grant

AI HazMat Specialist for Supplemental Learning

Organization SPECTRAL LABS, INC.Location SAN DIEGO, UNITED STATESPosted 19 Jul 2025Deadline 30 Jun 2026
NIHUS FederalResearch GrantFY2025AI AugmentedAI assistedAI based modelAI botAI drivenAI enhancedAI integratedAI modelAI model trainingAI poweredAI systemAI trainingAccidentsArtificial IntelligenceArtificial Intelligence enhancedArtificial intelligence model trainingAugmented by AIAugmented by the AIAugmented with AIAugmented with the AIChatGPTChemicalsComputer ReasoningComputer softwareDataDecision MakingDevelopmentDocumentationE-learningEmergenciesEmergency SituationEmergency responseEnsureEventFirst AidGPT-2GPT-3GPT-4GoalsHAZMATHAZMAT disasterHAZMAT emergencyHAZMAT workerHAZWOPERHazardous MaterialsHazardous SubstancesHazardous Waste Operations and Emergency ResponseHuman ResourcesHydrogen OxideIndustryKnowledgeLearningLifeMachine IntelligenceManpowerNational Institutes of HealthOn-Line SystemsOnline SystemsOutcomePhaseProceduresPropertyProtocolProtocols documentationResearchResearch ResourcesResourcesRunningSBIRSafetySmall Business Innovation ResearchSmall Business Innovation Research GrantSoftwareSourceSpecialistStudentsTestingTimeTrainingTraining ProgramsTrustUnited States National Institutes of HealthUpdateVeteransWaterWorkartificial intelligence assistedartificial intelligence augmentedartificial intelligence drivenartificial intelligence integratedartificial intelligence modelartificial intelligence poweredartificial intelligence trainingartificial intelligence-based modelchat botchatbotcomputer-assisted instructioncomputer-based educationcomputer-based instructioncomputer-based learningcomputer-based trainingconversational AIconversational agentconversational botdata to traindataset to traindevelopmentaldigital educationdigital learningeLearningelectronic learningemergency personnelemergency responderemergency service personnelemergency service responderenhanced with AIenhanced with Artificial Intelligenceexperiencefootguidebookshaz mathaz mat disasterhaz mat emergencyhaz mat workerhazardhazardous materials disasterhazardous materials emergencyhazardous materials workerhazardous substances disasterhazardous substances emergencyhazardous substances workerimprovedindependent learninginstructorinternet-assisted educationinternet-based trainingmultimedia learningon-line educationon-line learningonline computeronline educationonline learningoperationoperationspersonal protection equipmentpersonal protective equipmentpersonnelprogramsresponseself-directed learningself-regulated learningskillssupplemental instructionsupplemental learningtechnology-enhanced learningtooltraining datatutorial supporttutoringvirtualvirtual learningweb basedweb-accessibleweb-based instructionweb-based training
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Full Description

Project Summary/Abstract
It can be difficult to master all the concepts related to hazardous materials training

independently, even while having access to the Emergency Response Guidebook (ERG) and

NIOSH Pocket Guide to Chemical Hazards (NPG). Classes cover general knowledge but

sometimes it would be better to ask a HazMat specialist what their course of action would be in

specific scenarios. It takes years of experience to be able to think on your feet in an emergency

situation, and personnel new to the field may not be confident or quick in their decision making

in a time of crisis.

This work aims to create an Artificial Intelligence (AI) model to become an unparalleled

HazMat specialist. This AI model will be trained extensively on the ERG, NPG, HAZWOPER

lesson documentation, and many other trusted HazMat resources, and then further refined with

direct information from HazMat industry veterans. Users will be able to ask HazMat questions

and receive detailed responses to promote the safety of HazMat workers and protection of life

and property. The AI will supplement any HazMat training by providing an extra resource to ask

questions and find accurate information on actions necessary regarding a hazardous materials

emergency. Responses will be focused on the standards set forth by 29 CFR 1910.120.

Grant Number: 1R43ES037505-01
NIH Institute/Center: NIH

Principal Investigator: Carl Bautista

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