Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning

2025-07-29

AI Summary

This study investigates how generative AI (GenAI) reshapes information-seeking behaviors among interdisciplinary learners—exemplified by bioinformatics students—with particular focus on their orienteering strategies within knowledge spaces and their ability to detect “information scent” in AI-generated responses. Method: Employing autoethnography, the research analyzes multi-round GenAI interaction logs to trace cognitive dynamics across learning phases. Contribution/Results: Findings indicate that while GenAI enhances orienteering efficiency after goal clarification, it induces significant cognitive overload during early exploration. Crucially, conventional text-based information scent cues lose predictive validity; instead, domain-specific prior knowledge emerges as a key moderator for discerning high-quality AI outputs. This work presents the first empirical account of shifting cognitive mechanisms in GenAI-mediated information seeking and proposes a novel framework—“prior-knowledge-driven information scent evaluation”—offering evidence-based design principles and pedagogical risk alerts for educational AI integration.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Philosophy and Ethics of AI: AI & Epistemology
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This full research paper investigates the impact of generative AI (GenAI) on the learner experience, with a focus on how learners engage with and utilize the information it provides. In e-learning environments, learners often need to navigate a complex information space on their own. This challenge is further compounded in interdisciplinary fields like bioinformatics, due to the varied prior knowledge and backgrounds. In this paper, we studied how GenAI influences information search in bioinformatics research: (1) How do interactions with a GenAI chatbot influence learner orienteering behaviors?; and (2) How do learners identify information scent in GenAI chatbot responses? We adopted an autoethnographic approach to investigate these questions. GenAI was found to support orienteering once a learning plan was established, but it was counterproductive prior to that. Moreover, traditionally value-rich information sources such as bullet points and related terms proved less effective when applied to GenAI responses. Information scents were primarily recognized through the presence or absence of prior knowledge of the domain. These findings suggest that GenAI should be adopted into e-learning environments with caution, particularly in interdisciplinary learning contexts.

Problem

• Impact of GenAI chatbots on learner information search behaviors
• Effectiveness of GenAI in interdisciplinary learning like bioinformatics
• Role of prior knowledge in recognizing GenAI information scent

Innovation

• GenAI chatbot for interdisciplinary learning
• Autoethnographic approach in bioinformatics research
• Information scent recognition via prior knowledge