Enrico Pantale, dean and director of a new NMSU Institute for Applied Practice in AI and Machine Learning, told the committee that the university is bringing together campus expertise to apply AI to state priorities and to expand AI education for students and K–12 teachers.

“AI is here…we have to prepare our students to handle AI,” Pantale said, describing an institute-funded effort that supports research teams and a newly launched bachelor’s degree in artificial intelligence and a professional-development program for K–12 teachers.

Why it matters: NMSU presented applied projects focused on rural needs: precision ranching and virtual fencing that reduce labor and allow real‑time animal and water monitoring; a state‑branded extension chatbot built from validated extension data to provide locally relevant, cited answers; and an AI‑enhanced 4‑H curriculum designed for K–12 engagement and peer review.

Craig Gifford of NMSU’s animal and range sciences department summarized precision‑ranching pilots that combined collars, towers and a portal to track animal movement, water status and range conditions after large wildfires. He said virtual fencing reduced cattle impacts in riparian zones and shortened livestock-gather time. “We were able to monitor calving events and alert ranchers,” Gifford said, describing machine‑learning analyses that flagged a calf‑predation event and a cow with a suspected broken toe so ranchers could intervene.

Marcus Krone and NMSU extension staff described an “extension bot” pilot: a chatbot trained on validated extension materials and hosted as an extension‑branded tool so local farmers and educators can get real‑time, citation‑backed guidance. The project includes peer review, data‑governance protections and a centralized extension data repository that controls which generative models or APIs can access validated content.

Discussion and equity concerns: Committee members and presenters discussed the cost and data ownership implications of agricultural IoT and virtual‑fence deployments. Presenters acknowledged implementation costs for towers and collars but said device and IoT pricing has declined and that cooperative or public‑private arrangements (for example, shared towers or Forest Service participation) could lower entry costs for small ranchers. Presenters also described data‑security plans and emphasized that local producers would control and share data only as they choose.

Ending: NMSU invited land grants and local practitioners to co‑design pilots, offered teacher professional development and said it will continue to expand AI education and applied projects for rural New Mexico.