Motivating Information Explorers: AI-based Orientation System for Promoting Web-based Investigative Learning

Abstract

Web-based investigative learning is an example of information exploration. Learners are expected to explore learning resources according to their interest in order to construct wider and deeper knowledge regarding their question. In our previous studies, we have modeled a process of investigative learning as a cycle of three phases: (1) searching and navigating Web resources, (2) knowledge construction, and (3) question expansion, and supported learners’ metacognitive activities with a cognitive tool we have developed. However, learners are required to be motivated for learning in order to engage in such self-regulated learning. In this paper, we propose an approach to promote learners’ motivation by providing information regarding their initial question so that they can perceive value in their investigation. To provide orientation according to learners’ values, we have classified the value types and viewpoints to present information about the question based on their values. We also propose a system that provides the orientation by generating summaries about their question using a large language model (LLM). According to a preliminary case study, it is suggested that the orientation approach promotes learners’ knowledge construction and deep question expansion.

Keywords: Information Exploration, Orientation, Value, Motivation, Self-regulated Learning

DOI: 10.54941/ahfe1006956