Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots
Project Overview
The document explores the integration of generative AI (GenAI) in education, focusing on Learning Analytics Dashboards (LADs) and the role of chatbots in enhancing learner engagement. It emphasizes the significance of GenAI literacy, revealing that both conventional and scaffolding chatbots improve learners' understanding of complex visualizations. However, the research indicates that scaffolding chatbots offer more substantial support, particularly benefitting learners with lower GenAI literacy levels. This highlights the need for educational tools to prioritize GenAI literacy in their design, suggesting that incorporating scaffolding techniques can foster a more equitable learning environment. Overall, the findings advocate for the thoughtful integration of GenAI in educational contexts to enhance comprehension and support diverse learner needs.
Key Applications
Generative AI chatbots integrated into Learning Analytics Dashboards (LADs)
Context: Educational technology for medical and nursing students engaging with complex visualizations
Implementation: Developing conventional (reactive) and scaffolding (proactive) GenAI chatbots to assist learners in understanding LADs
Outcomes: Both chatbots significantly improved learners' comprehension of visualizations, with scaffolding chatbots being particularly beneficial for those with lower GenAI literacy.
Challenges: Effectiveness varies based on learners' GenAI literacy; lower literacy may hinder full engagement with conventional chatbots.
Implementation Barriers
Literacy Barrier
Varying levels of GenAI literacy among students impact their ability to effectively engage with GenAI chatbots.
Proposed Solutions: Integrating scaffolding techniques in chatbots to provide guided support, reducing reliance on high GenAI literacy.
Complexity Barrier
Complex educational data and visualizations may overwhelm learners without proper support.
Proposed Solutions: Utilizing explanatory analytics and multimodal GenAI to simplify data interpretation and enhance user interaction.
Project Team
Yueqiao Jin
Researcher
Kaixun Yang
Researcher
Lixiang Yan
Researcher
Vanessa Echeverria
Researcher
Linxuan Zhao
Researcher
Riordan Alfredo
Researcher
Mikaela Milesi
Researcher
Jie Fan
Researcher
Xinyu Li
Researcher
Dragan Gašević
Researcher
Roberto Martinez-Maldonado
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yueqiao Jin, Kaixun Yang, Lixiang Yan, Vanessa Echeverria, Linxuan Zhao, Riordan Alfredo, Mikaela Milesi, Jie Fan, Xinyu Li, Dragan Gašević, Roberto Martinez-Maldonado
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai