mlsanityData to 7 Oct 2026

Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning

arXiv 2202.05402 ↗First posted 11 Feb 2022cs.HC cs.AI cs.LGcited by 53

Not on the map. Why?

Abstract (arXiv)

When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the information they receive and process this information thoughtfully. How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning? To answer these questions, we conducted three experiments in which individuals were asked to make nutritional decisions and received simulated AI recommendations and explanations. In the first experiment, we found that when people were presented with both a recommendation and an explanation before making their choice, they made better decisions than they did when they received no such help, but they did not learn. In the second experiment, participants first made their own choice, and only then saw a recommendation and an explanation from AI; this condition also resulted in improved decisions, but no learning. However, in our third experiment, participants were presented with just an AI explanation but no recommendation and had to arrive at their own decision. This condition led to both more accurate decisions and learning gains. We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions. This work provides some of the most direct evidence to date that it may not be sufficient to include explanations together with AI-generated recommendation to ensure that people engage carefully with the AI-provided information. This work also presents one technique that enables incidental learning and, by implication, can help people process AI recommendations and explanations more carefully.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). Builds on 16 of its 70 references that are in the map. First posted on arXiv 11 Feb 2022.

Builds on

16 of 70 references.

References · 16

  1. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  2. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  3. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  4. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  5. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  6. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  7. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  8. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 138
  9. 2020"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humanscited by 86
  10. 2018Contrastive Explanation: A Structural-Model Approachcited by 64
  11. 2021The Flaws of Policies Requiring Human Oversight of Government Algorithmscited by 44
  12. 2021Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lenscited by 35
4 more
  1. 2018Contrastive Explanations with Local Foil Treescited by 34
  2. 2020Leveraging Rationales to Improve Human Task Performancecited by 20
  3. 2021The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studiescited by 13
  4. 2019Weight of Evidence as a Basis for Human-Oriented Explanationscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 10; 2024: 16; 2025: 9; 2026: 16.

53 papers.

Sort:

By area:

  1. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  2. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  3. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  4. 2023Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision supportcited by 65
  5. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
  6. 2025Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistenciescited by 39
  7. 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38
  8. 2022Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imagingcited by 32
  9. 2023Selective Explanations: Leveraging Human Input to Align Explainable AIcited by 29
  10. 2023Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Makingcited by 25
  11. 2024Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learningcited by 16
  12. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
38 more
  1. 2024Generative AI for Education (GAIED): Advances, Opportunities, and Challengescited by 13
  2. 2024Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learningcited by 13
  3. 2024One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generationscited by 13
  4. 2025AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Supportcited by 12
  5. 2024Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skillscited by 11
  6. 2024DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Explorationcited by 10
  7. 2024Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Makingcited by 9
  8. 2023Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledgecited by 6
  9. 2024Evaluation of Geographical Distortions in Language Modelscited by 6
  10. 2023Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbookcited by 5
  11. 2025Political Ideology Shifts in Large Language Modelscited by 5
  12. 2024A Survey of AI Reliancecited by 4
  13. 2023The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Featurescited by 3
  14. 2024From Evidence to Decision: Exploring Evaluative AIcited by 3
  15. 2024Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Processcited by 3
  16. 2024Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformationcited by 3
  17. 2026AI Agents Push Humans Out of the Loopcited by 3
  18. 2023Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journalscited by 1
  19. 2024Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluationcited by 1
  20. 2025Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisionscited by 1
  21. 2025Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Makingcited by 1
  22. 2026AI Literacy, Safety Awareness, and STEM Career Aspirations of Australian Secondary Students: Evaluating the Impact of Workshop Interventionscited by 1
  23. 2026The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoningcited by 1
  24. 2026HANSEL: Extracting Breadcrumbs from Web Agent Trajectories for Interactive Verificationcited by 1
  25. 2026Towards an Argumentative Foundation for Evaluative AIcited by 1
  26. 2024An Empirical Examination of the Evaluative AI Frameworkcited by 0
  27. 2025Guidance Source Matters: How Guidance from AI, Expert, or a Group of Analysts Impacts Visual Data Preparation and Analysiscited by 0
  28. 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisionscited by 0
  29. 2025Six Guidelines for Trustworthy, Ethical and Responsible Automation Designcited by 0
  30. 2025Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AIcited by 0
  31. 2026Human-AI Collaboration in Large Language Model-Integrated Building Energy Management Systems: The Role of User Domain Knowledge and AI Literacycited by 0
  32. 2026The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflowscited by 0
  33. 2026Humanwashing -- It Should Leave You Feeling Dirtycited by 0
  34. 2026Understanding How International Students in the U.S. Are Using Conversational AI to Support Cross-Cultural Adaptationcited by 0
  35. 2026Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Developmentcited by 0
  36. 2026AI Assistants Overassistcited by 0
  37. 2026Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocolcited by 0
  38. 2026AI Watchdog: Agent Interfaces for Detecting and Defending Against Manipulative Dark Patterns in AI Conversationscited by 0

50 most cited of 53 All 53 (JSON)