293-1 “What’s Next?” Promoting Divergent, Critical, and Systems Thinking to Expand Beyond AI-Generated Professional Case Studies in Undergraduate Geoscience Courses.

Session: Developing and Assessing Workforce Skills in Geoscience Courses and Programs

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Abstract:

The Earth is a complex system with dynamic interdependencies. Alterations to a single component (e.g., geosphere, hydrosphere, biosphere, etc.) commonly trigger cascading effects across the system. Professional geoscientists are tasked with solving the interconnectedness between these components. Meanwhile, geoscience education should equip future professionals with the diverse skills needed to successfully navigate these interconnected systems on Earth.

The cognitive processes of divergent, critical, and systems thinking have been identified as vital skills for student development, helping them solve multifaceted challenges. Divergent thinking promotes the generation of creative questions. While critical thinking prompts the rigorous evaluation of information. Finally, these analyses culminate in systems thinking, a holistic approach which solves challenges not in isolation but as a connected network.  

The educational use of case studies fosters the development of divergent, critical, and systems thinking by presenting students with real-world problems. The pedagogical practices of inquiry-based learning (i.e., engaging students through exploration) and constructivism (i.e., constructing knowledge through experience) not only emphasize the use of case studies to authentically engage students and actively develop cognitive processing skills, but also serve as a powerful career-preparation tool. Students may simulate cognitive and procedural practices that were performed by professionals in the geoscience field in a low-pressure training scenario.

However, case studies are often presented as structured systems with fixed historical data sets and guided solution pathways. Conversely, the Earth system is dynamic and always evolving. We aim to use Artificial Intelligence (AI) to enhance case studies, making them adaptive and capable of emulating Earth's dynamic nature; utilizing variable data inputs and multiple solution pathways in evolving scenarios.

We have developed and implemented an active learning and career-preparation activity with scaffolded high-order learning objectives to: (1) apply content knowledge to effectively prompt an AI platform to produce a case study, (2) analyze and evaluate both applied and missing data sets within the case study, and (3) design a solution to help solve or limit the challenge presented. The use of an ill-structured case study developed in real time has successfully enabled students to expand their cognitive processing skills and explore several pathways to address complex simulated challenges on Earth. Furthermore, this approach serves as an accessible adaptive professional development tool for both geoscientists during active enrollment at a university or advanced workforce training.  

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