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Adaptive Human-AI Collaboration: A Review of Multimodal Context Modeling, Uncertainty-Aware Intervention, and Longitudinal Co-Adaptation — arXiv:2609.13180

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13180
License (as archived): CC0-1.0
Archived: 2026-10-15T23:54:33.713Z
SHA-256 of the archived text: cbc038350bdbaff98f8ef343cbf1d8a3f5467972eaf30ab38793009d33bc3333
Stable URL of this page: https://metraipolis.com/documento/dc_4ffdfcd30fb7

How to cite

Adaptive Human-AI Collaboration: A Review of Multimodal Context Modeling, Uncertainty-Aware Intervention, and Longitudinal Co-Adaptation — arXiv:2609.13180 — arXiv (en), https://arxiv.org/abs/2609.13180 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4ffdfcd30fb7 (Metraipolis archive dc_4ffdfcd30fb7, sha256 cbc038350bdbaff98f8ef343cbf1d8a3f5467972eaf30ab38793009d33bc3333)

BibTeX

@misc{dc4ffdfcd30fb7,
  title = {Adaptive Human-AI Collaboration: A Review of Multimodal Context Modeling, Uncertainty-Aware Intervention, and Longitudinal Co-Adaptation — arXiv:2609.13180},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13180},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4ffdfcd30fb7; sha256 cbc038350bdbaff98f8ef343cbf1d8a3f5467972eaf30ab38793009d33bc3333},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13180. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Artificial intelligence is shifting from a static decision-support tool to an adaptive collaborator that must sense context, decide when and how to intervene, and improve through repeated interaction with humans individually and in groups. Yet meta-analytic evidence shows that human-AI combinations often fail to outperform the best of either partner alone, and the enabling literature remains fragmented across multimodal sensing, uncertainty quantification, reliance and delegation, facilitation, and teaming. This paper reports a review of adaptive human-AI collaboration literature through a closed-loop lens. Following iterative identification, staged selection against explicit criteria, structured extraction, taxonomy-driven synthesis, and snowballing, we analyze 50 reviewed works. We contribute i) a taxonomy of multimodal context modeling, from individual states to collective states such as group engagement and participation equality; ii) a taxonomy of uncertainty-aware intervention, covering uncertainty sources, estimation and calibration mechanisms, an intervention repertoire that ranges from explanation modulation and deferral to group facilitation, and intervention policies; and iii) a taxonomy of longitudinal co-adaptation and synergy-oriented evaluation. We integrate the three taxonomies into MCAL, a dual-timescale Multimodal Co-Adaptation Loop reference model, and instantiate it on a mixed human-robot workspace, walking every stage of the loop through one concrete setting to show what each taxonomy cell holds in practice.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2609.13180 — 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 2026-10-15T23:24:31.329Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).