Artificial Empathy: User Experiences with Emotionally Intelligent Chatbots
Bibliographic record
Abstract
This study aims to explore user experiences with emotionally intelligent chatbots, focusing on their perceived empathy, satisfaction with interactions, and the impact on user perception. Additionally, it seeks to identify the main challenges and future expectations users have towards these AI systems. Employing a qualitative research design, this study collected data through semi-structured interviews with 28 participants who had interacted with emotionally intelligent chatbots in various contexts. Thematic analysis was conducted to identify main themes, categories, and concepts within the data, providing insights into users' perceptions and experiences. The analysis revealed eight main themes: Perceived Empathy, Interaction Satisfaction, Trust and Security, Human-Like Interaction, User Adaptation, Impact on User Perception, Barriers to Engagement, and Future Expectations. These themes encompass categories such as Emotional Understanding, Contextual Sensitivity, Engagement Level, Privacy Concerns, Learning Curve, AI Capabilities, Technological Limitations, and Improvement Suggestions. Participants valued chatbots' ability to understand and adapt to their emotional states but highlighted challenges in achieving authentic empathy and expressed concerns over privacy and data security. Emotionally intelligent chatbots hold promise for enhancing user experiences through artificial empathy. However, the authenticity of empathy, coupled with ethical considerations such as privacy and security, presents significant challenges. Future developments should focus on improving the genuineness of empathetic responses, ensuring ethical use of AI, and addressing users' concerns to fully realize the potential of emotionally intelligent chatbots.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".