The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
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].”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 AI in problem‐solving (2025) · cites this paper as Gajos & Mamykina, 2022
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
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“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 AI in problem‐solving (2025) · cites this paper as Gajos & Mamykina, 2022
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Metadata
- 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 › Human Factors and Ergonomics › 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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