Featured project · Luddy LINK · Feb 2025 to present

Digital Humans

Project Lead for Digital Humans work at Luddy LINK, which places students on applied technology projects at the Luddy School of Informatics, Computing, and Engineering at Indiana University (IU). The focus is AI-driven virtual personas in business workflows, not avatar demos for their own sake. When an AI system looks and talks like a person, what makes people trust it, use it, and keep using it? Handshake content creator work is the live-persona half of that question: a public profile has to feel like a real person talking, or people bounce. Digital Humans is the same craft when the persona is built rather than performed. The consent layer sits in Plain Disclosure (cookies first, then the person-shaped interface). The influencer narrative lives on IU Pages.

status in progress role Project Lead org Luddy LINK dates Feb 2025 to present

01 · Project Overview

Project Overview

Problem statement

Organisations are putting person-like AI into customer experience (CX) and employee workflows. Looking human is not the same as earning trust. Disclosure, expectations, and oversight decide whether people keep using a Digital Human ... or leave after one awkward turn. My lead work stays business-facing: useful interfaces, not spectacle.

Dates
February 2025 to present.
Team / role
Project Lead at Luddy LINK. Team size and lab internals stay high-level on this public page.
What the work is for
Figure out how AI-driven virtual personas can fit real service and staff workflows, with trust, disclosure, and human oversight kept explicit.
My part
Own the framing, the research questions, the coordination rhythm, and how the work is explained outside the lab. No invented publications. No clinical framing.

02 · Field

What Digital Humans are (and are not)

This section is field context. It is not a claim that Luddy LINK has measured these effects.

Not a chatbot. Not a recorded avatar.

In industry language, a Digital Human is an interactive, AI-powered virtual person you can talk to in real time ... with a face, a voice, a body, and a personality (UneeQ, 2026). Conversation is usually driven by a large language model (LLM) grounded in an organisation’s own data, plus animation for expression and lip-sync. A text chatbot answers in a pane. A pre-recorded avatar plays a script. A Digital Human is meant to sit in between: a person-shaped interface at scale.

Information-systems research uses a similar cut. Digital Humans are “AI-driven agents with a high degree of humanness” used in service settings (Mücksch et al., 2026). Vendors also pitch them for CX (customer experience) and for staff work such as training and internal support (Soul Machines, 2025). That is the market. It is not a ship list for this project.

Trust is easier to spend than to keep

Looking more human can raise first trust. It also raises the cost of a mistake. In an online experiment, people trusted an error-free Digital Human more than a point-and-click interface ... and trusted it less, once it failed, than they trusted the same error on the simpler interface (Mücksch et al., 2026). Anthropomorphism and transparency can each help on their own. Stacking both is not automatically better: in a 2025 experiment (N = 490), combining human-like cues with transparency sometimes reduced trust (Bliss and Flatten, 2025). Disclosure has to be intelligible, not decorative.

What the HCI literature already shows

These are field papers. They are not Luddy LINK results.

The classic warning is still the Uncanny Valley: as something approaches a lifelike human appearance, affinity can drop into eeriness, and movement makes the drop steeper (Mori, 1970; authorised English translation, IEEE Spectrum / IEEE Robotics & Automation Magazine, 2012). A face that almost works is worse than a face that does not pretend.

Voice on top of text can raise both anthropomorphism and judged accuracy. In a CHI 2024 extended abstract (n = 2,165; published 11 May 2024), a speech-plus-text interface was rated more human-like, and more accurate, than text only. Using a first-person “I” rather than “the system” raised accuracy ratings and lowered risk ratings in one context (Cohn et al., CHI EA 2024). Looking and sounding like a person is a trust cue. It is also a way to spend trust.

Disclosure is not one effect. In a Marketing Science field experiment with about 6,000 financial-services customers, telling people they were talking to an AI chatbot before the conversation cut purchase rates by more than 79.7% (Luo et al., 2019; INFORMS write-up). A later open-access customer-service study (interviews plus an experiment, n = 194) did not find that same disclosure penalty (van der Goot, Koubayová, and van Reijmersdal, 2024). Sales calls and help-desk chats are different jobs. The lesson I keep is timing and context, not a slogan that disclosure always hurts.

Three published facts that earn a place here

Industry figures below are published sources, not personal metrics and not lab results.

  1. 72% of organisations reported using AI in at least one business function in McKinsey’s early 2024 Global Survey on AI (fielded 22 February to 5 March 2024; 1,363 respondents). The same survey put regular generative-AI use in at least one function at 65%. Keep those apart: 72% is AI use, not a 2025 gen-AI figure (McKinsey, 2024).
  2. MIT NANDA’s July 2025 report found that only about 5% of custom enterprise AI tools reach production. Wide trial is not the same as a system staff can keep (MIT NANDA, 2025; THE Journal write-up).
  3. 51% of respondents from organisations using AI said their organisation had seen at least one negative consequence in the prior year, with inaccuracy the most common (McKinsey, 2025). A January 2026 industry roundup repeats the same 51% figure (Master of Code, 2026).

