mlsanityData to 14 Oct 2026

Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer

arXiv 2207.00497 ↗First posted 1 Jul 2022cs.HC cs.AI cs.LGcited by 6

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Abstract (arXiv)

Across a multitude of work environments, expert knowledge is imperative for humans to conduct tasks with high performance and ensure business success. These humans possess task-specific expert knowledge (TSEK) and hence, represent subject matter experts (SMEs). However, not only demographic changes but also personnel downsizing strategies lead and will continue to lead to departures of SMEs within organizations, which constitutes the challenge of how to retain that expert knowledge and train novices to keep the competitive advantage elicited by that expert knowledge. SMEs training novices is time- and cost-intensive, which intensifies the need for alternatives. Human-AI collaboration (HAIC) poses a way out of this dilemma, facilitating alternatives to preserve expert knowledge and teach it to novices for tasks conducted by SMEs beforehand. In this workshop paper, we (1) propose a framework on how HAIC can be utilized to train novices on particular tasks, (2) illustrate the role of explicit and tacit knowledge in this training process via HAIC, and (3) outline a preliminary experiment design to assess the ability of AI systems in HAIC to act as a trainer to transfer TSEK to novices who do not possess prior TSEK.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 9 of its 39 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

9 of 39 references.

References · 9

  1. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  2. 2019Counterfactual Visual Explanationscited by 313
  3. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  4. 2021Hybrid Intelligencecited by 55
  5. 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
  6. 2022On the Effect of Information Asymmetry in Human-AI Teamscited by 17
  7. 2020Mapping the Landscape of Artificial Intelligence Applications against COVID-19cited by 14
  8. 2020Do you comply with AI? -- Personalized explanations of learning algorithms and their impact on employees' compliance behaviorcited by 11
  9. 2013Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approachcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 0; 2026: 1.

6 papers.