EMOTIONAL INTELLIGENCE IN MACHINES: CAN AI CULTIVATE EMPATHY?
Bibliographic record
Abstract
In the context of rapid digitalization and transformation of educational practices, there is a need to rethink the role of artificial intelligence in pedagogy. If in the first wave of digitalization, AI was considered primarily as a cognitive tool for automation and optimization of learning, then at present a new vector is being actualized - the study of its affective potential. Of particular importance is the question: is AI, deprived of its own emotional nature, capable of participating in the formation of such key competencies of the 21st century in humans as empathy, emotional regulation and social sensitivity? The article is devoted to the analysis of the concept of emotional intelligence of machines and consideration of AI not only as an intermediary in the transfer of knowledge, but also as an agent of the educational process. The novelty of the study lies in the integration of theories of emotional intelligence with modern developments in the field of affective computing, which allows us to propose an interdisciplinary model of “empathic AI pedagogy.” This model assumes a transition from traditional cognitive digitalization to “affective education,” where emotional AI becomes an accomplice in the development of the student’s personality. The article examines in detail the technological foundations of emotional AI: emotion recognition systems based on voice, facial expressions and behavioral signals; mechanisms for synthesizing empathically colored text and speech; multimodal architectures that combine cognitive and affective models of interaction. It is shown that the integration of such solutions into educational platforms opens up opportunities for supporting student motivation, reducing anxiety, developing empathy and developing soft skills. The practical significance of the study is that it offers conceptual guidelines for state educational policy, training of teachers to work with emotional AI, and international cooperation in the development of standards for affective education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".