Supporting boards live under Research: research stats and Plain Disclosure. They are tools, not a second brand.

What has to sit under the face

A Digital Human is a stack, not a single product. Public-safe layers, without a rate card:

  1. Avatar / animation. The face, body, and lip-sync. This is what demos sell first.
  2. Voice. Text-to-speech (TTS), and whose voice rights sit behind it.
  3. Language. A large language model (LLM), often with retrieval-augmented generation (RAG) against an organisation’s own documents.
  4. Streaming. Real-time audio and video (often WebRTC / LiveKit-class infrastructure) so the turn feels live.
  5. Systems of record. CRM (customer relationship management) write-back, tickets, and a path to a human when the persona should stop talking.
  6. Disclosure. People have to know they are talking to a system. In the EU, Article 50 of the AI Act makes that a legal obligation for many interactive systems from 2 August 2026 (EUR-Lex, Regulation (EU) 2024/1689; consolidated text, 27 July 2026; European Commission guidelines, last update 6 August 2026). Article 50(1) requires that AI systems intended to interact directly with natural persons are designed so those people are informed they are interacting with an AI system. A narrower transition applies only to Article 50(2) machine-readable marking: providers of synthetic-content systems already on the market before 2 August 2026 have until 2 December 2026 (consolidated Article 113(4), amendment M1).

If any of those layers is missing, you still have a demo. You do not have a business interface.

Vendor classes (not a shopping list)

The market splits into families. Naming a class is enough on this page. Prices and session math stay off it.

Named CX deployments (vendor-published)

These are named organisations on vendor pages I opened. The numbers are the vendor’s. They are not my measurements.

What is hard in practice

Practitioner threads and vendor docs keep repeating the same unglamorous problems. They are not my lab results.

03 · Organisation / Context

Organisation / Context

Organisation: Luddy LINK at IU Luddy, Bloomington. This is campus lead work on Digital Humans. It is not a client build and not Serve IT.

1. What the organisation needs

Clear thinking about person-like AI in business settings. When does disclosure help? When does looking human mislead? What does “useful” mean for people who have to live with the system after the demo?

2. The challenge

Organisational AI use is now common. Production-grade custom tools are still rare. The hard part is adoption with accountability, not another interface that only looks friendly. The three published facts sit in What they are. The filterable board is under Research.

3. Process now underway

Frame the questions. Map workflow touchpoints. Read the trust and disclosure literature at a working level. Keep public claims honest. Detail in Process / Approach.

4. Intended outcome (honest scope)

In progress. A lead-owned case for Digital Humans as business interfaces. Process is visible here. Artifacts land when they are public-safe. This page does not claim peer-reviewed findings, product launches, or lab metrics.

04 · Process / Approach

Process / Approach

Lead work in progress. I will not invent study outcomes. The process itself is what outsiders can evaluate right now.

Research framing

Trust, disclosure, adoption, oversight, privacy, usability. Keep the business question concrete: customer journeys and employee workflows, not sci-fi personas.

Planned · Figure P1

Research-question one-pager ... public-safe excerpt forthcoming.

Context mapping

Map where a Digital Human would sit in a service path: intake, escalation to a human, and what disclosure looks like at each step.

HCI and trust lens

Human-computer interaction (HCI) already has working evidence I can use without pretending it is this project’s result. Person-like agents can earn more trust than a plain control ... and lose more of it when they err (Mücksch et al., 2026). Anthropomorphism and transparency are not a package deal (Bliss and Flatten, 2025). Voice on a text channel can raise judged accuracy as well as human-likeness (Cohn et al., 2024). I separate what the field already knows from what this project has measured. This page only claims the former.

Coordination

Keep scope clear. Keep language precise for campus and hire audiences. Refuse overclaim on unpublished results.

What should land next

Framing memo, workflow map, and a trust / disclosure checklist. Planned slots below until those are public-safe.

05 · Professional and Technical Skills Used

Professional and Technical Skills Used

Domain

Technical stack (supporting)

Leadership and communication

Documentation

06 · Deliverables / Artifacts

Deliverables / Artifacts

What is public stays honest. No private lab data, no clinical framing, no invented screenshots of unpublished systems.

Shipped · research stats board

Filterable board of published industry figures. Open research stats.

Figure 0. Research stats board · shipped artifact

Figure 1 · Planned

Digital Humans problem / research-question one-pager ... forthcoming when public-safe.

Figure 1. Framing memo · planned

Figure 2 · Planned

Business workflow touchpoint map (customer or employee path) · planned.

Figure 2. Workflow map · planned

Figure 3 · Planned

Trust / disclosure checklist for person-like AI interfaces · planned.

