Choudhury, Tanupriya, Yadav, Mohit
ORCID: https://orcid.org/0000-0002-9341-2527, Dewasiri, Narayanage Jayantha and Minakshi, -
(2026)
Adaptive Learning Ecosystems: AI-Enabled Cloud Platforms for Employee Upskilling.
In:
Automating Intelligence With Cloud-Native AI Tools.
IGI Global Scientific Publishing, Hershey, pp. 1-13.
ISBN 9798337374420
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Abstract
The accelerating pace of technological change has created a critical skills gap, rendering traditional corporate training models obsolete. This chapter explores the transformative potential of adaptive learning ecosystems—AI-enabled, cloud-native platforms that create dynamic, personalized environments for continuous employee upskilling. It deconstructs the core architectural pillars of these systems: artificial intelligence for personalization, cloud infrastructure for scalability, and dynamic content for relevance. The analysis covers the mechanisms of adaptation, strategic benefits for organizational agility, and a practical implementation framework. It also addresses critical challenges, including data privacy and algorithmic bias. Ultimately, the chapter argues that investing in these ecosystems is a strategic imperative, enabling businesses to build a resilient, future-ready workforce and sustain a competitive advantage in the AI-augmented economy.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Adaptive learning | Artificial intelligence | Data privacy | Algorithmic bias | AI-augmented economy |
| Subjects: | Social Sciences and humanities > Business, Management and Accounting > Business and International Management Social Sciences and humanities > Business, Management and Accounting > Management Information Systems Social Sciences and humanities > Business, Management and Accounting > Management of Technology and Innovation |
| Depositing User: | Mr. Arjun Dinesh |
| Date Deposited: | 02 Mar 2026 07:31 |
| Last Modified: | 06 May 2026 10:37 |
| Official URL: | https://doi.org/10.4018/979-8-3373-7442-0.ch001 |
| URI: | https://pure.jgu.edu.in/id/eprint/10968 |
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