Focus 4: Broadening Participation and Workforce Development

broadening participation and workforce devlopment

Each member of the senior leadership team is deeply committed to improving the diversity of both AI and ES workforces.  This is shown in our extensive broadening participation and workforce development and outreach plans, where we are reaching out to underrepresented populations from K-12 through community college. 

AI2ES leadership is deeply committed to improving equity and to be inclusive and welcoming to all.  As part of this commitment, we have created a strong Code of Ethics and Code of Conduct for AI2ES members.

AI2ES has three overarching goals for both Broadening Participation and Workforce Development.  These goals aim to increase the opportunities for students of all backgrounds to learn more about AI, environmental science, and AI for environmental science.

  • Build a diverse AI for ES workforce by implementing and evaluating an applied AI for ES curriculum at the Community College Level and pilot test at an HSI/MSI
  • Develop and share AI/ES curriculum for K-20 and workforce retraining
  • Broaden and train AI for ES through targeted internships, mentoring and recruitment.

Broad Goals

1. Build a diverse AI for ES workforce by implementing and evaluating an applied AI for ES curriculum at the Community College Level and pilot test at an HSI/MSI

The first goal centers on the design and implementation of a new Community College AI program, being developed at DMC and in conjunction with TAMUCC. The award is designed as a multi entry program for professionals to augment their skills, for high school students as a next step in their education, for direct employment. The South Texas location of the new Community College AI educational program will contribute to the diversification of the AI workforce and test a model to create an Applied AI workforce along with creating a new educational pipeline. The model will be exported nationwide through a collaboration with the Advanced Technological Education (ATE) program of two-year community colleges. AI2ES will work with the directors and principal investigators of ATE national centers to disseminate the new curriculum materials through the networks and workshops. AI2ES will push diversity through its recruiting, hiring, outreach, public events and other communication. AI2ES has ambitious goals for diversity recruitment for its team members and will improve its chances to meet these goals through talks and recruiting through SACNAS, CUAHSI and other conferences and organizations promoting entry into STEM and AI for underrepresented populations in our field.

Our specific goals are to:

  • Pilot development of Artificial Intelligence Community College Educational program at Del Mar College
  • Nationwide promotion and documentation of the DMC/AI2ES new program
  • Assistance to develop similar programs across the nation with emphasis on other institutions serving underrepresented minorities.
  • Create sustainable pathway of new students from secondary school while continuing to serve returning adult learners
  • Complete two new ML/GIS courses
  • Complete the third GeoAI course
  • Engage learners in meaningful AI/ML projects
  • Market GeoAI program city-wide
  • Expand our outreach participation at  national conferences
  • Increase number of women and minorities engaged in AI education 
  • Engage our local geospatial technology industries at our annual GIS Day
  • Develop open source GeoAI labs with International travel supplement award

Del Mar College AI Award

AI Award Curriculum

AI Award details

Curriculum of the Del Mark Community College AI Certificate

AI Award is Stackable at DMC

Stackable AI award

The AI certificate is stackable in the community college and university curriculum. 

Community College AI/ML Programs

  • Team presented program plans at the 2020 Virtual ATE Conference “Broadening the AI Workforce through a Community College Program to start national expansion.
  • New AI/ML Occupational Skills Award approved by college
  • Completion of the AI within GIS applications course
  • New pipelines established
    • Incoming students ~ 24 students since Fall 2021
    • Undergraduate student researchers from DMC for TAMUCC ~ 3 students
    • Continued DMC graduates transferring to TAMUCC
    • New secondary school teacher participants in the Fundamental of AI course (Fall 2022)

Webinar ad for DMC

AI in GIS marketing flyer


Building the Nex-Gen GeoAI Curriculum

GEO AI Learners



  • Successfully completed first cohort through 2 of 3 new GeoAI courses, set for graduation in fall 2022
    • GeoAI 101: Fundamental of AI with Machine Learning
    • GeoAI 102: Applying ML Tools to GIS
  • Successfully completed first GeoAI course for second cohort
  • Completed the advanced GeoAI course labs and lecture materials
  • Updated the new GeoAI course curriculums
  • Established F2F pipeline with local schools and government agencies for recruitment of students
  • Created pipeline of transfer students between Del Mar College and TAMUCC partners
  • Success in enrolling sustainable cohort of students for Fall 2022
  • Engagement with the local GIS Industry Advisory Board
    • Meet twice annually to review program, curriculum, graduates
    • Rely on industry experts to support the program with internships and jobs
    • Disciplines include GIS, Remote Sensing, Unmanned Vehicles

