Behavioral Validation for Conversational AI

The behavioral layer of your human-facing AI needs to be proven,
not assumed.

Whether you are building an AI agent or evaluating one, the question is the same:
Does it actually change human behavior?

I provide the scientific methodology to answer it.



Research and industry partners

Hebrew University of Jerusalem Israel Center of Addiction and Mental Health Eli Lilly Meharry Medical College Novartis Health-QB Wolfson Medical Center Cambridge University Xoltar Amsterdam Brain & Cognition Mandel Leadership Institute Mayo Clinic Bayer Beilinson Medical Center #ReMarkable Beyond Verbal Communication Clalit Cornell Hadassah University Hospital Hebrew University of Jerusalem Israel Center of Addiction and Mental Health Eli Lilly Meharry Medical College Novartis Health-QB Wolfson Medical Center Cambridge University Xoltar Amsterdam Brain & Cognition Mandel Leadership Institute Mayo Clinic Bayer Beilinson Medical Center Beyond Verbal Communication Clalit Cornell Hadassah University Hospital
Hebrew University of Jerusalem Israel Center of Addiction and Mental Health Eli Lilly Meharry Medical College Novartis Health-QB Wolfson Medical Center Cambridge University Xoltar Amsterdam Brain & Cognition Mandel Leadership Institute Mayo Clinic Bayer Beilinson Medical Center #ReMarkable Beyond Verbal Communication Clalit Cornell Hadassah University Hospital

Who I Work With

Evaluating AI

Enterprise & Product Teams
Assessing vendor AI products

Vendors make behavioral claims. You need independent verification of whether those claims hold before you commit budget, integrate systems, or expose users.

  • Independent behavioral audit of vendor products
  • User characteristics and preferences mapping
  • Validation methodology your team can trust
  • Evidence-based evaluation framework for procurement
Building AI

Companies Building Conversational AI
Developing human-facing AI agents

You need to know your agent is behaviorally sound before it reaches users — and that it produces outcomes you can defend to partners, investors, and regulators.

  • Understand your user population before you build
  • Prompt adaptation for proper tone and language
  • Stress-test behavior across every probable scenario
  • Validate outcomes with peer-reviewed rigor

The Problem

Conversational AI fails at the behavioral layer.


You must measure it right.

The engineering stack gets tested rigorously. Security, hallucinations, failure contingencies, fringe behaviors — these have established frameworks and active solutions.

The behavioral layer does not.

Whether a patient, user, or employee trusts the agent, engages with it over time, and actually changes their behavior as a result — these outcomes are assumed, not validated.
That's where products fail, and where my work begins.

Evidence of Outcomes

RCT · Smoking Cessation · Xoltar

15% complete cessation vs. 0% in controls.
N=91. Sustained at 12 months.

Peer-reviewed RCT comparing a behaviorally designed AI agent against educational video and ChatGPT. Results submitted to Technology in Behavioral Research.

Full methodology
Chronic Pain · Hospital Network

Patient adherence: 31% → 91%.
One behavioral intervention. No new features.

Behavioral personalization of proactive messaging in a chronic pain clinic network. No clinical changes, only alignment of communication with how patients actually respond.

Full case study
Why Last-Mile

The last mile is where conversational AI succeeds or fails. Almost no one is working on it.

In short

A technically working agent that users abandon isn't a model problem — it's behavioral. Most teams solve for one of three layers (emotional calibration, trust, preference adaptation) and assume the other two will follow. They don't.

The Deployment Gap

There's a crisis in conversational AI.

Companies are shipping conversational agents that are technically impressive — large language models fine-tuned on domain data, integrated into their journeys, compliant with every regulation that matters. And then users don't use them. Or they use them once. Or they use them and nothing changes.

The technology worked, but the product failed.

This is the last-mile problem. And it has nothing to do with model performance.

The Pipeline The Industry Has Built

To be clear: the engineering problems in conversational AI are real, and the field has made serious progress on them.

  • Getting a capable LLM working is no longer the bottleneck it once was.
  • Security and data privacy frameworks exist and are maturing.
  • Hallucination rates are measurable and increasingly controllable.
  • Failure contingencies — what happens when the primary model is unavailable, returns a low-confidence output, or hits an edge case — are becoming standard architecture.
  • Fringe behaviors, the long tail of unexpected inputs that break a system in production, are increasingly anticipated and tested for.

None of this is trivial. All of it is necessary.

And none of it is sufficient.

Irrelevant
LLM Development

Core model training and weights.

Behavioral Science Needed
Problem & Population Definition

Crucial for mapping user characteristics, preferences and human behaviors.

