New Mexico State University received funding from the Nation Science Foundation (NSF) to implement a National Research Traineeship Program (NRT).
An NRT program explores ways graduate students in research-based doctoral degree program explore and develop skills, knowledge, and competencies needed to pursue a range of STEM careers.
Mission
The mission of the AIALA program is to expand the skillset of doctoral students in fields of Computer Science, Animal and Range Sciences, Plant and Environmental Sciences, Water Science and Management, and others can work to implement and develop new technologies utilizing Artificial Intelligence to their area of study to solve issues in arid land agriculture. Students will be exposed to professional development opportunities to prepare them to enter the workforce and be successful in their careers.
About AIALA
The purpose of the AIALA program is to (engage) introduce students studying one of the accepted degree programs to either, Artificial Intelligence (AI), or to Arid Land Agriculture.
Students will participate in courses that will prepare them to learn about the other degree program. For example, students in Animal and Range Sciences (ARS), will learn about the other program such as Computer Science (CS), Artificial Intelligence (AI), its terminology, as well as basics of coding or understanding coding concepts. The same logic will be used for Computer Science students in understanding the basics of PES and/or ARS. Students are not expected to become experts in secondary area. Students, however, are expected to comprehend and have a working knowledge of the alternate discipline. Students will also actively participate in a research project(s) where they will combine their primary discipline with a secondary field of study.
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Ethics- ethical training on working with human or animals as research subjects and the ethical standards according to IRB.
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Grant Writing- preparing students for finding grants and the writing process to develop a strong proposal for submission.
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Diversity- professional development opportunity to prepare students for the work-environment and how to collaborate with a diverse team.
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Other topics will also be addressed.
Primary emphasis of AIALA will be dedicated to underrepresented groups in AIALA areas. Emphasis will be place on Hispanic students. The number of students from underrepresented groups who enter and complete graduate studies in areas relevant to AI and ALA at PhD-granting institutions is disturbingly low, despite their growth in numbers nationally and representation in undergraduate studies. Nationally, Hispanic undergraduate enrollment increased from 22% to 36% between 2000 and 2018. Yet, the number of Hispanics who completed graduate programs in computing fields comprised a mere 3% in master’s and 2% in doctoral programs. Underrepresentation in graduate computing programs is also present for women, with 25% presence in doctoral programs. Similarly challenges arise in disciplines relevant to ALA – e.g., Hispanic students make up only 3.7% of the doctoral enrollment in agricultural sciences.
At NMSU in the last 5 years, the CS Hispanic student population grew from 40.8% to 62%, and the overall underrepresented minorities grew from 46% to 67.1%. AIALA will contribute collaborations with the broad CAHSI network of HSIs, which has already demonstrated a higher potential to develop talent in computing for underrepresented groups. Since 2000, CAHSI institutions have graduated a higher share of Hispanic computing bachelor’s degrees than average U.S. higher education institutions and HSIs. Higher shares of Hispanic graduate students are enrolled in HSIs, making HSIs essential sites for Hispanic graduate recruitment in STEM.
One of the objectives of AIALA is to adapt proven recruitment practices developed at the undergraduate level (at NMSU and through CAHSI) to promote diversity at the graduate level. AIALA will also work in close partnership with the Center of Excellence on Sustainable Food and Agricultural Systems at NMSU to attract graduate students interested in innovation in agricultural systems.