Functional vs. phenomenological empathy in large language models
Rethinking artificial empathy through experimental evidence
Bibliographic Data
| ID | 22433667 |
|---|---|
| Authors | Ahmad Mugableh (0000-0002-5511-553X, Jouf University), Hissah Mohammed Alruwaili (0000-0002-7877-3076, Jouf University) |
| Year | 2026 |
| Volume | 13 |
| Issue | 1 |
| Publication date | 2026-06-29 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | East European Journal of Psycholinguistics (JOURNAL) |
| Journal identifiers | ISSN: 2312-3265 • E-ISSN: 2313-2116 |
| Publisher | Lesya Ukrainka Volyn National University (PUBLISHER • UA) |
| DOI | 10.29038/eejpl.2026.13.1.mug |
| OpenAlex | W7167330290 |
| Language | EN |
| References cited | 20 |
Although large language models (LLMs) are increasingly used in emotionally sensitive contexts, it remains unclear whether their responses reflect genuine empathy or functional simulation. This study employed a mixed-methods experimental design to compare empathetic responses from human participants (n = 100) with those generated by LLMs under three conditions: text-only, anthropomorphic cue, and multimodal with cue. Using ten standardized emotional vignettes, 200 independent raters evaluated all responses with the Consultation and Relational Empathy (CARE) measure. Quantitative results showed that LLM-generated responses were consistently rated as more empathetic than human responses, with empathy scores increasing across enhanced design conditions. Qualitative analysis using the Empathic Communication Coding System (ECCS) revealed that LLMs relied heavily on surface validation and supportive strategies, while demonstrating limited contextual probing and a lack of experiential grounding compared to human responses. These findings highlight a critical distinction between perceived empathy and phenomenological empathy. While LLMs can effectively simulate empathic communication and achieve high ratings due to consistency and alignment with social expectations, they lack the contextual depth and experiential understanding that characterize human empathy. This study contributes to the literature by moving beyond performance-based evaluations and clarifying the structural differences between human and artificial empathy. The results have important implications for the use of LLMs in healthcare, education, and counseling, where perceived empathy may enhance user experience but also raises concerns about over-trust and anthropomorphic attribution. Disclosure Statement The authors reported no potential conflicts of interest.
Empathic concern · Empathy · Experiential learning · Interpretative phenomenological analysis · Perception · Simulation theory of empathy · Artificial Intelligence in Healthcare and Education · Digital Mental Health Interventions · Empathy and Medical Education
Affective Computing
The Neural Bases for Empathy
The complex relation between morality and empathy
These Things Called Empathy
On seeing human
Minds, brains, and programs
Large language models for surgical informed consent
Empathetic Large Language Models, the social capacities and human flourishing
Using thematic analysis in psychology
| Highly cited | No |
|---|