ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED LEARNING
Bibliographic record
Abstract
The aim of this study is to evaluate how AI-driven tools and platforms influence students’ academic performance, engagement, and learning satisfaction. This quantitative study investigates the role of Artificial Intelligence (AI) in personalized learning within higher education settings. Data were collected through a structured questionnaire administered to 300 university students using AI-based educational technologies. The results reveal a significant positive correlation between AI integration and students’ perceived personalization of learning experiences (r = 0.73, p < 0.01). Additionally, 74% of the respondents reported that AI tools improved their ability to identify learning gaps and receive tailored content. Students noted benefits such as self-paced learning, instant feedback, and higher motivation. The study highlights the transformative potential of AI in shifting from a one-size-fits-all educational model to a learner-centered approach. These findings offer valuable insights for educators, policymakers, and EdTech developers aiming to enhance personalized education through intelligent systems.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".