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Out of sync: Accommodation, evaluation, and emergence in five weeks with an agentic AI teammate — arXiv:2610.41057

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41057
License (as archived): CC0-1.0
Archived: 2026-11-03T07:48:31.323Z
SHA-256 of the archived text: b9445256389fa52c2cb89e83cc0f89fcab2710bd988a83b51bebd985c082a118
Stable URL of this page: https://metraipolis.com/documento/dc_bbc55045fcbb

How to cite

Out of sync: Accommodation, evaluation, and emergence in five weeks with an agentic AI teammate — arXiv:2610.41057 — arXiv (en), https://arxiv.org/abs/2610.41057 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bbc55045fcbb (Metraipolis archive dc_bbc55045fcbb, sha256 b9445256389fa52c2cb89e83cc0f89fcab2710bd988a83b51bebd985c082a118)

BibTeX

@misc{dcbbc55045fcbb,
  title = {Out of sync: Accommodation, evaluation, and emergence in five weeks with an agentic AI teammate — arXiv:2610.41057},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41057},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bbc55045fcbb; sha256 b9445256389fa52c2cb89e83cc0f89fcab2710bd988a83b51bebd985c082a118},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41057. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As agentic AI in education shifts from reactive tools toward partners that take initiative and persist over time, a central question is how learners relate to, evaluate, and benefit from artificial agency. We conducted a longitudinal experiment in an undergraduate learning analytics course, in which 17 student teams (N = 48) collaborated with a persistent LLM-based teammate across weekly sessions. The agent decided when to intervene through a trigger-based architecture, and its social role was manipulated between teams: a Cognitive Agent that challenged quick consensus or a Supportive Agent that invited quieter members to contribute. Combining chat discourse, post-session surveys, and spoken reflections, we identified distinct patterns unfolding across three timescales. Students accommodated their language to the agent's role immediately, using more social language toward the Supportive Agent from the first session. Their helpfulness evaluations were recalibrated from session to session in relation to the agent's observed behavior, rather than remaining anchored to students' pre-existing attitudes toward AI. A slower crossover emerged in perceived collaboration process and positive-affect word use in reflections: Supportive-Agent teams began below Cognitive-Agent teams and ended above them, although students rated the Supportive Agent less helpful throughout. Because these responses follow different trajectories, single-session evaluations of agentic AI may miss changes that emerge only through repeated interaction.

Tema degli esploratori: governance digitale e beni comuni (elenco arXiv cs.CY, nuovi lavori del giorno).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2610.41057 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:40:52.181Z dalla pagina https://arxiv.org/list/cs.CY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).