Affective Machines and Human Empathy: A Systematic Review of Emotional AI’s Impact on Social Interaction and Behavioral Trust
Bibliographic record
Abstract
As artificial intelligence (AI) systems become increasingly embedded in everyday human interaction, a new frontier – “emotional AI or affective computing” that is transforming how machines perceive and respond to human emotions. This systematic review synthesizes recent empirical evidence (2015-2025) examining the behavioral, psychological and societal impacts of emotion-recognizing AI in human trust, empathy and social interaction. Using the PRISMA methodology, the review analyses 50 empirical studies across domains including healthcare, education, customer service and human–machine collaboration. The results reveal a dual pathway: while emotional AI can foster improved human–machine cooperation and emotional well-being through adaptive empathy simulation, it simultaneously poses risks of privacy violations, cultural bias and emotional manipulation. Drawing on frameworks from social cognition and behavioral trust theory, we show that human trust in machine-mediated empathy is shaped more by perceived sincerity and relational cues than by raw algorithmic accuracy. The review concludes with ethical and practical recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".