Enhancing graduate students’ data visualization literacy through an inquiry-based learning companion system integrated with generative AI
Year: 2025
Type: article
Abstract: Incorporating intelligent robotics into STEM education enhances students’ knowledge proficiency and higher-order thinking. In the era of big data, students must possess strong data literacy and visualization skills. However, students often struggle to understand and apply data effectively. This study developed an Intelligent Inquiry-Based Learning Companion System utilizing Generative AI (GenAI) to address these challenges and improve data visualization literacy among graduate students. The study involved 53 graduate students from a university in northern Taiwan, enrolled in an 18-week course. The experimental group used the Intelligent Inquiry-Based Learning Companion System, while the control group used a general ChatGPT for their interactions. The students interacted with an AI, which supported their inquiry-based learning through interactive conversations. The results indicate that students become more confident in interpreting complex material sets after using the Intelligent Inquiry-Based Learning Companion System. Students also find it easier to share their findings with classmates. The inquiry-based learning companion system enhances organizational strategies and critical thinking, although it has a limited impact on metacognitive self-regulation, peer learning, and help-seeking behaviors. The system completed the coursework, although some users reported issues with more complicated, open-ended tasks that required further assistance from humans to complete them properly. It enabled students to dive into a more specific set of questions. In addition, students mentioned that immediate, customized responses and dashboards can enrich their learning experience.
Author: Hui-Chun Hung and Pei-Jhou Lai
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7095066233