| _version_ | 1866902027451236352 |
|---|---|
| author | Raxmonova Nilufarxon Vaxobjon qizi |
| author_facet | Raxmonova Nilufarxon Vaxobjon qizi |
| contents | <p>This article examines the evolution of higher mathematics teaching, connecting traditional optimization models with modern artificial intelligence (AI) integration. It explores how mathematical modeling strengthens problem-solving in optimization tasks and transitions to AI-driven adaptive systems that personalize learning and develop logical thinking. Drawing on differential and individualized pedagogical strategies, the study analyzes case studies and empirical data from Uzbekistan and international implementations. The integration of machine learning for real-time feedback and predictive analytics improves student performance, while addressing challenges such as accessibility and teacher training. The paper proposes a hybrid framework combining optimization principles with AI to enhance engagement, comprehension, and outcomes, highlighting its potential to prepare students for advanced STEM challenges.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19644044 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HIGHER MATHEMATICS INSTRUCTION MODERN METHODS FROM OPTIMIZATION MODELS TO AI INTEGRATION Raxmonova Nilufarxon Vaxobjon qizi <p>This article examines the evolution of higher mathematics teaching, connecting traditional optimization models with modern artificial intelligence (AI) integration. It explores how mathematical modeling strengthens problem-solving in optimization tasks and transitions to AI-driven adaptive systems that personalize learning and develop logical thinking. Drawing on differential and individualized pedagogical strategies, the study analyzes case studies and empirical data from Uzbekistan and international implementations. The integration of machine learning for real-time feedback and predictive analytics improves student performance, while addressing challenges such as accessibility and teacher training. The paper proposes a hybrid framework combining optimization principles with AI to enhance engagement, comprehension, and outcomes, highlighting its potential to prepare students for advanced STEM challenges.</p> |
| title | HIGHER MATHEMATICS INSTRUCTION MODERN METHODS FROM OPTIMIZATION MODELS TO AI INTEGRATION |
| url | https://doi.org/10.5281/zenodo.19644044 |