Figure 3. Trust and disclosure checklist · planned

Figure 4 · Planned

Prototype or demo stills ... only if and when they are cleared for public portfolio use.

Figure 4. Prototype visuals · planned

Before / after

N/A for now. Lead / research track. No before-after product ship claimed here.

Testimonials

No quote published yet.

Related shipped proof

Code ships on jadexzhao. Clinic practice: Serve IT · Serve-AI. Industry figures sit under Research.

07 · Sources

Sources used on this page

Public, year-stamped sources. Market figures are not Jade’s results. HCI, Art. 50, D-ID, and practitioner links below were opened on 16 September 2026.

  1. McKinsey (2024). The state of AI in early 2024. 72% of organisations reported using AI in at least one business function. Same survey: 65% reported regular generative-AI use in at least one function. Fielded 22 February to 5 March 2024; 1,363 respondents. Kept from the 2024 Global Survey (72% is AI use in at least one function, not a generative-AI figure). mckinsey.com
  2. Challapally, A., Pease, C., Raskar, R., and Chari, P. (July 2025). The GenAI Divide: State of AI in Business 2025. MIT NANDA. PDF: mlq.ai copy. News write-up: THE Journal, 28 August 2025.
  3. McKinsey (2025). The state of AI: How organizations are rewiring to capture value / later 2025 Global Survey on AI. 51% of respondents from organisations using AI reported at least one negative consequence. mckinsey.com
  4. Tsymbal, T. (updated 27 July 2026). 350+ Generative AI Statistics. Master of Code. Repeats the 51% negative-consequence figure as an industry roundup. masterofcode.com
  5. Hattersley, M. (9 June 2026). What are digital humans? UneeQ. Industry definition: interactive AI-powered virtual person versus chatbot or recorded avatar. digitalhumans.com
  6. Soul Machines (2 September 2025). Digital Workforce announcement: human-like interfaces for enterprise CX and internal work. Business Wire
  7. Mücksch, J., Papen, M.-C., Lichtenberg, S., Brendel, A. B., Bellger, M., and Siems, F. (2026). Trust Me If You Can: The Role of Competence, Benevolence, and Integrity When Digital Humans Fail. ECIS 2026 Proceedings. aisel.aisnet.org
  8. Bliss, G., and Flatten, T. (2025). Trusting Generative AI-based Conversational Agents: The Role of Anthropomorphism and Transparency as Trust Signals. ICIS 2025 Proceedings. aisel.aisnet.org
  9. Cohn, M., Pushkarna, M., Olanubi, G. O., Moran, J. M., Padgett, D., Mengesha, Z., and Heldreth, C. (11 May 2024). Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models. CHI EA ’24, Article 54, pages 1 to 15. n = 2,165. Speech plus text raised anthropomorphism and judged accuracy; first-person “I” raised accuracy and lowered risk in one context. doi.org/10.1145/3613905.3650818
  10. Regulation (EU) 2024/1689, Article 50. Transparency obligations for providers and deployers of certain AI systems, including systems intended to interact directly with natural persons. Official Journal text applies from 2 August 2026 (Article 113). Official text: EUR-Lex 32024R1689. Consolidated text (27 July 2026): providers of synthetic-content systems on the market before 2 August 2026 have until 2 December 2026 for Article 50(2) (CELEX 02024R1689-20260727). Commission guidelines, last update 6 August 2026: digital-strategy.ec.europa.eu.
  11. D-ID (opened 16 September 2026). D-ID Pricing Plans, Studio table. Videos, Agents, Video Translate, and API still listed as live SKUs. Trial through Enterprise. No prices copied here. d-id.com/pricing/studio
  12. blackchaos22 (r/AI_Agents, still live 16 September 2026; posted about five months earlier). Practitioner report: D-ID Talks Streams freeze on the last frame; D-ID Agents V4 (LiveKit) as a continuous stream. reddit.com/r/AI_Agents
  13. Keall, C. (February 2026). AI casualty: Once high-flying Soul Machines in receivership, KPMG looks for buyer. NZ Herald. Voluntary receivership 5 February 2026. Vendor-risk example, not a project result. nzherald.co.nz

08 · Final Reflection

Final Reflection

Mid-stream notes. Still complete enough for hiring.

What I know now

If an interface looks and talks like a person, disclosure and oversight are product requirements, not footnotes. The restaurant-floor standard still applies: the system has to hold under real use. Field papers already show that person-like agents spend trust faster when they fail. That is why this case stays honest about scope.

Challenges

Keep public copy useful without leaking unpublished work. Do not confuse field knowledge with project results that are not on this page yet.

What I would improve

Ship public-safe artifacts earlier: framing memo and workflow map. Recruiters should not have to wait for a paper that may never be the right public vehicle.

Ethical line

No clinical framing on this site. No fake metrics. Accountability stays human even when the interface looks human.

Resume: cv.pdf. Profile: about. Email: jlzhao@iu.edu.