Confirmation
1
Document Type
Paper
Location
ONU McIntosh Center; Ballroom
Start Date
21-4-2026 5:15 PM
End Date
21-4-2026 5:30 PM
Abstract
Artificial intelligence chatbots increasingly provide legal information to consumers, but AI "hallucinations" (confidently stated but incorrect responses) pose serious risks in immigration law. Incorrect information about USCIS forms, fees, processing times, or filing procedures can result in visa denials, deportation proceedings, or permanent bars to entry.
This research presents a novel "source-grounded AI" system that eliminates hallucinations in immigration legal information. Rather than relying solely on large language models (LLMs) trained on general internet data, the system uses USCIS.gov as the primary source of truth for all operational data including current forms, fees, processing times, filing addresses, and policy updates. The LLM provides natural language understanding and conversational interface, while a real-time USCIS data layer ensures factual accuracy.
Testing demonstrates 100% accuracy in responses to USCIS procedural questions, with every response including source citations linking to official USCIS.gov pages. This contrasts sharply with general-purpose AI chatbots which produce hallucinated or outdated immigration information in approximately 35% of queries.
This approach addresses the access to justice crisis in immigration law, where 87% of cases lack legal representation, by providing reliable, free legal information without crossing into unauthorized practice of law. The system serves as a replicable model for AI-assisted legal services in other practice areas where authoritative government sources exist.
Recommended Citation
Igwe, Hephzibah, "USCIS-Grounded AI: Preventing Hallucinations in Immigration Legal Services" (2026). ONU Student Research Colloquium. 35.
https://digitalcommons.onu.edu/student_research_colloquium/2026/Papers/35
Open Access
Available to all.
Included in
Computer Engineering Commons, Immigration Law Commons, Legal Ethics and Professional Responsibility Commons, Science and Technology Law Commons
USCIS-Grounded AI: Preventing Hallucinations in Immigration Legal Services
ONU McIntosh Center; Ballroom
Artificial intelligence chatbots increasingly provide legal information to consumers, but AI "hallucinations" (confidently stated but incorrect responses) pose serious risks in immigration law. Incorrect information about USCIS forms, fees, processing times, or filing procedures can result in visa denials, deportation proceedings, or permanent bars to entry.
This research presents a novel "source-grounded AI" system that eliminates hallucinations in immigration legal information. Rather than relying solely on large language models (LLMs) trained on general internet data, the system uses USCIS.gov as the primary source of truth for all operational data including current forms, fees, processing times, filing addresses, and policy updates. The LLM provides natural language understanding and conversational interface, while a real-time USCIS data layer ensures factual accuracy.
Testing demonstrates 100% accuracy in responses to USCIS procedural questions, with every response including source citations linking to official USCIS.gov pages. This contrasts sharply with general-purpose AI chatbots which produce hallucinated or outdated immigration information in approximately 35% of queries.
This approach addresses the access to justice crisis in immigration law, where 87% of cases lack legal representation, by providing reliable, free legal information without crossing into unauthorized practice of law. The system serves as a replicable model for AI-assisted legal services in other practice areas where authoritative government sources exist.