International Collaborations

  • Del Mar College seeks the long-term sustainability and global dissemination of AI/ML materials
    • Seek to build a modern open source software alternative for our curriculum
    • Work with our EU partners who lead in open source software development
    • Create a repository of undergraduate GeoAI curriculum modules

Leaders: Davis (DMC), Tissot (TAMUCC)
Members: Barrett (DMC/TAMUCC), Betz (DMC), Caruso (DMC), Ebert-Uphoff (CSU), Kennington (DMC/TAMUCC), King (TAMUCC), Nelson (DMC), O’Cinneide (DMC/TAMUCC), Starek (TAMUCC)

2. Develop and share AI/ES curriculum for K-20 and workforce retraining

AI2ES is actively developing modules for AI and ES for K-12, as well as tutorials for undergraduate and graduate students and workforce retraining.  Many of our outreach efforts are targeted specifically at women, under-represented minorities, low socioeconomic-status students, and first-generation students.

Our specific goals are to:

  • Develop K-12 education modules for AI & ES and share online
  • Develop and teach K-12 modules to low socioeconomic status, first generation, and URM students
  • Develop and teach AI and trustworthy AI tutorials aimed at undergraduates and graduate students for AI & ES and share online
  • Publish at least 4 tutorial papers and shared data and code on AI for ES
  • Publish at least 3 talks for the NCAR Explorer Series per year after year 1
  • Host public demonstrations of AI for ES at events such as the National Weather Festival, Weather Fest, and NCAR Super Science Saturday.
  • Make targeted partnerships to increase the impact of our education and workforce development modules as well as career training.
  • Develop and share an AI ethics curriculum which includes ES ethics.

Summer STEM Coding Camp for Middle Schoolers

  • Successfully conducted five day Summer Code Camp, #Code_IT Camp
    • Hosted 15 middle school students in 2021
    • Hosted 20 middle school students in 2022
  • Camp offers several levels of coding curriculum focused on spatial reasoning and computer programming, logic-based programming, and text-based coding for drones.
  • Student team worked together towards the final project on the last day, the Ultimate Team Challenge
    • Each team will include at least one advanced member to lead team
  • Each camper took home a mBot Neo robot they individually built and coded to continue their STEM education.
  • Team centric approach allowed campers to improve on their technical, communication and leadership skills as they worked as a team to achieve camp objectives.
  • Prior year campers are invited to join current year as volunteers to mentor and assist in instruction

DMC Coding camp


Trustworthy AI for Middle/High School

Trustworthy AI for Middle/High School

  • Core of the AI code completed, and development/redevelopment will continue as appropriate to fit AMS distribution model
  • Made contact with AMS education program director to begin coordination for deployment in Year 3
    • Identify appropriate programs, ideally targeting minority- and underserved middle and high school programs
    • Exploring opportunity to pilot with local schools as part of AMS initiative – including Spanish-language bilingual programs

Trustworthy Artificial Intelligence for Environmental Science (TAI4ES) Summer School

Summer School flyer


summer school registrant map

  • We hosted the TAI4ES summer school in 2021 and 2022
  • July 26-29, 2021
    • Over 400 attendees on 26-27; about 200 attendees on 28-29
    • Day 1: Trustworthy AI for Environmental Science
    • Day 2: Explainable AI, Robustness, Uncertainty
    • Day 3: Ethics, Risk communication
    • Day 4: Case studies, R2O
  • June 27-30, 2022
    • Over 600 attendees
    • Intertwined lectures from institute members and hands-on activities
    • Morning lectures and short activities on foundational topics
    • Afternoon trust-a-thon: developed user personas and reflection questions that emphasized thinking about the user while completing the coding exercises
    • Day 1: Trust, Interdisciplinary research, XAI
    • Day 2: Explainability, Interpretability, XAI
    • Day 3: Trust and Data
    • Day 4: Uncertainty Quantification
  • All materials are freely available
  • Plan to create customizable learning journeys for learners of all ages
    • Goal: Make it easy for scientists with different backgrounds to select modules for their own learning.
    • Create “Make-your-own-summer-school” framework that remains available for anyone on our website.