Out of Scope
Regulatory & Data Privacy

Legal framework and encryption.

Out of Scope
Hallucination Reduction & RAG

Technical architectural grounding.

Behavioral Science Needed
Conversation Planning

Ensures the agent uses appropriate "Nudges" and choice architecture to drive adherence without triggering reactance.

Behavioral Science Needed
Simulations & Evaluation

Validating the agent against real-world human irrationality.

What The Known Pipeline Misses

  • A user who is ambivalent about change.
  • An agent that responds correctly but feels cold.
  • A conversation that covers all the right ground and leaves the user feeling unseen.
  • A dialogue structure inviting too many adverse event reports.
  • A support flow that resolves the ticket but leaves the customer angrier than before.

These aren't failure modes that show up in your QA suite.
They show up in your retention curve — weeks after launch, quietly, without a stack trace.

This is where the known pipeline ends.
And it ends precisely at the moment that matters most: the ongoing relationship between your agent and your user.

Why Conversational AI Broke The Old Playbook

Classical product design was a finite problem. You defined the screens, mapped the flows, anticipated the edge cases. Users chose from options you provided. QA meant checking every defined state.

Conversational AI has no defined states.

When a user can say anything — share a fear, express ambivalence, push back, go completely off-script — the interaction space becomes effectively infinite. The agent needs to respond to all of it: consistently, and in a way that reflects your brand and your intent.

You cannot engineer your way through that space. You have to map it behaviorally — identifying the scenarios that matter most, understanding how users are likely to think and feel in each one, and designing responses that move them in the right direction.

This is what behavioral science was built for.
It just took conversational AI to make it indispensable.

The Three Layers of The Last Mile

Most teams underestimate the last mile because they're solving for one layer when there are three.

01

Emotional Calibration

Does the agent respond appropriately to what the user is actually feeling — not just what they literally said? Does it know when to push and when to hold back? Can it handle ambivalence without losing the thread?

02

Trust Architecture

Does the user believe the agent is on their side? Understands what they need? Trust in conversational AI is fragile and non-linear — it builds slowly and collapses fast. The agent needs to be designed around how trust actually works, not assumed.

03

Preference & Style Adaptation

Users are not a monolith. Some want direct guidance. Others need to feel heard first. A well-designed agent doesn't just respond — it reads the person and adapts in real time.

Get all three right, and you have an agent that users return to.
Get any one wrong, and you have a product that works in demos and fails in the field.

A Note on Regulatory Compliance

Scope
Clarification

Regulatory compliance — HIPAA and SaMD classification in healthcare, PCI-DSS and SOX in finance, GDPR and CCPA nearly everywhere — is the responsibility of the product, its engineering team, and its legal counsel. These are distinct and well-defined disciplines with established frameworks, and the specific framework changes by vertical even though the pattern doesn't.

My work sits at a separate layer: measuring whether the behavioral intervention produces the outcomes it claims. Whether users engage, trust, adhere, and change behavior. These outcomes are currently unmeasured in most conversational AI deployments — not because the tools don't exist, but because they require a different kind of expertise to apply.

Both layers are required. Regulatory compliance without behavioral validation means a compliant product that doesn't work. Behavioral validation without compliance means outcomes that can't be deployed. The disciplines are complementary, not interchangeable.

HIPAA · Healthcare FDA / EMA · Healthcare PCI-DSS · Finance SOX · Finance GDPR · Global CCPA · Global

Why This Is A Behavioral Science Problem

Behavioral science isn't the study of how people should behave. It's the study of how they actually behave — under uncertainty, under stress, in the context of their own beliefs, habits, and ambivalence. That's exactly the population your AI agent is talking to.

The frameworks exist. Motivational Interviewing was designed precisely for the user who is ambivalent about change. CBT maps the cognitive patterns that keep people stuck. Behavior change theory tells you which lever to pull at which moment in a user's journey. The same underlying logic shows up outside clinical settings too — the Fogg Behavior Model, Cialdini's influence principles, and nudge theory are the same science applied to different contexts.

What's new is applying them at scale, in real time, through a conversational AI — and then validating that they actually work. Not assuming. Validating.

That's what I do.

Services

Three methodologies.
One question: does it work?

Each engagement is scoped as a defined project with clear deliverables. Whether you are building an agent or evaluating one, the methodology is the same: evidence-based, and designed to produce findings you can act on and defend.

My process is designed to be high-impact and low-overhead.
You provide the domain context and system access;
I provide the forensic evidence and the roadmap forward.

