Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks | Paper Citation Record

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This paper cites Peeking inside the black-box: A survey on explainable artificial intelligence (xai).

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Peeking inside the black-box: A survey on explainable artificial intelligence (xai)

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Highlights: Summarizing agent behavior to people

Reference 2

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Explainable agents and robots: Results from a systematic literature review

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks The theory of learning by doing

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks How cognitive biases affect xai-assisted decision-making: A systematic review

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Can you trust the black box? the effect of personality traits on trust in ai-enabled user interfaces

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Unresolved cited work

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Complexity measures and decision tree complexity: a survey

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Plan explanations as model reconciliation: Moving beyond explanation as soliloquy

Reference 9

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This paper cites Methods and standards for research on explainable artificial intelligence: lessons from intelligent tutoring systems.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Methods and standards for research on explainable artificial intelligence: lessons from intelligent tutoring systems

Reference 11

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Toward personalized xai: A case study in intelligent tutoring systems

Reference 12

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Thinking, fast and slow

Reference 13

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This paper cites Wearable reasoner: Towards enhanced human rationality through a wearable device with an explainable ai assistant.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Wearable reasoner: Towards enhanced human rationality through a wearable device with an explainable ai assistant

Reference 14

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks An implemented theory of mind to improve human-robot shared plans execution

Reference 15

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions

Reference 16

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Explainable artificial intelligence for education and training

Reference 17

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks On cognitive preferences and the plausibility of rule-based models

Reference 18

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This paper cites Considering the context to build theory in hci, hri, and hmc: Explicating differences in processes of communication and socialization with social technologies.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Considering the context to build theory in hci, hri, and hmc: Explicating differences in processes of communication and socialization with social technologies

Reference 19

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Geiskkovitch, Daniel J

Reference 20

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This paper cites Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller

Reference 21

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review

Reference 22

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Behavior explanation as intention signaling in human-robot teaming

Reference 23

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Counterfactual visual explanations

Reference 24

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks The principles and limits of algorithm-in-the-loop decision making

Reference 25

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Santos, and Rui Prada

Reference 26

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Metrics for Explainable AI: Challenges and Prospects

Reference 27

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Causability and explainability of artificial intelligence in medicine

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks u ller. Measuring the quality of explanations: the system causability scale (scs). KI-K \

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Will algorithms blind people? the effect of explainable ai and decision-makers’ experience on ai-supported decision-making in government

Reference 30

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Representativeness revisited: Attribute substitution in intuitive judgment

Reference 31

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Personalised self-explanation by robots: The role of goals versus beliefs in robot-action explanation for children and adults

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Explainable artificial intelligence in education

Reference 33

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Too much, too little, or just right? ways explanations impact end users' mental models

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks An Evaluation of the Human-Interpretability of Explanation

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Lim, Anind K

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Who should i trust: Ai or myself? leveraging human and ai correctness likelihood to promote appropriate trust in ai-assisted decision-making

Reference 38

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks a mling. Explainable agents for less bias in human-agent decision making. In Davide Calvaresi, Amro Najjar, Michael Winikoff, and Kary Fr \

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks knowing me, knowing you

Reference 40

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Ex(plainable) machina: how social-implicit xai affects complex human-robot teaming tasks

Reference 42

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Natural born explainees: how users’ personality traits shape the human-robot interaction with explainable robots

Reference 43

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks To explain or not to explain: The effects of personal characteristics when explaining music recommendations

Reference 44

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks What's in a user? towards personalising transparency for music recommender interfaces

Reference 45

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Explainable recommendations and calibrated trust: Two systematic user errors

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

Reference 47

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Unresolved cited work

Reference 48

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks How robots influence humans: A survey of nonverbal communication in social human--robot interaction

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Investigating strategies for robot persuasion in social human–robot interaction

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Learning by doing

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making

Reference 53

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Explainable robotics in human-robot interactions

Reference 55

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks The human side of xai: Bridging the gap between ai and non-expert audiences

Reference 56

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks new” and “old

Reference 57

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks How to quantify the degree of explainability: Experiments and practical implications

Reference 58

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Selecting optimal split-functions for large datasets

Reference 59

Resolution: verified exact; doi, observed 2026-08-12T19:56:36.115415Z

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Towards explainable multi-objective probabilistic planning

Reference 60

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Observation fc3b8b74-7c72-42d2-8623-9b2218f4de32 · outbound

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Evaluating the effectiveness of explanations for recommender systems

Reference 61

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Evaluating xai: A comparison of rule-based and example-based explanations

Reference 63

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Decision tree extraction using trained neural network

Reference 64

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks The effects of explanations on automation bias

Reference 65

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions

Reference 66

Resolution: verified exact; doi, observed 2026-08-12T19:56:36.096845Z

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Observation 80e18ba9-b211-49b4-a89a-08f67cfa858c · outbound

This paper cites Explainable embodied agents through social cues: A review.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Explainable embodied agents through social cues: A review

Reference 67

Resolution: verified exact; doi, observed 2026-08-12T19:56:36.084206Z

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Observation 39adaf51-3fc0-4225-a5cf-95e9c025e9f8 · outbound

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Investigating explainable human-robot interaction with augmented reality

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:38.635174Z

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Observation b2209e35-5eb4-4a0f-a5e5-b94d15217a12 · outbound

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Unresolved cited work

Reference 69

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Scout: Self-aware discriminant counterfactual explanations

Reference 70

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T19:56:36.010961Z

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Effects of explanations in ai-assisted decision making: Principles and comparisons

Reference 72

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Observation a69a43aa-383a-4f8a-becc-f1611f60cd79 · outbound

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Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Wason and J.ST.B.T

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T19:56:36.023404Z

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This paper cites Towards human-centred explainable ai: A systematic literature review.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Towards human-centred explainable ai: A systematic literature review

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:38.621970Z

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Observation 9c4fffd4-dbd8-44b3-8107-430477817bfa · outbound

This paper cites Learning decision trees with reinforcement learning.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks Learning decision trees with reinforcement learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:38.609971Z

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Observation 235bfe6e-836f-4a84-b55c-8747e9f57cd2 · outbound

This paper cites write newline.

Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks write newline

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T19:56:36.035526Z

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