31 Transforming Geoscience Education: Reimagining how we teach the Earth
This session will explore innovative and student-driven approaches to geoscience education. Contributions may focus on either design and implementation or research on evidence-based instructional strategies like active learning, project-based learning, field experiences, and others.
Tuesday, 10 March 2026
1:30 PM - 5:00 PM
RCC, 105
Student GAI use in introductory geoscience courses: an initial implementation into a first-year earth history course
Paper number: 31-10
Program: Technical Sessions
Time: 4:00 PM – 4:15 PM
Authors
Abstract
Generative artificial intelligence (GAI) presents itself in the most common, everyday digital landscape with many students well versed in prompting for information and getting results of unknown quality and limited quality analysis and review. Often, simple prompt-writing does not translate to discipline-specific, meaningful engagement for the student, or to the information being promulgated from faculty to the student. Lack of experience and deep exposure to complex topics leaves new students at a disadvantage for critical analysis of GAI responses, and thus a poor understanding of the topics being studied. This presentation will focus on a pilot program established to determine methods and outcomes to properly and/or responsibly integrate GAI into introductory geoscience courses.
This initial trial focuses on the introduction of GAI to an introductory course in the geosciences, specifically GEOL 105 or Earth History, specifically to bridge a growing gap between GAI activities and reasoning at the introductory geoscience level. The students’ use of GAI will be based around the learning outcome: “students will responsibly, transparently, and intentionally use GAI tools to organize, theorize upon, and analyze Earth history data.” In general the scaffolds will have the students: (1) gather traditional resources (through library and Google scholar searches) and summarize information by reading published information (no GAI), (2) prompt GAI (Copilot, Claude.ai, ChatGPT) first to generate testable output using only the software with similar inputs used to gather information similar to the traditional resources, and (3) develop GAI interactions and prompts with the students’ insights to expand their understanding of a topic and make qualitative predictions of the more detailed outcomes. Information presented will include early reflections on multiple projects to evaluate overall student experiences and programmatic successes and failures. Assessments will be made on actual outcomes, in-class discussion, and through student learning metrics. However, it is clear that these observations will be context specific as ongoing lessons learned and opportunities identified will be examined for the pilot program.
Geological Society of America Abstracts with Programs. Vol. 58, No. 1
doi: 10.1130/abs/2026SE-411856
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