ChatGPT, the voice from elsewhere: a poetic and therapeutic dialog between human and artificial intelligence
Bibliographic record
Abstract
With the recent advancements of generative artificial intelligence (AI), the dialog between humans and AI is no longer merely functional but can be a profound means of reflection and psychological growth, especially when artistic approaches, such as poetry, are included. AI has the potential to generate poetic responses that deeply resonate with human experience, facilitating the expression of emotions, promoting psychological well-being, and encouraging personal reflection. This article describes a poetic exchange over a seven-day period between the author and ChatGPT, personified as the “Voice from Elsewhere,” that explores an existential crisis. After each exchange, the results were analyzed from a Jungian perspective to highlight connections to symbols and archetypes that could be used in Poetry Therapy or other therapeutic environments. The results indicate that ChatGPT has the capacity to create poems related to existential questions which could open the way for new and enriching dialogs, capable of potentially supporting psychotherapeutic work. Poetry, whether human or machine generated, will remain an artistic means to explore and experience inner transformation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".