Bibliographic record
Abstract
The proposed piece seeks to critically explore pedagogical implication of ChatGPT, especially on students’ capacities to author a text. The piece suggests that increased reliance on the ChatGPT, while provide short term solution to produce a text, in the long term it is likely to lead to ‘death of an author’. Here the usage of the phrase is a twist to earlier usage by Barthes- which refers to ‘death of an author’ where once the text is written, it gets re-created in readers’ reception and through interpretive act and imagination. The overarching argument of the paper emphasizes that technology is not neutral, especially in a context where its opacity has risen concerns about surveillance, control, and manipulation of human behavior, and therefore its infiltration in education begs critical questioning and sensitive e-value-ation. The discussion argues that rise of AI in education should be checked and not embraced uncritically, but rather it should be critically scrutinized, debated, and scaffolded through critical theoretical, pedagogical, and ethical references to counter its hegemonic and de-humanization of learning. For empirical part, the analysis draws upon reflections generated through a focus group discussion with four undergraduate students enrolled in a Bachelors degree in Computer Science who employed use of ChatGPT in preparing their speeches in context of a humanities course. The students found ChatGPT useful in terms of composing a text/speech and saving time and efforts. However, they realized that its use caused them loss of authentic learning, imagination and suppressed self’s voice. Based on the analysis, the piece shares further insights into pedagogic implications, and suggest a pedagogical scaffolding using critical pedagogical references of relationship between technology and human/learners’ values, distinction between information, knowledge, and wisdom, application and experiential learning references, and praxis in learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".