Dataset of GenAI-Assisted Information Problem Solving in Education

2026-01-19

AI Summary

This study addresses the empirical gap in understanding how students leverage generative artificial intelligence (GenAI) during information problem-solving, particularly amid cultural and socioeconomic diversity that raises equity concerns. Using FLoRA—a learning analytics platform embedded with a GenAI chatbot—the research collected multimodal data from 279 undergraduate students as they developed data science project proposals. The dataset encompasses dialogue logs, writing trajectories, final submissions, human-assigned scores, and background survey responses. For the first time, this work presents an open-access, fine-grained, and multimodal interaction dataset that systematically captures students’ collaborative processes with GenAI in authentic tasks and their associated learning outcomes. The resource offers an empirical foundation and innovative tool for designing inclusive AI-enhanced educational technologies.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Information Problem Solving (IPS) is a critical competency for academic and professional success in education, work, and life. The advent of Generative Artificial Intelligence (GenAI), particularly tools like ChatGPT, has introduced new possibilities for supporting students in complex IPS tasks. However, empirical insights into how students engage with GenAI during IPS and how these tools can be effectively leveraged for learning remain limited. Moreover, differences in background, shaped by cultural and socioeconomic factors, pose additional challenges to the equitable integration of GenAI in educational contexts. To address this gap, we present an open-source dataset collected from 279 students at a public Australian university. The dataset was generated through students'use of FLoRA, a GenAI-powered educational platform that widely adopted in the field of learning analytics. Within FLoRA, students interacted with an embedded GenAI chatbot to gather information and synthesize it into data science project proposals. The dataset captures fine-grained, multi-dimensional records of GenAI-assisted IPS processes, including: (i) student-GenAI dialogue transcripts; (ii) writing process log traces; (iii) final project proposals with human-assigned assessment scores; (iv) surveys of biographic and prior knowledge in data science and AI; and (v) surveys capturing students'GenAI experience and perceptions of GenAI's effectiveness in supporting IPS. This dataset provides a valuable resource for advancing our understanding of GenAI's role in educational IPS and informing the design of adaptive, inclusive AI-powered learning tools.

Problem

• Information Problem Solving
• Generative Artificial Intelligence
• Educational Equity
• Student Engagement
• GenAI in Education

Innovation

• Generative Artificial Intelligence
• Information Problem Solving
• Learning Analytics
• Educational Dataset
• AI in Education