Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
Open accessAbstract
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.
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Previous studies have attributed the cause of overreliance on AI to a lack of cognitive engagement [10,32,55].
“Previous studies have attributed the cause of overreliance on AI to a lack of cognitive engagement [10,32,55].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [32]
The Introduction (p. 1) links overreliance on AI recommendations to potentially superficial processing, and Related Work (p. 2) suggests that interventions may reduce overreliance by encouraging deeper engagement. Both passages frame the link as a possibility, not an established cause; the paper also says its own study did not test overreliance (Discussion, p. 10). (LLM, conf=0.85)
Experimental — accuracy not yet measuredIt is possible that when not thinking aloud, especially in a lowstakes setting, people will be less cognitively engaged with AI explanations [32] and some effects found in the study will not be observed.
“It is possible that when not thinking aloud, especially in a lowstakes setting, people will be less cognitively engaged with AI explanations [32] and some effects found in the study will not be observed.”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [32]
The Discussion (p. 10) says the study involved low-risk decisions and suggests people might engage more deeply with AI in more consequential settings. The Introduction (p. 2) also suggests people may engage less deeply with explanations when given an AI recommendation. The paper does not address whether thinking aloud changes engagement or whether the citing study’s effects would disappear without it. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPeople achieved learning gains from interacting with AI explanations when they critically engaged with AI explanations.
“Similarly, Gajos and Mamykina (2022) found that people achieved learning gains from interacting with AI explanations when they critically engaged with AI explanations.”
From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Gajos & Mamykina, 2022
The Discussion (p. 10) reports incidental learning when participants received AI explanations without recommendations and had to process them to make a decision. But the Introduction (p. 2) says deeper cognitive engagement was hypothesized to explain the learning gain; the paper does not establish that participants critically engaged as an observed condition of learning. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe actual impact of explanations can be influenced by various factors, including task difficulty, decision-maker expertise, decision design, and the cognitive load required to comprehend the explanation, and also the type of explanations and the way it is presented.
“The actual impact of explanations can be influenced by various factors, including task difficulty [44], decision-maker expertise, decision design [45], and the cognitive load required to comprehend the explanation [46] and also the type of explanations and the way it is presented.”
From AI Trust: Can Explainable AI Enhance Warranted Trust? (2023) · cites this paper as [45]
Active cognitive processes involved in evaluating AI explanations are key to effective collaboration between humans and AI.
“The results indicate that active cognitive processes involved in evaluating AI explanations are key to effective collaboration between humans and AI.”
From The role of critical thinking on undergraduates' reliance behaviours on generative <scp>AI</scp> in problem‐solving (2025) · cites this paper as Gajos & Mamykina, 2022
People sometimes follow AI recommendations blindly even when the recommendations are wrong.
“or perhaps even worse, people follow them blindly, even when the recommendations are wrong.”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [6]
Cognitive forcing slightly mitigated overreliance compared to feature-based and uncertainty judgements, particularly disregarding incorrect AI recommendations, but with no statistically significant differences in task performance.
“Their results showed that cognitive forcing slightly mitigated overreliance, compared to feature-based and uncertainty judgements, particularly disregarding incorrect AI recommendations, but with no statistically significant differences in task performance.”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [6]
Recent research has shown that recommendation-driven explainable/interpretable AI has little effect on decision making, although this is not always the case.
“However, recent research has shown that the recommendation-driven explainable/interpretable AI has little effect on decision making [1, 6-8, 28, 32], although this is not always the case [22,23,41].”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [6]
Buçinca et al. and Gajos and Mamykina show that explainability did not mitigate over-reliance, and in some cases increased over-reliance.
“Similarly, in experiments on a general task with general participants, Buçinca et al. [1] and Gajos and Mamykina [6] show that explainability did not mitigate over-reliance, and in some cases it increased over-reliance.”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [6]
Despite cognitive forcing functions being more effective, they were least preferred by participants.
“Interestingly, despite cognitive forcing functions being more effective, they were least preferred by participants.”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [6]
Current cognitive forcing techniques are recommendation-driven.
“Cognitive forcing takes a similar approach, but current cognitive forcing techniques are recommendation-driven [1,6].”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [6]
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Human Discernment of Algorithmic Errors: A Case Study in Child WelfarePreprint
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“In this study, we examined the effectiveness of information-driven support and the role of recommendations through the design and evaluation of an AI-enabled decision-support system within the context of pediatric trauma resuscitation.”
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Human-Centered AI (Also) for Humanistic Management
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Modern AI program Chinese choral arts: cognitive training and motivation of college choristers
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- Authors
- Gajos, K. Z., & Mamykina, L.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- Venue
- 27th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2022-03-22
- Pages
- 794-806
- OpenAlex
- W4220961556
- Cited by
- 127
- References
- 50
- Citations by year
- 2026: 34 · 2025: 47 · 2024: 22 · 2023: 21 · 2022: 3
- FWCI
- 12.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (IIS-2107391)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- incidental learning, AI assistance, AI explanations, AI recommendations, decision-making, cognitive engagement, advice taking, human-AI interaction
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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.
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