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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 provide people with AI-generated recommendations and explanations 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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This content will become publicly available on March 17, 2027
How Does LLM-powered Coding Assistance Shape Incidental Learning? Exploring Cognitive Forcing Strategies in Programming Education
Many AI-based code assistants, particularly those poweredby Large Language Models (LLMs), provide completesolutions, which can reduce active problem solving andlimit incidental learning, the acquisition of knowledge asa byproduct of task engagement. Such learning requiresactive participation rather than passive acceptance ofAI-generated answers, which might be incorrect. This studyexamines how incidental learning can be supported throughguided interaction. We present LeetCoach, an LLM-assistedcoding platform that applies a cognitive forcing strategy,prompting learners to reflect and take incremental stepsinstead of receiving full solutions. Using LeetCode-stylequestions, we conducted a pilot study with novice andadvanced college programmers who completed tasks underassisted and unassisted conditions. Novices showedsubstantial post-test gains despite receiving AI guidanceonly during the intervention, suggesting that incidentalexposure improved later performance. Advanced learnersshowed smaller gains. Across both groups, participantsrequired fewer debugging attempts in the post-test comparedto earlier stages, indicating improved debugging efficiencyand algorithmic understanding. These findings provide earlyevidence that LLMs can be designed to promote indirectlearning while shaping problem-solving strategies. Thiswork offers a proof of concept for cognitively informedtutoring systems in computer science education anddiscusses implications for integrating LLMs to enhance bothimmediate outcomes and lasting skill development.
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- Award ID(s):
- 2451134
- PAR ID:
- 10693379
- Publisher / Repository:
- https://ojs.aaai.org/index.php/AAAI/article/view/42121
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 40
- Issue:
- 48
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 40880 to 40888
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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