---
type: source
id: manhede-2026-online-markets-training
title: Human discernment of artificial intelligence in online markets can be shaped by experience and training
authors:
- Tanaka Manhede
- Yuliana Fartachuk
- Steven A. Martinez
- Allison Langer
- Jason M. Chein
year: 2026
venue: AI & Society
volume: null
pages: null
doi: 10.1007/s00146-026-03373-3
peer_review: peer-reviewed-journal
evidence_tier: B
design: 'Correlational baseline (AI attitudes, AI engagement vs discernment) plus a two-group training study: experimental group trained on labelled human/AI examples, then immediate feedback and per-correct monetary reward; control group did similar exercises with unlabelled examples and delayed, performance-based reward'
modality:
- text
- image-general
stimuli: 'Vacation-rental listings: human-written (Airbnb) or AI-generated (ChatGPT or Claude) descriptions; half text-only, half paired with property-exterior and host-face images'
n_participants: 117
n_note: 120 recruited, 3 excluded; experimental n=60, control n=57
topics:
- human-detection
- ai-text
- training
- individual-differences
summary: Baseline discernment of AI-generated vs human rental listings was 57.4%; heavier AI engagement predicted worse discernment (r = -0.29) while AI attitudes did not; labelled training with feedback added 9.4 points, control training none.
results:
- metric: Baseline accuracy (all)
  value: 57.4%
  note: t(116)=6.87, p<0.001; experimental 57.6%, control 57.3%
  source_location: null
- metric: AI engagement (SCAIES) vs discernment
  value: r(115) = -0.29, p = 0.002
  note: socioaffective engagement r = -0.26
  source_location: null
- metric: Attitudes toward AI vs discernment
  value: r = -0.14, p = 0.14
  note: n.s.
  source_location: null
- metric: Training gain, experimental
  value: +9.4 points baseline → final
  note: ≈6.7 after stage 1; further 2.7 n.s. (p = 0.085)
  source_location: null
- metric: Change, control
  value: -0.6 points (p = 0.74)
  note: ''
  source_location: null
moderators:
- AI-platform engagement
- Labelled examples + immediate feedback
limitations:
- Single listing genre
- Short-term; no retention test
- Logged ChatGPT use did not significantly correlate with discernment (r = -0.11)
verification:
  status: verified-fulltext
  confidence: high
  checked: 2026-10-18
  via: link.springer.com article page (open access, CC BY)
url: null
pmid: null
pmcid: null
arxiv_id: null
openalex_id: null
oa_url: null
cited_in:
- MR-0001
updated: '2026-10-18'
---

# Human discernment of artificial intelligence in online markets can be shaped by experience and training

> **Source record** `manhede-2026-online-markets-training` · 2026 · AI & Society  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-fulltext  
> **Bottom line:** Baseline discernment of AI-generated vs human rental listings was 57.4%; heavier AI engagement predicted worse discernment (r = -0.29) while AI attitudes did not; labelled training with feedback added 9.4 points, control training none.

## Citation {#citation}

Tanaka Manhede, Yuliana Fartachuk, Steven A. Martinez, Allison Langer, Jason M. Chein (2026). Human discernment of artificial intelligence in online markets can be shaped by experience and training. *AI & Society*. https://doi.org/10.1007/s00146-026-03373-3

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#manhede-2026-online-markets-training](/data/citations.json#manhede-2026-online-markets-training)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Correlational baseline (AI attitudes, AI engagement vs discernment) plus a two-group training study: experimental group trained on labelled human/AI examples, then immediate feedback and per-correct monetary reward; control group did similar exercises with unlabelled examples and delayed, performance-based reward |
| Modality | text, image-general |
| Stimuli | Vacation-rental listings: human-written (Airbnb) or AI-generated (ChatGPT or Claude) descriptions; half text-only, half paired with property-exterior and host-face images |
| Participants (N) | 117 |
| N detail | 120 recruited, 3 excluded; experimental n=60, control n=57 |
| Topics | human-detection, ai-text, training, individual-differences |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Baseline accuracy (all) | 57.4% | t(116)=6.87, p<0.001; experimental 57.6%, control 57.3% |
| AI engagement (SCAIES) vs discernment | r(115) = -0.29, p = 0.002 | socioaffective engagement r = -0.26 |
| Attitudes toward AI vs discernment | r = -0.14, p = 0.14 | n.s. |
| Training gain, experimental | +9.4 points baseline → final | ≈6.7 after stage 1; further 2.7 n.s. (p = 0.085) |
| Change, control | -0.6 points (p = 0.74) |  |

## Moderators {#moderators}

- AI-platform engagement
- Labelled examples + immediate feedback

## Limitations {#limitations}

- Single listing genre
- Short-term; no retention test
- Logged ChatGPT use did not significantly correlate with discernment (r = -0.11)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-fulltext` |
| Compiler confidence | high |
| Checked on | 2026-10-18 |
| Via | link.springer.com article page (open access, CC BY) |