In short

Three scoped services, one behavioral question each: what will this population actually do. Projects run 2–12 weeks with a fixed deliverable — skim the summaries below, or expand any service for the full methodology.

01
User Intelligence

Before you build — or before you buy — you need to understand who the agent is actually serving.

For teams at the design stage, and for evaluators assessing whether a vendor understood their user population

Right for you if: You are pre-build, your agent was built without this foundation, or you need an independent baseline for evaluating a vendor's claims about user fit.

Most product teams work from personas that describe who their users are. User Intelligence goes further: it maps how they think, what drives and blocks behavior change in this specific population, what communication styles build trust, and what scenarios the agent is almost certain to encounter.

This is the foundation that makes everything downstream more accurate — conversation architecture, validation criteria, brand voice. Without it, the agent is designed for an imaginary user. For evaluators, this analysis also provides the benchmark against which a vendor's claimed user understanding can be tested.

What this produces

A behavioral profile of the target population — qualitative and quantitative — including early indicators, personality dimensions, motivational drivers, communication preferences, high-risk scenarios, and prompt design recommendations.

Also used for

Enterprise procurement and product teams evaluating whether a vendor's product was designed with adequate understanding of their specific user population — most often healthcare, finance, and other high-stakes verticals.

02
Behavioral Stress Testing

Your agent passed QA. QA tests defined states. It does not test what users actually bring to a conversation.

For teams pre-launch, and for evaluators auditing a vendor product before procurement

Right for you if: You have a working system approaching launch, or you are a buyer needing independent evidence of behavioral performance before committing.

Behavioral Stress Testing is a systematic audit of an agent against the full range of scenarios the real user population is likely to present — ambivalence, resistance, emotional distress, off-script disclosures, edge cases. It runs simulated conversations across hundreds of probable scenarios and identifies where the agent responds correctly but wrongly: technically sound, behaviorally damaging.

This is not a bug hunt. It is a behavioral fidelity assessment.

What this produces

A structured audit report mapping agent performance across scenario categories, with severity ratings, failure patterns, and prioritized recommendations. Suitable for internal remediation or for informing procurement decisions.

Also used for

Enterprise procurement teams requiring independent behavioral validation of a vendor's AI product before contract signature or deployment — especially in healthcare, finance, and other regulated industries.

03
Behavioral Forensics

Your agent is live.
Retention is declining.
Your team has theories.
You need evidence.

For teams with live systems underperforming, and for evaluators investigating a deployed vendor product

Right for you if: A live system is underperforming and you need to understand the behavioral mechanisms before deciding how to respond.

Behavioral Forensics is a methodological post-deployment investigation. Using conversation data, dropout patterns, and behavioral analysis, it identifies where the agent is losing users, what the underlying behavioral mechanisms are, and what changes will actually move outcomes — as opposed to what feels intuitive but won't.

This is not a UX audit. It is a behavioral diagnosis with a prioritized remediation roadmap.

What this produces

A forensic analysis including population simulations, failure taxonomy, user segment breakdowns, root cause hypotheses with evidence, and a prioritized remediation roadmap. Actionable by both internal product teams and vendor management.

Also used for

Enterprise procurement and product teams investigating why a deployed vendor product is not producing the outcomes that were contracted, and building an evidence-based case for remediation or contract review — a pattern seen most often in healthcare, finance, and other regulated industries.

Not Sure Where to Start?

Most engagements begin with a scoping conversation.

If you know something isn't working — or you need to know whether a vendor's product will work — that is a sufficient starting point.

Case Studies

The evidence, in detail.

Three projects across different domains, stages, and client types.
Methodology, sample sizes, and findings below.

Full paper will be submitted upon request.

In short

One method across all three: measure behavior change directly, not proxies like engagement or session length. Headline numbers are visible below for each case — expand any case for the full problem, methodology, and findings.

ClientXoltar Ltd.
Study DesignTwo-arm RCT
N91 (51 exp. / 40 control)
StagePlan-to-Product
Follow-up12 months
DomainAddiction

A behaviorally designed AI agent achieved complete smoking cessation outcomes a general-purpose AI could not replicate.

The Problem

Xoltar was developing a conversational AI agent for smoking cessation — a domain where digital interventions have a long history of strong early engagement and poor sustained outcomes. The clinical challenge was not producing an AI that could discuss quitting smoking. It was producing one that could support a patient through the full behavioral arc of cessation.

The commercial challenge was equally specific: the market was saturated with ChatGPT-based products making unsubstantiated therapeutic claims. Xoltar needed peer-reviewed evidence to differentiate credibly and secure pharmaceutical partnerships.

