USCIS’s AI Screening vs. EB1A Experts’ Turing AI: Two Very Different Uses of AI in Your Case

The USCIS AI Screening vs EB1A Experts AI is important since AI in immigration 2026 will now impact your case in two different ways, and the Turing AI EB1A Evaluation comes much earlier than an officer ever looks at your case. Imagine airport scanning machine and a travel planner. They both rely on software programs, but they perform entirely different functions. 

At a glance: USCIS employs AI for sorting evidence, detecting fraud, and aiding security clearance, although the decision of eligibility remains with the officers. The Turing AI is employed prior to filing in order to create a roadmap of the applicant’s background relative to the EB-1A requirements and to determine deficiencies in evidence.

Read More: USCIS’s Vetting Center: What EB-1A Filers Are Reporting Since the AI Screening Rollout

USCIS AI Fraud Detection: What Does the Screening Do?

The publicly documented AI tools used by USCIS can be found in the DHS AI Use Case Inventory for USCIS. There are multiple instances in which the agency uses AI technology, and none of them have been labeled as making eligibility decisions. The three relevant entries are:

• ELIS Evidence Classifier: A machine learning tool used to tag and classify evidence documents so that the adjudicators can easily find the important documents.
• Text Analytics Sentence Similarity Model: An analysis of narratives in applications to find any similarities that might indicate fraud or security concerns; it is currently listed for humanitarian programs and not employment-based ones.
• FDNS-DS NexGen Case Processing: A pre-deployment machine learning use case for fraud detection and case prioritization investigations.

Since January 2025, there have been multiple updates to the DHS AI Use Case Inventory. Make sure to check the updated version of the inventory.

Equally noteworthy is the marking of the items as retired. One item marked as retired is I-539 Approval Prediction, and another one is Topic Modeling on RFE Data. Also marked as retired is Asylum Text Analytics, which was used for detecting frauds based on plagiarism in asylum applications. There is no item mentioned as predicting approvals.

The Vetting Center Adds a Security Layer

The USCIS made an announcement about a new Vetting Center based in Atlanta on December 5, 2025. As per the announcement, the center conducts vetting of both pending and approved applications by utilizing screening databases, “including law enforcement and intelligence” of DHS along with state-of-the-art technologies including “artificial intelligence.”

In our previous blog post dated September 18, we had described the process of screening, which is conducted using federal screening databases, such as HART, IDENT, TECS, ATS, and State Department databases based on the practitioner’s observation. Reports of increased delays and new pattern of RFEs after the announcement are largely based on practitioner observations and not published USCIS statistics.

What the AI Does Not Do?

The officers continue to employ the EB-1A standard under the law. The applicant has to demonstrate either an extraordinary one-time achievement or that at least three of the ten criteria set out in the regulation have been satisfied, and even fulfilling the requirement does not automatically imply eligibility. Thereafter, USCIS conducts the final merits assessment to determine whether there is sustained national or international recognition of the record as a whole.

There has been a fall in the approval rates, although the cause is disputed. According to Greenberg Traurig’s analysis of the USCIS data on the EB-1A, published in the National Law Review, the EB-1A has an approval rate of 66.9% for the fiscal year 2025 and approximately 53% in the fourth quarter, where the decline is attributed to the increased scrutiny of the evidence as opposed to automation despite USCIS having not singled out any single factor as the cause.

Timeline is important in this case since the fourth quarter of FY2025 for USCIS was between July to September 2025, which means that the Q4 drop happened prior to the introduction of Vetting Center in December 2025. With regard to the EB-2 NIW case, the approvals in FY2025 were reported at 55.2%, with 35.7% in the fourth quarter.

Read More: https://eb1aexperts.com/eb1a-final-merits-determination-2026-impact-guide-mukherji-miller 

Turing AI Case Evaluation: What Does It Do Differently?

The Turing AI by EB1A Experts is the evaluation tool used by the firm to evaluate the credentials of an applicant prior to filing a petition. Unlike the system by USCIS which evaluates what has been filed, Turing AI works during the planning stage where additional evidence can still be collected.

What Turing AI Evaluates?

In accordance with the company’s explanation of their approach to technology, there are four major capabilities:

• Criteria Mapping – links the achievements to the EB-1A requirements, or O-1A and EB-2 NIW requirements as applicable, to identify the strengths and weaknesses of the record relative to the criteria.
• Comparison – creates comparative benchmarks in the form of percentile citations, salary comparisons, and award selectivity to compare the applicant’s achievement to others in the field.
• Criteria gap identification – identifies the criteria where more documentation could bolster the claim.
• Narrative Development support – organizes the evidence in a framework that can be used to create a strategy for the petition by the strategists and the attorneys.

