Conversational AI for Mental Health: The Moderating Role of Big Five Personality Traits
Bibliographic record
Abstract
Artificial intelligence is one of the most rapidly emerging research areas today. Researchers have found that, dumping one’s troubles with AI could gain emotional support and moderate negative affects. However, there is still lack of research and summary on how personality factors influence the mental health intervention. Therefore, this paper examines the relationship between personality and the effects of AI conversation in the area of mental health by literature review. The research found that: (1) The interference of personality on the mental health intervention outcome has existed already. Therefore, studying the impact of personality differences on AI mental health intervention can be challenging, considering its’ difficult to isolate the effect of users’ current mental health status. (2) People with distinct personality hold different attitude about talking to AI and AI technology in general. For example, Neurotic users, on the one hand, stay vigilant on AI interaction; on the other hand, they wish AI could fulfill their social need of being understood and accepted. Based on the research, this article proposed two related recommendation to AI mental health product designing as following: (1) Considering that Individuals with certain personalities exhibit a higher need for the product, the priority and the designing focus should be given to the user group with personality traits that are targeted by the design features. (2) Future research should focus on the personality influences on the motivation and attention focus of AI chatbot using to improve the participation rate and the effect of mental health support.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".