The Methodology

I designed the therapeutic architecture of the agent from the ground up, grounding it in Motivational Interviewing — a clinical framework developed for patients who are ambivalent about behavior change. The architecture comprised 11 goal-progression stages, maintaining natural conversational flow while ensuring therapeutic fidelity across the patient journey.

Prior to live deployment, I ran hundreds of simulated conversations to stress-test the agent against the full range of patient scenarios — resistance, relapse, ambivalence, off-script disclosures. Automated quality assurance systems monitored therapeutic fidelity at scale. The trial used a two-arm RCT design comparing the agent against an educational video control and a ChatGPT condition.

Two-arm RCT | N=91 (51 experimental, 40 control) | 12-month follow-up
Primary outcome: verified complete cessation
Secondary outcomes: MI adherence, conversation engagement
Submitted: Journal of Technology in Behavioral Science

The Results

15%
Complete cessation (experimental) vs. 0% in controls at 12 months
88%
MI adherence vs. 18% for ChatGPT condition
3×
Longer conversations vs. ChatGPT (14 min vs. 4 min)
3×
More interaction turns vs. ChatGPT (23 vs. 7)
  • Pharmaceutical partnerships secured on the basis of the evidence package
  • Results submitted to peer-reviewed journal (under review)
  • Outcomes sustained at 12-month follow-up
The Insight

Structure beats sophistication. A behaviorally grounded agent with a defined therapeutic architecture outperformed a more capable general model across every measured outcome — not despite its constraints, but because of them. Therapeutic rigor does not limit engagement. It produces it.

ClientHealth-Qb
DomainChronic Pain
ServiceBehavioral Forensics + Intervention
StageLive System

Homework adherence: 31% to 91% | 33% increase in clinical outcomes.

The Problem

A network of chronic pain clinics had deployed a proactive chatbot to encourage patients to complete prescribed home therapeutic exercises. Homework adherence, one of the strongest predictors of treatment outcomes in chronic pain, was at 31%. The technology was functioning correctly. The behavioral outcomes were not.

The Methodology

I conducted a behavioral analysis of the patient population and the existing messaging architecture, identifying the gap between how the chatbot was communicating and how different patient segments actually respond to health prompts. The intervention was targeted: personalizing the proactive messaging at the end of each session and between sessions, to match individual patient communication styles, motivational profiles, and behavioral patterns — rather than broadcasting the same message to all patients regardless of profile.

The Results

+33%
Relief in Pain Symptoms
91%
Adherence following behavioral personalization
  • Intervention: behavioral personalization of messaging only
  • No new features, no interface changes, no additional engineering
  • Improvement driven entirely by communication alignment
The Insight

Patients do not fail to adhere because they don't care. They fail because the intervention was not designed for them specifically. When messaging matches how a person actually responds to encouragement, challenge, or accountability — the behavior follows.

ClientWolfson Medical Center
DomainOncology
ServiceBehavioral Assessment & Intervention
PublishedPeer-Reviewed Clinical Study

Patients weren't just tolerating the AI.
They were nodding at it, smiling at it, and trusting it with their pain scores.

The Problem

An oncology department treating ovarian and endometrial cancer patients needed a way to monitor chemotherapy side effects between clinical visits — without adding burden to an already stretched medical team. The gap between sessions is precisely where symptoms escalate undetected, trust erodes, and outcomes suffer.

The Methodology

I defined the behavioral roadmap and conducted a behavioral assessment and intervention with an AI avatar delivering video-based conversational check-ins to patients post-chemotherapy. The evaluation focused on how the agent handled the emotional and relational demands specific to oncology: fear, physical distress, and the need to feel genuinely heard. Behavioral calibration ensured the avatar could sustain patient engagement across 83 sessions — and that patients perceived it not as a form to fill out, but as a presence worth talking to.

The Results

1,187
Communication gestures recorded — nods, smiles, and agreements directed at the avatar
14
Grade 3–4 symptom events caught and escalated to the medical team in time
2 weeks
Early detection of treatable symptoms
  • 83 sessions completed across 7 patients, averaging under 5 minutes each — high compliance, low friction
  • Every severe symptom event was detected and relayed; no critical signals were missed
  • Patients exhibited social engagement behaviors toward the avatar, indicating perceived relational presence
  • No changes to clinical protocols — behavioral calibration alone drove the outcome
The Insight

Over 1,000 nonverbal gestures directed at an AI is not a UX metric. It is a signal about what happens when an agent is behaviorally calibrated for its context. Patients in chemotherapy don't engage with tools — they engage with presences. When the avatar behaved in ways that felt attuned to their emotional state, they responded as they would to a person: with nods, with smiles, with disclosure. That disclosure is what made the clinical monitoring possible. The behavior was the infrastructure.

