Use of Generative AI in Web-based Investigative Learning

Nowadays, generative AI has had a considerable influence on learning and education. It has found application invarious contexts. However, it remains unclear how it should be utilized to promote learning. In this paper, we address anissue of whether and how generative AI could be used in the context of investigating any question with exploration ofinformation obtained from the Web to construct knowledge in a wider and deeper way. In our previous research, we havemodeled the investigative learning process with Web resources, and developed a cognitive tool called interactive LearningScenario Builder (iLSB), which provides some scaffolds for conducting the Web-based investigative learning process asmodeled. In this paper, we describe case studies, in which the use of generative AI has been examined in comparison with theuse of iLSB in the following three contexts: (1) learning through communicating with generative AI before knowing the wayto learn represented by the Web-based investigative learning model, (2) learning through communicating with generative AIafter knowing the model, and (3) learning by means of iLSB integrated with generative AI. The results of the studiesdemonstrated that, in comparison to iLSB, generative AI was less effective in helping learners acquire information related tothe question. Furthermore, it was observed that learners were able to acquire a greater amount of information with generativeAI after knowing the model. Additionally, it is suggested that generative AI could effectively assist in acquiring backgroundknowledge about the question to be investigated before exploring Web resources and in organizing information to constructknowledge.

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Publication Details

Journal
Transactions of the Japanese Society for Artificial Intelligence
Published
2025-05-01
DOI
https://doi.org/10.1527/tjsai.40-3_f-o106
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Use of Generative AI in Web-based Investigative Learning

Akihiro Kashihara, Yutaka Watanabe
Transactions of the Japanese Society for Artificial Intelligence
Artificial Intelligence in Healthcare and Education
article

Use of Generative AI in Web-based Investigative Learning

Akihiro Kashihara, Yutaka Watanabe
article en

Abstract

Nowadays, generative AI has had a considerable influence on learning and education. It has found application invarious contexts. However, it remains unclear how it should be utilized to promote learning. In this paper, we address anissue of whether and how generative AI could be used in the context of investigating any question with exploration ofinformation obtained from the Web to construct knowledge in a wider and deeper way. In our previous research, we havemodeled the investigative learning process with Web resources, and developed a cognitive tool called interactive LearningScenario Builder (iLSB), which provides some scaffolds for conducting the Web-based investigative learning process asmodeled. In this paper, we describe case studies, in which the use of generative AI has been examined in comparison with theuse of iLSB in the following three contexts: (1) learning through communicating with generative AI before knowing the wayto learn represented by the Web-based investigative learning model, (2) learning through communicating with generative AIafter knowing the model, and (3) learning by means of iLSB integrated with generative AI. The results of the studiesdemonstrated that, in comparison to iLSB, generative AI was less effective in helping learners acquire information related tothe question. Furthermore, it was observed that learners were able to acquire a greater amount of information with generativeAI after knowing the model. Additionally, it is suggested that generative AI could effectively assist in acquiring backgroundknowledge about the question to be investigated before exploring Web resources and in organizing information to constructknowledge.

Transactions of the Japanese Society for Artificial IntelligenceVol. 40(3)
University of Electro-Communications (JP)
Openalex Percentile: Top 94%
Artificial Intelligence in Healthcare and Education
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