ArticleAdvances in experimental medicine and biology2026
Data-Driven Precision Learning: Transforming Adult Education with AI and Analytics.
Article in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lifelong learning has become essential in modern society, driven by technological advancements and the increasing demand for continuous skill development. E-learning platforms have transformed education by enhancing accessibility, flexibility, and personalization. Artificial intelligence (AI) and data-driven technologies further refine these platforms, enabling adaptive learning, real-time feedback, and predictive analytics to improve engagement and outcomes. The COVID-19 pandemic accelerated the adoption of e-learning, shifting it from a supplementary tool to a primary mode of education.This study examines the impact of e-learning on adult education, focusing on AI-driven personalization and data analytics. In Greece, national and European policies have facilitated digital education, yet challenges such as infrastructure limitations and digital inequalities persist. Additionally, data mining in e-learning environments has the potential to optimize instructional methods and predict learning outcomes. A proposed framework for precision education integrates multimodal data sources, including biometric indicators, to enhance individualized learning experiences.While AI-powered e-learning systems offer transformative opportunities, ethical concerns surrounding data privacy and equitable access must be addressed. Ensuring responsible data management and inclusive policies will be crucial in maintaining the effectiveness of digital education and fostering lifelong learning in an increasingly technology-driven world.
Indexed as
Identifiers
41479104What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.