Automated Determination of a Behavioral Personality Type Using the Disc Method: Comparative Research of Programs and Chatbots Based on Artificial Intelligence
Bibliographic record
Abstract
The article presents the results of the comparative research and testing of programs and chatbots based on artificial intelligence in order to determine the type of client's personality using the DISC method. The purpose of this study is to draw conclusions about the comparative effectiveness of the automated programs of psychometric text-based analysis and the possibility of their use in practice. We have analyzed 62 text mining programs and 11 artificial intelligence chatbots using automated language models and identified those that are capable of psychometric text analysis based on the DISC methodology. Using selected programs, we analyzed the text of social media posts and interviews of the selected company leader in order to determine his psychological characteristics and personality type. The studied programs are able to determine the personality type based on his/her texts and social networks, however, in our opinion, today such an assessment is not as reliable as with direct psychometric testing of a person and observation of his/her behavior in real life. This method of studying a person is quite useful from a marketing point of view and allows to prepare a product and business offer based on the psychological characteristics of a potential client.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".