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

TL;DR

This study examines how generative AI chatbots influence information search behaviors in interdisciplinary bioinformatics learning, revealing that they support navigation after planning but can hinder it beforehand, with implications for e-learning design.

Contribution

It provides empirical insights into the nuanced role of GenAI in interdisciplinary education, highlighting when and how it aids or impedes learner information seeking.

Findings

GenAI supports orienteering after a learning plan is set.

Pre-planning, GenAI can be counterproductive for learners.

Traditional information cues are less effective in GenAI responses.

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.