Cited by 50Intelligent Tutoring Systems and Adaptive LearningSocial Robot Interaction and HRIHuman-Automation Interaction and Safety

Six challenges for human-AI Co-learning

van den Bosch, Karel, Schoonderwoerd, Tjeerd, Blankendaal, Romy, Neerincx, Mark·2019·Lecture notes in computer science

Integrity signals

Registry

Crossref · 10.1007/978-3-030-22341-0_45

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Demanding explainability, observability, or transparency may cause AI systems to constrain their potential benefit for human society to what can be understood by humans.

    “In addition, according to Werkhoven et al. (2018) demanding explainability observability, or transparency (Belkom, 2019; van den Bosch et al., 2019) may cause artificial intelligent systems to constrain their potential benefit for human society, to what can be understood by humans.”

    From Human- versus Artificial Intelligence (2021) · cites this paper as van den Bosch et al., 2019

  • Human-aware AI partners that operate like human collaborators must be able to sense, understand, and react to complex human behavioral qualities such as attention, motivation, emotion, creativity, planning, or argumentation.

    “When human-aware AI partners operate like “human collaborators” they must be able to sense, understand, and react to a wide range of complex human behavioral qualities, like attention, motivation, emotion, creativity, planning, or argumentation, (e.g. Krämer et al., 2012; van den Bosch and Bronkhorst, 2018; van den Bosch et al., 2019).”

    From Human- versus Artificial Intelligence (2021) · cites this paper as van den Bosch et al., 2019

Cites

Flagged first, then by citations

Cited by

Sorted by citations

  • Human- versus Artificial Intelligence

    Korteling, J. E. (., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.622364 · Cited by 619 · see citing works

    “sense, understand, and react to a wide range of complex human behavioral qualities, like attention, motivation, emotion, creativity, planning, or argumentation, (e.g. Krämer et al., 2012; van den Bosch and Bronkhorst, 2018; van den Bosch et al., 2019).”
    BackgroundSelf-citation

Authors
van den Bosch, K., Schoonderwoerd, T., Blankendaal, R., & Neerincx, M.
Affiliations
Netherlands Organisation for Applied Scientific Research (NL)
Venue
Lecture notes in computer science
Published
2019
Pages
572-589
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2961759237
Cited by
50
References
60
Citations by year
2026: 9 · 2025: 9 · 2024: 17 · 2023: 7 · 2022: 3 · 2021: 5
FWCI
0.77
Type
conference paper
Language
en
Open access
closed
Locations
  • Lecture notes in computer science (publishedVersion)
  • TNO Repository (submittedVersion)
  • Repository hosted by TU Delft Library (TU Delft) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Keywords
hybrid teams, human-machine collaboration, artificial agents, mental models, self-awareness, theory of mind, adaptive collaboration, personalized learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review