Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
- URL: http://arxiv.org/abs/2606.11669v1
- Date: Wed, 10 Jun 2026 05:28:32 GMT
- Title: Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
- Abstract summary: Generative AI (GenAI) tools offer opportunities for augmenting human cognitive tasks.<n>Among these tasks, information seeking is rapidly reshaped by GenAI tools, with potentially profound implications for learning and knowledge acquisition.<n>We conducted a field experiment in which participants sought information through either ChatGPT or Google Search over a span of 8 days.<n>Our findings show that participants in the ChatGPT group experienced diminished agency in their information-seeking processes, as they offloaded much of the information selection to AI.
- Score: 33.12893683348563
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative AI (GenAI) tools offer increasing opportunities for augmenting human cognitive tasks. Among these tasks, information seeking is being rapidly reshaped by GenAI tools, with potentially profound implications for learning and knowledge acquisition. To investigate these implications, we conducted a between-subjects field experiment in which participants pursued informal learning by seeking information through either ChatGPT or Google Search over a span of 8 days. Using a daily diary protocol, we gathered in-situ data on their information-seeking processes. Our findings show that participants in the ChatGPT group experienced diminished agency in their information-seeking processes, as they offloaded much of the information selection to AI, and consequently experienced greater meta-cognitive load arising from this reduced sense of control. We further highlight two sources of distortion in information access when using ChatGPT: biases in ChatGPT outputs, particularly towards providing solution-oriented artifacts over principled knowledge; and systematic shifts in users' information-seeking behaviors, whereby the conversational and socially-oriented interaction paradigm of current GenAI tools may inadvertently reduce exploration of the broader knowledge space. As a result, on average, participants in the ChatGPT group had worse learning outcomes than those using Google, especially for higher-order critical learning. Our work suggests inherent tensions between offloading information seeking to AI and meaningful learning, and provides broader implications for understanding AI's risks to human cognition.
Related papers
- MetaCues: Enabling Critical Engagement with Generative AI for Information Seeking and Sensemaking [7.495555759291]
We developed a novel GenAI-based interactive tool for information seeking that delivers metacognitive cues alongside AI responses and a note-taking interface to guide users' search and associated learning.<n>Preliminary findings regarding participants' search behavior show that MetaCues leads to increased confidence in attitudinal judgments about the search topic as well as broader inquiry.
arXiv Detail & Related papers (2026-03-20T04:34:19Z) - Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems [47.03825808787752]
This paper transitions from literature review to practical countermeasures.<n>We report on improved AI-generated content through Large Language Models (LLMs) and multimodal systems.<n>We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI.
arXiv Detail & Related papers (2026-01-29T16:42:22Z) - Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning [1.489200532210589]
This research paper investigates the impact of generative AI (GenAI) on the learner experience.<n>In e-learning environments, learners often need to navigate a complex information space on their own.
arXiv Detail & Related papers (2025-07-29T04:16:21Z) - Information Retrieval in the Age of Generative AI: The RGB Model [77.96475639967431]
This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.<n>We propose a model to characterize the generation, indexing, and dissemination of information in response to new topics.<n>Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation.
arXiv Detail & Related papers (2025-04-29T10:21:40Z) - Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval [0.0]
This study introduces a novel approach to bias-aware knowledge retrieval by leveraging agentic framework and the innovative use of bias detectors.<n>By empowering users with transparency and awareness, this approach aims to foster more equitable information systems.
arXiv Detail & Related papers (2025-03-27T07:54:39Z) - Banal Deception Human-AI Ecosystems: A Study of People's Perceptions of LLM-generated Deceptive Behaviour [11.285775969393566]
Large language models (LLMs) can provide users with false, inaccurate, or misleading information.
We investigate peoples' perceptions of ChatGPT-generated deceptive behaviour.
arXiv Detail & Related papers (2024-06-12T16:36:06Z) - Not a Swiss Army Knife: Academics' Perceptions of Trade-Offs Around Generative Artificial Intelligence Use [12.85888736626468]
We interviewed 17 knowledge workers, including faculty and students, to investigate the social and technical dimensions of generative AI (Gen AI)<n>Our participants raised concerns about the opacity of the data used to train Gen AI.<n>Participants recognized Gen AI's potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant.
arXiv Detail & Related papers (2024-05-02T04:25:35Z) - Private Knowledge Sharing in Distributed Learning: A Survey [50.51431815732716]
The rise of Artificial Intelligence has revolutionized numerous industries and transformed the way society operates.
It is crucial to utilize information in learning processes that are either distributed or owned by different entities.
Modern data-driven services have been developed to integrate distributed knowledge entities into their outcomes.
arXiv Detail & Related papers (2024-02-08T07:18:23Z) - Beyond Factuality: A Comprehensive Evaluation of Large Language Models
as Knowledge Generators [78.63553017938911]
Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks.
However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge.
We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
arXiv Detail & Related papers (2023-10-11T08:22:37Z) - Adaptive cognitive fit: Artificial intelligence augmented management of
information facets and representations [62.997667081978825]
Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets.
Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information.
We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
arXiv Detail & Related papers (2022-04-25T02:47:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.