From Data To Sensemaking: Supporting Student Learning Through AI-Integrated CURE Design
This poster presents an AI-integrated CURE design that supports student sensemaking with real-world cybersecurity data through scaffolded assignments, ethical AI use, and research-driven learning.
Presented by:
Li Xu, The University of Arizona
Hear it from the author:
Transcript:
Hi, this is Li Xu from the University of Arizona.
My work focuses on how we can better support student sensemaking when they work with complex data and generative AI in higher education. One challenge I’ve observed is that while students now have access to powerful AI tools, they often struggle to interpret data meaningfully and to evaluate AI outputs critically and ethically. To address this, I designed an AI-integrated Course-based Undergraduate Research Experience, or CURE, that brings together data thinking, structured prompting, and reflective inquiry. At the core is a sensemaking cycle where students explore data, interpret patterns, generate research questions, and then critically evaluate AI-supported interpretations using evidence before refining their analysis. The key idea is that AI is positioned as a supporting tool—not an answer generator—while students remain responsible for reasoning, justification, and insight development.
I’d be really interested to hear how you’re thinking about helping students engage critically with AI in your own teaching. Thank you.
Key Words:
Sensemaking, AI, CURE
Abstract:
This poster presents the design of an AI-integrated Course-based Undergraduate Research Experience (CURE) that engages students in analyzing real-world healthcare cybersecurity data and developing metacognitive sensemaking practices. While prior work has examined CURE and generative AI separately, limited research has explored their intentional integration to support student sensemaking. Grounded in data thinking and ethical AI literacy, the framework incorporates scaffolded AI use, reflective writing, and structured prompting to guide inquiry. The design demonstrates how generative AI can support data exploration and modeling while requiring critical evaluation and responsible use, offering a practical, learner-centered framework for research-driven teaching.
Outcomes:
1. Analyze how an AI-integrated CURE framework supports student sensemaking through scaffolded assignments, reflective writing, and structured prompting strategies.
2. Apply structured prompting and generative AI strategies to support data exploration, modeling, and student sensemaking while promoting critical evaluation and ethical use.
3. Construct or adapt elements of an AI-integrated CURE design to support students’ engagement in data-driven scientific inquiry, including question formulation, data analysis, and evidence-based reasoning.
References:
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning (SSRN Scholarly Paper 4337484). Social Science Research Network. https://doi.org/10.2139/ssrn.4337484
Bozkurt, A., & Bae, H. (2024). May the Force Be With You JedAI: Balancing the Light and Dark Sides of Generative AI in the Educational Landscape. Online Learning, 28(2), Article 2. https://doi.org/10.24059/olj.v28i2.4563
Farrelly, T., & Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11), 1109-. https://doi.org/10.3390/educsci13111109
Hillier, M. (2023, March 30). A proposed AI literacy framework. TECHE. https://teche.mq.edu.au/2023/03/a-proposed-ai-literacy-framework/
OECD (2021), AI and the Future of Skills, Volume 1: Capabilities and Assessments, Educational Research and Innovation, OECD Publishing, Paris, https://doi.org/10.1787/5ee71f34-en.