Courses, Tools, Tutorials, and Workshops

  • a Python Toolkit for traditional ML explainability
    • Feature Importance – Different flavors of permutation importance, ALE/PD variance, and grouped importance 
    • Feature Effects/Attributions – Partial Dependence (PD), Accumulated Local Effects (ALE), ICE, SHAP, LIME, tree interpreter
    • Feature Interactions – 2nd Order PD/ALE, interaction strength, and Friedman H-statistic
ML Tutorial for Earth Scientists
  • 250+ slides of a gentle introduction to ML for earth scientists (focus on climate science)
  • Created by graduate students and postdocs in ATS at CSU
  • Code and slides available on github and Zenodo
  • Supported by DOE and NSF CAREER grants
ML for Atmospheric Science* Grads
  • First advanced machine learning course for earth scientists at CSU
  • 25 participants
  • Nearly every lecture included coding modules
  • Material is freely available on github

ML for Atmospheric Sciences at CSU

AI2ES Machine Learning Tutorials for Operational Meteorology
  • As an effort to provide a meteorology specific ML reference, we began drafting two papers to provide a plain language summary with open source code. Part 1 (on traditional ML) has been published in WAF and part 2 (on deep learning) will be submitted soon.
  • The paper medium was chosen to enable chance encounters with WAF readers (typically meteorologists in the operational sector).
  • These papers will be presented at AMS 2023 to increase visibility of the work

risk comm workshop landing page

Risk communication (RC): Workshop on Trust and Trustworthy AI/ML
  • Virtual workshop held over 2 half-days, August 15-16, 2022. The 29 attendees included 8 graduate student and postdoctoral notetakers, 9 members of the AI2ES leadership team including RC Team leads, and an EAB member.
  • Participants represented diverse perspectives, from computer science, atmospheric science, risk and science communication, environmental sciences, psychology, sociology, and cognitive and industrial systems engineering, and from across the public and private sectors.
Risk communication (RC): Tutorial
  • Introduce AI2ES researchers to risk perception and communication research, the role of trust in risk communication
  • Put new research on human trust in AI into the context of prior research on trust and risk communication
  • Increase familiarity with risk concepts and risk communication research methods and findings potentially applicable for AI and XAI
  • Facilitate and promote interdisciplinary collaboration across AI2ES
  • More information

Risk Tutorial image

Additional K-20 and workforce retraining outreach

  • Partnered with American Meteorological Society for their teacher training
  • Site-wide meetings to full AI2ES membership. These meetings are recorded and published on the AI2ES Website as AI2ES Talks
  • Meetings were held to discuss potential collaborative educational opportunities with NOAA and with the AMS Education Programs.
  • Del Mar College hosted a Discovery Day for high schoolers. AI2ES presented a talk and provided an all-day demonstration of UAS and GIS technologies. An estimated 900 attendees total (March 25, 2022).
  • Del Mar College presented at a NASA-funded STEM camp for secondary students on April 5 and May 21, 2022 discussing our AI2ES curriculum and projects.
  • Del Mar College participated in recruitment events, Bay Day/Earth Day and the Hurricane Conference.
  • The TAMU-CC AI2ES team was highlighted as part of the 2022 TAMU-CC Research week through a panel entitled “Pioneering Coastal Artificial Intelligence”

Project Atmosphere

  • Amy McGovern participated in “Project Atmosphere” in July 2022
    • Gave one hour lecture on AI for weather and climate
  • Meetings were held to discuss potential collaborative educational opportunities with NOAA and with the AMS Education Programs.
  • TAMU-CC Co-PI Tissot taught Environmental Forecasting
  • OU PI McGovern teaches AI fully online

Leaders: Gagne (NCAR), McGovern (OU), Rogers (CSU) 
Members: Barnes (CSU), Becker (NCAR), Betz (DMC), King (TAMUCC), Kumler (NOAA), Lagerquist (CSU/NOAA), Tissot (TAMUCC)

3. Broaden and train AI for ES through targeted internships, mentoring and recruitment

Our internal mentoring and internships will increase participation and provide research and educational opportunities to US citizens, nationals, and permanent residents, especially women and members of underrepresented groups who are undergraduate and graduate students, postdoctoral researchers, industrial fellows, faculty members from all colleges and universities, and others in the activities of AI2ES and its sub awardees. AI2ES will encourage them to pursue careers in science and engineering and will identify actions that will enhance and ensure ethnic/racial diversity throughout the life of the Institute.  Our specific goals for the internships and mentoring are listed below.

