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| Autors principals: | , , |
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| Format: | Recurso digital |
| Idioma: | anglès |
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Zenodo
2026
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| Accés en línia: | https://doi.org/10.5281/zenodo.20052967 |
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| _version_ | 1866902317883719680 |
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| author | Vaishali Amit Jain Ronak Duggar |
| author_facet | Vaishali Amit Jain Ronak Duggar |
| contents | <p class="MsoNormal"><span>AI is changing how the learning manifestation of university students is delivered through making it more intelligent and customised to each student. This time, the researcher will present an intelligent study planner, which will reduce academic stress and enhance performance during the learning process by integrating AI in the present paper. The planner, in this case, takes into account such issues as the study habits, the workload, or the personal learning preferences of students based on machine learning and analytics. Based on this information, it constructs custom, self-adaptive timetables of study. The system also tracks our performance and adjusts our plans as we go to ensure that we remain on track and efficient. The findings indicate that AI-supported planners may help to increase engagement, enhance grades and reduce stress, yet we must continue to be concerned with data privacy, ethics, and technological constraints in the future.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20052967 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smart Study Planner AI-Based Academic Stress Reduction and Student Learning Outcomes Improvement Vaishali Amit Jain Ronak Duggar Artificial intelligence, personalised learning, adaptive learning, study planner, academic stress, learning analytics, machine learning. <p class="MsoNormal"><span>AI is changing how the learning manifestation of university students is delivered through making it more intelligent and customised to each student. This time, the researcher will present an intelligent study planner, which will reduce academic stress and enhance performance during the learning process by integrating AI in the present paper. The planner, in this case, takes into account such issues as the study habits, the workload, or the personal learning preferences of students based on machine learning and analytics. Based on this information, it constructs custom, self-adaptive timetables of study. The system also tracks our performance and adjusts our plans as we go to ensure that we remain on track and efficient. The findings indicate that AI-supported planners may help to increase engagement, enhance grades and reduce stress, yet we must continue to be concerned with data privacy, ethics, and technological constraints in the future.</span></p> |
| title | Smart Study Planner AI-Based Academic Stress Reduction and Student Learning Outcomes Improvement |
| topic | Artificial intelligence, personalised learning, adaptive learning, study planner, academic stress, learning analytics, machine learning. |
| url | https://doi.org/10.5281/zenodo.20052967 |