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Other literature type . 2022
License: CC BY
Data sources: ZENODO
ZENODO
Other literature type . 2022
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2022
License: CC BY
Data sources: Datacite
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Creating Digital Products Enhanced By Artificial Intelligence and Adaptive Learning Models

Authors: Kondepudi, Swathi;

Creating Digital Products Enhanced By Artificial Intelligence and Adaptive Learning Models

Abstract

As user expectations shift toward intelligent, personalized digital experiences, creators must reimagine how products are built and delivered. This article explores the integration of artificial intelligence (AI) and adaptive learning models into digital product development. It outlines how these technologies can enable products to respond to user behavior in real time, deliver tailored content, and continuously improve functionality. By examining the key components of AI-enhanced systems—including data strategy, model selection, UX considerations, and ethical design—the article offers a comprehensive guide for teams aiming to develop responsive and scalable digital products. Real-world examples from education, health, productivity, and media illustrate how adaptive learning and AI can increase engagement, retention, and value. Whether you’re a founder, product manager, or developer, embracing AI and adaptive systems is now essential to staying competitive in a dynamic digital marketplace. Intelligent products are no longer futuristic—they are the new standard

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Keywords

AI Digital Products, Adaptive Learning, Machine Learning, Personalized UX, Smart Applications

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green