Bibliographic record
Abstract
Abstract Artificial intelligence is a widely discussed topic, especially in the context of global economic growth. AI is significantly transforming education at all levels, from kindergarten through grade 12 and higher education, by enabling personalized learning experiences and broadening access to quality educational resources, resulting in deep philosophical and societal shifts. On the one hand, AI enhances learning methods and improves the learner’s experience, whereas on the other hand, it poses challenges for youth in becoming job-ready. Although AI offers numerous benefits across various sectors, its integration into the field of education raises critical concerns and deeply impacts societal structures. Many research scholars believe that with the use of AI, the roles of school, college, teachers, and leaders in education will change. AI will have an impact on society and may affect the entire education system in the future. In this regard, this article reports the findings of a study that analyzed the perspectives of students, teachers, and educational leaders, and it discusses the possible scenarios with the influx of AI in education. Further, the article examines the implications of AI on the Indian education system. The study included qualitative and quantitative methods of data collection. The research study was designed based on questionnaires/interviews that obtained the opinions of students, teachers, and educational leaders. The article concludes with the balanced approach of integrating AI in the Indian education system with traditional pedagogical teaching methods.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".