Personalized Education in the Artificial Intelligence Era: What to Expect Next
| Autor: | Mihaela van der Schaar, Andrew S. Lan, Jie Xu, Setareh Maghsudi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Personalized education business.industry Applied Mathematics media_common.quotation_subject Big data Globe 020206 networking & telecommunications 02 engineering and technology Personalized learning Data science Knowledge acquisition medicine.anatomical_structure Signal Processing 0202 electrical engineering electronic engineering information engineering medicine Electrical and Electronic Engineering business Curriculum Diversity (politics) media_common |
| Zdroj: | IEEE Signal Processing Magazine. 38:37-50 |
| ISSN: | 1558-0792 1053-5888 |
| DOI: | 10.1109/msp.2021.3055032 |
| Popis: | The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions. |
| Databáze: | OpenAIRE |
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