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Human vs. Machine: A Pygmalion Perspective on Anthropomorphism and the Effectiveness of AI Feedback for Individual Learning

Hai‐Jiang Wang et al.

Human Resource Management (US)2026https://doi.org/10.1002/hrm.70053article
FT50AJG 4ABDC A*
Weight
0.41

Abstract

Despite the widespread adoption of artificial intelligence (AI) in human resource management (HRM) practices, developmental feedback provided by AI does not always achieve its intended effectiveness. Drawing on the Pygmalion theory and the literature on human‐AI interactions, we propose that individuals perceive lower expectation when AI provides developmental feedback, compared to human feedback providers. Perceived expectation positively influences individual learning (i.e., learning motivation and learning performance) and mediates the relationship between feedback provider (human vs. AI) and individual learning. Furthermore, we propose that feedback recipients may perceive higher expectation from anthropomorphic (vs. non‐anthropomorphic) AI, leading to greater learning outcomes. Our findings provide support for these predictions through two scenario‐based experiments (Studies 1 and 2) and a field experiment (Study 3) across a variety of learning contexts. This research sheds new light on the underlying mechanisms of human‐AI interaction and offers practical implications for organizations seeking to utilize AI technology more effectively in employee learning and development.

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https://doi.org/https://doi.org/10.1002/hrm.70053

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@article{hai‐jiang2026,
  title        = {{Human vs. Machine: A Pygmalion Perspective on Anthropomorphism and the Effectiveness of AI Feedback for Individual Learning}},
  author       = {Hai‐Jiang Wang et al.},
  journal      = {Human Resource Management (US)},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/hrm.70053},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.