Research on the Application of ChatGPT in the Interdisciplinars of Higher Education
Bibliographic record
Abstract
This paper focuses on analyzing and assessing the role and potential impacts of ChatGPT within the realm of higher education, with a specific emphasis on its utility in interdisciplinary academic and research contexts. As an advanced tool in the ever-evolving landscape of artificial intelligence, ChatGPT's deployment in educational and research frameworks introduces both novel opportunities and challenges. The study employs an array of methodological approaches including comprehensive literature reviews, in-depth case studies, empirical data analysis, and comparative research to gauge the effectiveness of ChatGPT in higher educational settings. Findings from the study suggest that ChatGPT serves as a pivotal medium for the integration and dissemination of knowledge across various disciplines, promoting cross-disciplinary dialogue and understanding. It also plays a significant role in enhancing students' ability to think across different academic domains and nurture their innovative skills. The multifaceted research approach not only sheds light on the diverse functionalities of ChatGPT in interdisciplinary higher education but also underscores its potential as a forward-looking tool in shaping future educational and research paradigms.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".