Need similar evidence for your product or evaluation?

Every engagement starts with understanding the specific population, context, and behavioral claim being made or tested.

About

I make conversational AI account for how humans behave.

In short

PhD in Psychology (Hebrew University of Jerusalem), eight years in startup founding teams, former Chief Behavioral Research Officer at Xoltar. Now an independent consultant — peer-reviewed methodology, applied to real products.

My name is Alon Goldstein. I hold a PhD in Psychology from the Hebrew University of Jerusalem, where I studied high-level non-conscious cognitive processes — the mental machinery that drives behavior below the level of awareness. That research turned out to be unusually good preparation for working with conversational AI, which needs to account for what people do rather than what they say they will do.

I have spent eight years in startups' founding teams at the commercial intersection of behavioral science and AI.

In my last role, as Chief Behavioral Research Officer at Xoltar, I designed and validated a conversational AI agent for smoking cessation that achieved peer-reviewed clinical outcomes in a controlled trial. Since then, I am an independent consultant working with companies building conversational AI, and the organizations evaluating and deploying it — including tech and healthcare companies, clinics, customer support, and call centers.

My work covers the full arc of the behavioral problem in conversational AI: from theoretical planning and literature review through modeling user populations and mapping characteristics to likely behaviors; understanding what actually drives behavior change through conversation; designing experiments that produce defensible, peer-reviewable evidence; prompt engineering and hands-on data science; measuring outcomes through behavioral change and validated self-reports rather than retention metrics; diagnosing why live systems underperform; and delivering findings through reports and stakeholder presentations that bridge domain experts and product teams to actionable insight.

The through-line is rigorous behavioral science applied to real products, validated against real outcomes — not frameworks that sound credible in a slide deck and dissolve on contact with real users.

I am based in Tel Aviv.
I work with organizations internationally.

Let's connect Hebrew University LogoHebrew University Recognition:
40 Under 40 Leaders Q Behavioral Thinking

Selected Publications

  • Goldstein, A., Sklar, A. Y., Hershkovitz, O. & Goldstein, A. (under review). Evaluating the Effectiveness of AI-Based Virtual Agents in Health and Wellness Behavioral Change. Journal of Technology in Behavioral Science.
  • Goldstein A., Havin, M., Reichart, R. & Goldstein, A. (2023). Decoding Stumpers: Large Language Models vs. Human Problem-Solvers. Findings of the Association for Computational Linguistics: EMNLP 2023, 11644–11653.
  • Goldstein, A., & Young, B. D. (2022). The unconscious mind. In Mind, Cognition, and Neuroscience (pp. 344–363). Routledge.
  • Kardosh, R., Sklar, A. Y., Goldstein, A., Pertzov, Y., & Hassin, R. R. (2022). Minority salience and the overestimation of individuals from minority groups in perception and memory. Proceedings of the National Academy of Sciences, 119(12), e2116884119.
  • Goldstein, A., Rivlin, I., Goldstein, A., Pertzov, Y., & Hassin, R. R. (2020). Predictions from masked motion with and without obstacles. PloS One, 15(11), e0239839.
Blog

The Behavioral Layer

Where conversational AI actually succeeds or fails. Writing on behavioral science, human behavior, and what it takes to build AI agents that produce real outcomes.

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    Alon Goldstein, PhD
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    About the author

    Alon Goldstein, PhD is a human-machine relationships expert, focused on designing, validating, and diagnosing the behavioral layer of conversational AI products and on human-machine relationships.

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    I work with organizations that have a specific behavioral question to answer — whether that is understanding a user population before building, stress-testing an agent before launch, diagnosing why a live system is underperforming, or independently evaluating a vendor's behavioral claims before procurement.

    If the question is whether a conversational AI product actually changes human behavior — and whether you can prove it — that is the right starting point for a conversation.

    Engagements are scoped to the specific question, population, and product stage — a pre-launch behavioral audit for an early-stage startup is a different undertaking than an independent evaluation for an enterprise procurement team. Projects typically run 2–12 weeks. Pricing follows a scoping conversation, once the actual question is clear.

    I respond to every inquiry within 48 hours.
    If we're not a fit, I'll tell you directly.

    If you are looking for general AI development, technical implementation, regulatory compliance consulting, or someone to validate a decision that has already been made, I am not the right fit.

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