  • Mentor all AI2ES junior faculty, postdoctoral fellows, graduate students, and undergraduate students.
  • Create internships for AI & ES targeting to URMs in private industry partners
  • Support and hire SIParCS and SOARS internships every year
  • Hire REU students at all sites and work with existing REU programs such as National Weather Center REU program
  • Support immersive visits for postdoctoral researchers and graduate students

Postdoctoral Scholars 

  • AI2ES currently supporting 9 postdoctoral scholars with 3 additional affiliated postdocs
  • Postdocs hired into a cohort
  • Learning from each other and others in AI2ES about research approaches and concepts outside of their disciplinary specialties – and applying them in some cases!

Postdoctoral Scholars

Research Experiences for Undergraduates

  • OU and TAMU-CC have hosted AI2ES REU students in 2021 and 2022
    • AI2ES has funded and mentored 22 students
    • OU has partnered with the established NWC REU program, and TAMU-CC has partnered with the Conrad Blucher Institute REU program
  • NCAR has hosted and mentored SIParCS interns 
    • Two interns in 2021 and two in 2022 focused on AI2ES research

TAMU-CC REU and Coding Camp


Mentoring activities

  • AI2ES is mentoring REU students at OU, UA, NCAR, TAMUCC helped by IBM – 5 TAMU-CC students in internship summer 2022
  • Internship opportunities offered by Industry partners
  • DMC has two active cohorts of learners in progress through our educational pipeline, with a 90% high pass rate for each cohort.
    • Several DMC graduates have transferred to TAMUCC for CS and GIS
    • TAMU-CC is currently employing six DMC students or graduates as undergraduate research assistants
  • AMS AI Conference January 2022: Hamid Kamangir won honorable mention for “Importance of 3D Convolution- and Physics-Based Modeling of Atmospheric Predictions: Fog Forecasting Case Study”
  • TAMU-CC 2022 Spring Student Research Symposium, April 8, 2022 – PhD student Evan Krell won the overall third place for “The influence of grouping features on explainable artificial intelligence for a complex fog prediction deep learning model”
  • Sandbox meteorological dataset and AI models created for the aforementioned papers and the AI2ES 2022 Summer School

Recruitment and outreach activities

  • Discovery Day at DMC brings in 700+ middle and high schools for STEM awareness: NASA-funded summer workshop for secondary and college students.
  • Spanish outreach:
    • AI2ES PhD student Marina Vicens-Miquel and fellow PhD student at the TAMU-CC Conrad Blucher Institute Isabel Garcia gave a TV interview (KRIS 6 News) on the topic “Hispanic women leading the way in STEM”
    • MyRadar is developing multiple AI2ES spanish-language videos for the general public to be deployed in their app
  • Women in STEM:
    • Members of AI2ES hosted a panel discussion at the 21st Conference on Artificial Intelligence for Environmental Science entitled “Women in AI: A Panel Celebrating the Pioneering Work of Women in the Field of Environmental Artificial Intelligence”.
  • Other HSI/MSI outreach:
    • CSU and CIRA supporting a student from City College of New York, a MSI, through NOAA but actively collaborating with AI2ES researchers
    • AI2ES has initiated a collaboration with NOAA CSCs to improve our REU diversity and recruitment

Coastal Bend AI2ES team

  • Diverse Students AI Pipeline
  • Hired and mentoring a diverse group of fourteen undergraduate research assistants at TAMUCC and DMC as well others AI2ES universities
  • Recruiting events for the AI2ES Community College program at local hispanic majority high schools and middle schools

Leaders: Williams (IBM), King (TAMUCC), Betz (DMC) 

Members: Barnes (CSU), Foster (Disaster Tech), Gagne (NCAR), Griffin (Disaster Tech), Hall (NVIDIA), Homeyer (OU), Kumler (NOAA), Lagerquist (CSU/NOAA), Medrano (TAMUCC), Musgrave (CSU), Snook (OU), Starek (TAMUCC), Tissot (TAMUCC), Tyle (Albany)