It is claimed by the firm that this method could reduce the time necessary for the first assessment from weeks to days. This is true only for the assessment process, but not for USCIS processing times, which are out of reach of any private firm.

Where Humans Take Over?

Turing AI does not petition nor does it give legal advice. “AI helps with evidence preparation and story development, whereas attorneys manage the legal requirements, case laws, and filings,” states EB1A Experts. An AI evaluation may determine whether an application meets the criteria or not, but it cannot guarantee how any specific immigration officer will view the application.

AI Vetting Center vs Case-Prep AI: Why Does It Matter?

The easiest way to make sense of the distinction would be to consider the party which the mechanisms benefit and the time at which they operate. The USCIS tools benefit the government post-petition. The Turing AI benefits the applicant pre-filing.

	USCIS AI screening	EB1A Experts’ Turing AI
Used by	USCIS personnel	EB1A Experts strategists
When it runs	After filing, during processing and vetting	Before filing, during evaluation and preparation
What it analyzes	Submitted documents and screening data	The applicant’s achievements, evidence, and career record
Core function	Organize evidence, flag fraud or security concerns	Map evidence to criteria, identify gaps
Who decides	USCIS officers	The applicant, with attorney counsel
Key limit	Does not determine eligibility on its own	Cannot guarantee approval

Two Moments in One Case

If plotted on a timeline, both processes never intersect. One affects each step of the same petition in a completely separate manner:

1. Prior to submission: Turing AI analyzes the record, maps the evidence to the regulatory criteria, and pinpoints the gaps that need to be addressed.
2. At submission: the submitted exhibits are processed in USCIS systems; tools like the Evidence Classifier categorize the documents for the officer.
3. During processing: fraud detection tools and, where relevant, checks from the Vetting Center are used in parallel with the officer’s analysis.
4. At decision: the officer applies the regulatory criteria and the final merits decision to the record.

The only phase that is under the control of the applicant is the first one. The rest takes place after the record has been submitted.

Why This Changes How You Prepare?

As technology verifies and cross-verifies your input, even slight discrepancies may receive greater scrutiny. According to practitioners, there are three common factors which cause problems for applicants. These include improperly categorized evidence, discrepancies among information such as job titles or dates from various papers, and scanned PDF files where text in the invisible layer does not correlate with the text on the page itself. USCIS has not released statistics regarding their categorization process, so consider them patterns but not necessarily mechanics.

The bar was raised again in August 2026 when USCIS policy under Policy Alert PA-2026-05 granted the officer authority to deny the benefit application for lack of sufficient evidence at the outset without having sent out an RFE or NOID, according to AILA. A petition which relies on the second submission becomes less certain to be approved.

Generic AI Drafting Is a Separate Risk

Neither of the two systems mentioned is equivalent to copying the resume in a generic bot and getting the bot to draft a petition. The USCIS’s sentence similarity technology is listed in humanitarian programs at the moment, while its now defunct plagiarism detection system was intended for use in asylum applications, but there can be no doubt that the agency does possess some means of text-matching. Therefore, using generic text to describe real accomplishments is a very bad choice.

Read More: https://eb1aexperts.com/eb1a-in-2026-why-strong-cases-fail-without-differentiation 

How Should You Think About AI in Immigration 2026?

Consider these two perspectives as separate questions with the same solution – verifiable evidence. The first side favors consistency and documentation. Preparation requires building all of those.

1. Count on cross-verification of all documents. Ensure that names, titles, dates, and numbers are consistent in each exhibit.
2. Organize exhibits effectively. This way, you will allow evidence to be placed under proper criteria.
3. Evaluate scanned documents. Check if the OCR text layer in the scanned PDF is consistent with actual text on the page.
4. Identify weaknesses early. Evaluation of materials allows you to fix possible issues in order to document achievements appropriately.
5. Make statements verifiable. Publications, citations, press coverage, and salary information have more impact than self-description.
6. Have your material reviewed by lawyers prior to submission. AI, including Turing AI, helps with preparation but cannot substitute for attorney’s evaluation.

Nowadays, AI is present on both sides of the EB-1A process, but they have different tasks. One tries to detect if anything in your file requires attention, while the other helps you to prepare your file for submission.

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