Influence of AIGC on Research Activity in Higher Education
Bibliographic record
Abstract
The development of generated artificial intelligence has a revolutionary impact on higher education. The application of AIGC technology has influenced various aspects of higher education extensively. The effective interaction and mutual promotion between AIGC and traditional university education would be necessary to realize the value of higher education in the digital era. To discuss the eminent issues, the paper analyzed positive contributions and negative influences of AIGC. AIGC technology empowered teaching method reform in university courses, which include reduction of repetitive activities, rigorous knowledge acquirement and sorting, and personalized learning opportunities. Thesis writing could be facilitated, from literature review, structural and organization, and language improvement. It also promoted innovative and creative activities in university courses, with broadened knowledge base and widened information source. And then, its negative effects are also discussed. The academic ethic and legal issues could lead to intellect property controversy, the privacy of information and the possibility of plagiarism. The innovation impetus and original thinking could be hindered. In addition, assessment of course work, exam and thesis should adapt to the scenario of incorporation of AIGC with original work of the students. The countermeasure to mitigate negative effect includes transform in cultivation plan and course design; the standardization and transformation of assessment and evaluation. The development strategy should focus on the role transition, of the university, from the academic authority to a knowledge learning choice, of AIGC from substitution to complementary, so as to ensure the development of innovation and original thinking of the future university graduates.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".