work could not be determined. These data suggest that co-creative continuity can be described not in terms of whether an error occurred, but in terms of observable outcomes: whether the interaction reconnected to the same joint task after the first repair initiation and how the subsequent interactional sequence unfolded. This study does not propose a new cause of breakdown or a new theory of repair. Rather, it aims to distinguish the outcome immediately following the first repair initiation, the impact on joint work, and the threshold at which the user subjectively perceives a breakdown, thereby delineating a local boundary in cases of naturalistic use.

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
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