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Cited by 62Human-Automation Interaction and SafetyUsability and User Interface DesignSocial Robot Interaction and HRI

AI Knowledge: Improving AI Delegation through Human Enablement

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Pinski, Marc, Adam, Martin, Benlian, Alexander·2023·Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3544548.3580794
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Abstract

When collaborating with artificial intelligence (AI), humans can often delegate tasks to leverage complementary AI competencies. However, humans often delegate inefficiently. Enabling humans with knowledge about AI can potentially improve inefficient AI delegation. We conducted a between-subjects experiment (two groups, n = 111) to examine how enabling humans with AI knowledge can improve AI delegation in human-AI collaboration. We find that AI knowledge-enabled humans align their delegation decisions more closely with their assessment of how suitable a task is for humans or AI (i.e., task appraisal). We show that delegation decisions closely aligned with task appraisal increase task performance. However, we also find that AI knowledge lowers future intentions to use AI, suggesting that AI knowledge is not strictly positive for human-AI collaboration. Our study contributes to HCI design guidelines with a new perspective on AI features, educating humans regarding general AI functioning and their own (human) performance and biases.

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  • Higher AI literacy may lead to a more critical evaluation of AI tools but could also introduce scepticism that could deter future reliance on AI.

    “Higher AI literacy may lead to a more critical evaluation of AI tools but could also introduce scepticism that could deter future reliance on AI (Pinski et al., 2023).”

    From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Pinski et al., 2023

    The Introduction and Section 3.2 describe AI knowledge as helping people align delegation decisions with their assessment of whether AI is suited to a task. Section 6.1 reports that AI knowledge can reduce intention to continue using AI. The paper does not establish scepticism as the reason for that reduction; Section 6.1 instead offers information overload as a possible explanation. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AI literacy not only boosts task performance, it also reduces individuals' intention to rely on AI in the future.

    “They also found that AI literacy not only boosts task performance, it also simultaneously reduces individuals' intention to rely on AI in the future.”

    From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Pinski et al., 2023

    Section 6.1 reports that AI knowledge can improve human–AI collaboration performance through better delegation and can reduce intention to continue using AI. But the Introduction and section 2.3 specify that the study tested AI knowledge, one element of broader AI literacy, rather than AI literacy as a whole. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Trust and AI literacy are intertwined and exert influence on reliance simultaneously.

    “Thus, empirical studies show that trust and AI literacy are intertwined and exert influence on reliance simultaneously.”

    From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Pinski et al., 2023

    Section 2.3 identifies AI knowledge as an element of AI literacy, and Sections 5.1 and 6 report that AI knowledge affects delegation behavior. The paper does not establish that trust and AI literacy are intertwined or that both simultaneously influence reliance; its reported findings concern knowledge, task appraisal, and delegation. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Reliance behaviours in human-AI collaboration are influenced by factors such as trust in AI and AI literacy.

    “Reliance behaviours in human-­ AI collaboration are influenced by factors such as trust in AI and AI literacy.”

    From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Pinski et al., 2023

  • Increased AI literacy might reduce the intention to use AI tools but could improve overall human-AI collaboration performance.

    “Prior research suggests that increased AI literacy might reduce the intention to use AI tools but could improve the overall human-AI collaboration performance (Pinski et al., 2023).”

    From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Pinski et al., 2023

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Authors
Pinski, M., Adam, M., & Benlian, A.
Affiliations
Technische Universität Darmstadt (DE)
ORCID
Venue
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2023-04-19
Pages
1-17
DOI
10.1145/3544548.3580794
OpenAlex
W4366549077
Cited by
62
References
11
Citations by year
2026: 21 · 2025: 25 · 2024: 16
FWCI
30.98
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
  • TUbilio (Technical University of Darmstadt) (submittedVersion)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
AI knowledge, AI delegation, human-AI collaboration, task performance

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 ›

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