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

arXiv: https://arxiv.org/abs/2202.05402 · HTML: https://mlsanity.com/paper/2202.05402/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection](https://mlsanity.com/paper/1811.07901/) (2018)
- [Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems](https://mlsanity.com/paper/2001.08298/) (2020)
- ["Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans](https://mlsanity.com/paper/2001.05871/) (2020)
- [Contrastive Explanation: A Structural-Model Approach](https://mlsanity.com/paper/1811.03163/) (2018)
- [The Flaws of Policies Requiring Human Oversight of Government Algorithms](https://mlsanity.com/paper/2109.05067/) (2021)
- [Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens](https://mlsanity.com/paper/2102.00593/) (2021)
- [Contrastive Explanations with Local Foil Trees](https://mlsanity.com/paper/1806.07470/) (2018)
- [Leveraging Rationales to Improve Human Task Performance](https://mlsanity.com/paper/2002.04202/) (2020)
- [The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies](https://mlsanity.com/paper/2109.01443/) (2021)
- [Weight of Evidence as a Basis for Human-Oriented Explanations](https://mlsanity.com/paper/1910.13503/) (2019)

## Built on by (most cited first, 30 of 53)

- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making](https://mlsanity.com/paper/2305.07722/) (2023)
- [Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies](https://mlsanity.com/paper/2502.08554/) (2025)
- [Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making](https://mlsanity.com/paper/2403.16812/) (2024)
- [Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging](https://mlsanity.com/paper/2205.09696/) (2022)
- [Selective Explanations: Leveraging Human Input to Align Explainable AI](https://mlsanity.com/paper/2301.09656/) (2023)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning](https://mlsanity.com/paper/2410.08922/) (2024)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [Generative AI for Education (GAIED): Advances, Opportunities, and Challenges](https://mlsanity.com/paper/2402.01580/) (2024)
- [Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning](https://mlsanity.com/paper/2403.05911/) (2024)
- [One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations](https://mlsanity.com/paper/2405.05581/) (2024)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making](https://mlsanity.com/paper/2401.05840/) (2024)
- [Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge](https://mlsanity.com/paper/2308.15700/) (2023)
- [Evaluation of Geographical Distortions in Language Models](https://mlsanity.com/paper/2404.17401/) (2024)
- [Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook](https://mlsanity.com/paper/2303.06223/) (2023)
- [A Survey of AI Reliance](https://mlsanity.com/paper/2408.03948/) (2024)
- [Political Ideology Shifts in Large Language Models](https://mlsanity.com/paper/2508.16013/) (2025)
- [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026)
- [The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features](https://mlsanity.com/paper/2310.08617/) (2023)
- [From Evidence to Decision: Exploring Evaluative AI](https://mlsanity.com/paper/2402.01292/) (2024)
- [Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making Process](https://mlsanity.com/paper/2406.08959/) (2024)
- [Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation](https://mlsanity.com/paper/2408.00024/) (2024)
- [Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals](https://mlsanity.com/paper/2311.14720/) (2023)

## 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.