AI-Powered Customized Learning Paths: Transforming Data Administration For Students On Digital Platforms
Clicks: 1
ID: 313073
2024
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #649 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
More than ever, effective information management and customized learning opportunities are needed for college students. This is because online education is being preferred by some people and thus choosing it. This article concentrates on AI-based personalized learning paths that the internet platforms are evolving of lately. Educational institutions can leverage AI algorithms and data analytics to find the unique learning characteristics of the student and design his/her educational route in a manner that will be most suitable for his/her learning style, academic achievements and memorization capabilities. AI-driven personalized learning concept is discussed in detail and the outline includes assignments, quizzes and peer assessed tests. Besides, paper argues on potential problems and ethical problems related to bias and data privacy. The main concern of this study is that personalized study paths lead to both the improvement of students' results in academic subjects, and also to the development of important skills of them for a digital era, by investigating this transformative power of AI in detail.
| Reference Key |
imported_1777058850_69ebc422dd10d
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Majid Ali, Ayesha Siddique, Anum Aftab, Muhammad Kamran Abid, Muhammad Fuzail |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.