Date
Publisher
arXiv
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.
Study at a glance
- Question: How do generative AI chatbots influence how learners search for information in complex, interdisciplinary subjects?
- Setting and data: Online bioinformatics learning setting.
- Method: Researchers studying their own learning experiences (autoethnographic approach).
Why it matters
Learners in e-learning environments often must navigate complex information spaces on their own. This challenge is compounded in interdisciplinary fields due to learners' varied prior knowledge and backgrounds.
Key findings
- In bioinformatics learning, generative AI supported information navigation once a learning plan was established, but was counterproductive before a plan existed.
- Traditionally helpful features like bullet points and related terms proved less effective for evaluating information in generative AI responses.
- Learners recognized useful clues in chatbot responses primarily through their own prior knowledge of the subject matter.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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