A Conversation with ChatGPT: Philosophy, Critical Thinking, and Higher Education
Bibliographic record
Abstract
The growing concern surrounding technology in education, particularly in higher education, serves as the impetus for this paper. After a period of avoiding ChatGPT due to fears that it might diminish my human form, I felt compelled to engage in a conversation with the tool, in light of the conference hosted by the Philosophers of Education Association of Nigeria (PEAN), themed “Philosophy, Artificial Intelligence, and Digital Education.” Through my exploration, I learned that “GPT” stands for generative pre-training transformer, a framework in language processing and machine learning designed to produce human-like responses to various prompts. Recognizing the reality of coexisting with artificial intelligence (AI) and acknowledging that not all emerging technologies merit the attention of philosophers, the rise of ChatGPT has undeniably become a pressing issue in higher education, particularly for faculty and students alike. In my discussions with ChatGPT-4, I engage with the role of AI in higher education, focusing on questions relevant to philosophy, critical thinking, and academic practices. By reflecting on the prompts from ChatGPT and the insights of educational theorists, I conclude that critical thinking and reasoning are intrinsic human qualities that set us apart from AI. Therefore, educators need to embrace their role in guiding student learning and fostering the virtues and skills necessary for navigating an increasingly AI-driven world.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".