{"id":668,"date":"2025-11-25T19:45:14","date_gmt":"2025-11-25T19:45:14","guid":{"rendered":"https:\/\/coeng.uaa.alaska.edu\/AI\/?page_id=668"},"modified":"2026-01-05T21:51:19","modified_gmt":"2026-01-05T21:51:19","slug":"raise-mini-grant-awardee-announcement-25-26","status":"publish","type":"page","link":"https:\/\/coeng.uaa.alaska.edu\/AI\/raise-mini-grant-awardee-announcement-25-26\/","title":{"rendered":"RAISE Mini Grant – Awardee Announcement 25\/26"},"content":{"rendered":"
\n \n \n\t\n\n\n\n
\n \n \n\t\n\n\n\n
\nRAISE - Mini Grants\n<\/div>

\nGrant Awards - Alaska AI Solutions Consortium Awardees\n<\/h1>
\nAcademic Year 25\/26\n<\/div>\n<\/div><\/div><\/div>\n<\/section>
\n \n \n\t\n\n\n\n
\nAnnouncing the Alaska AI Solutions Consortium grant awardees advancing AI innovation across Alaska through the RAISE initiative at UAA.\n<\/div><\/div>\n<\/section>
\n \n \n\t\n\n\n\n

\nProject #1\n<\/h1>
\n \n \n\t\n\n\n\n

\nProject:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nPredicting Thermal Conductivity of Warming Permafrost for Hazards Estimation using Machine Learning\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nFaculty PI:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nZhaohui (Joey) Yang, Ph.D.
Professor, Department of Civil Engineering at UAA
Chief Editor, ASCE J. of Cold Regions Engineering\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nCommunity
Partner:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nNome Joint Utilities System\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nGoals &
Outcomes:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nPermafrost thaw results in a significant loss of bearing capacity and ground subsidence, threatening the built infrastructure in permafrost regions. Traditional empirical and analytical methods struggle to capture permafrost's complex, nonlinear, coupled thermal-mechanical behavior. This proposal aims to develop data-driven models for predicting the thermal conductivity of warming permafrost using advanced machine learning (ML) techniques. Collaborating with our community partner, we will apply these developed models to evaluate the degradation of a key utility site in rural Alaska and predict the risks associated with permafrost degradation.\n<\/h1>\n<\/div><\/div>\"Nome<\/div>\n<\/section>
\n \n \n\t\n\n\n\n

\nProject #2\n<\/h1>
\n \n \n\t\n\n\n\n

\nProject:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nNorth Star Parents: AI-assisted behavioral parent training for Alaskan families\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nFaculty PI:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nKristin A. Riall, Ph.D., BCBA, LBA
Assistant Professor, Department of Psychology, College of Arts & Sciences at UAA
Behavior Analysis Program and Practicum Coordinator\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nCommunity
Partner:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nStep-In Autism Services\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nGoals &
Outcomes:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nBuilding an app that can provide in the moment supports for families who have children with Autism but are unable to access traditional behavior analytic training. We plan to provide in the moment guidance for managing challenging behaviors for families who are waiting for additional services, with back-up support available from a licensed clinician. Our goal is to build a bot that can provide basic training and guidance on the principles of behavior to reduce the likelihood of escalation of behavioral challenges using reinforcement-based strategies.\n<\/h1>\n<\/div><\/div>\"Step<\/div>\n<\/section>
\n \n \n\t\n\n\n\n

\nProject #3\n<\/h1>
\n \n \n\t\n\n\n\n

\nProject:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nStrategic Artificial Intelligence: Pilot Testing the Effectiveness of AI SEO, GEO and AI Media Assets \n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nFaculty PI:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nJoy Chavez Mapaye, Ph.D.
Professor of Journalism and Public Communications, College of Arts and Sciences at UAA
Certified Digital Marketing Professional
Adviser, UAA Public Relations Student Society of America (PRSSA)\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nCommunity
Partner:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nAnchorage Design Week
Fairness for Fairview
Fairview Community Council
Center for Safe Alaskans\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nGoals &
Outcomes:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nThis project aims to test the effectiveness of public health traffic safety campaign efforts through the use of artificial intelligence (AI) tools for search engine optimization (SEO) and generative engine optimization (GEO). Research on AI-generated media content will also be conducted through A\/B testing, a focus group and psycho-physiological measurements. Insights and implications from this project have the potential to shape public health evidence-based strategies and interventions (EBSIs), build public trust and create a healthier information environment in Alaska.\n<\/h1>\n<\/div><\/div>\"ANC\"Fairness\"Fairview\"Center<\/div>\n<\/section>
\n \n \n\t\n\n\n\n
\n \n \n \n <\/svg>\n <\/span>\n <\/div>\n

\nProject #4\n<\/h1>
\n \n \n\t\n\n\n\n

\nProject:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nARCTIC: AI for Rural Community Transformation and Infrastructure Coordination\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nFaculty PI:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nDr. Marie E. Lowe, Ph.D.
Associate Professor of Public Policy
Institute of Social and Economic Research
University of Alaska Anchorage\u00a0\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nCommunity
Partner:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nVillage of Alakanuk Tribe\n<\/h1>\n<\/div><\/div>
\n \n \n\t\n\n\n\n

\nGoals &
Outcomes:\n<\/h1>\n<\/div>
\n \n \n\t\n\n\n\n

\nRural and remote communities in Alaska are experiencing critical infrastructure challenges exacerbated by a changing environment. However, a significant gap remains in providing accessible, practical solutions to communities with limited resources and institutional capacity. A future research project will address this gap by creating an intelligent system that democratizes access to critical planning data, tools, and training. This mini-grant project will be used for scoping a preliminary design of a rural community planning chatbot and associated training program for Alaska public administrators. The goal of the mini-grant project will be to work with Alakanuk and project partners on reviewing planning documents and identifying community infrastructure needs; identifying local hardware\/software and internet connection capacity; conduct research on and outline goals for a community planning chatbot\u2019s core functions; research microlearning platforms; and contribute to an NSF proposal for building a prototype to test with communities.\n<\/h1>\n<\/div><\/div>\"Village<\/div>\n<\/section>