A Philosophical Inquiry Into Utilizing ChatGPT Through an I-Thou Framework
Bibliographic record
Abstract
Research into using AI for editing doctoral dissertation work in music education and a subsequent review of literature prompted this collaborative investigation. Specifically, this paper examines ChatGPT-Human collaboration in doctoral dissertation writing and editing through the lens of Martin Buber’s (1958) I-Thou relation. Constructed through dialogical discourse (Bakhtin 1981), our (the supervisor and the supervisee) voices interact with the intent to explore: (1) the ways ChatGPT was utilized for editing the supervisee’s dissertation and how reflexivity influenced the process, (2) the impact that ChatGPT-Human collaboration has on the supervisor-supervisee role shift, (3) the ethical considerations, including the supervisee’s voice, authorship, and agency that can be impacted in response to such shifts, and (4) whether such shifts and impacts contribute to specific aspects of our pedagogical values as teachers in the field of music education. This paper offers insights into the practical application of AI in music education and advocates for further honest dialogues regarding the utilization of ChatGPT.
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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 | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.094 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".