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TRUVACE RECORD VERSION
record: TRV-2026-0869
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-24T14:14:36.018947Z
status: published
lens: g_space
sector: education
headline: Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
dek: The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, re…
gain_title: AI/ML integration in adaptive e-learning systems personalizes learning experiences and optimizes learning paths, leading to higher student engagement, retention, and academic performance including increased test scores.
problem_title: (none)
trace_subject: (none)
gain_reading: AI/ML integration in adaptive e-learning systems personalizes learning experiences and optimizes learning paths, leading to higher student engagement, retention, and academic performance including increased test scores.
gain_evidence: AI/ML algorithms are instrumental in personalizing learning experiences | optimize learning paths, enhance engagement, and improve academic performance | increased test scores
problem_reading: (none)
problem_evidence: (none)
quick_read: Published December 6, 2023, this peer-reviewed literature review in Education Sciences examined 63 articles from 2010 onward on AI and machine learning in e-learning. It found adaptive algorithms are used to tailor learning paths to individual needs, with multiple studies reporting improved engagement, retention, and academic performance.

The synthesis matters because it consolidates evidence that personalization at scale is feasible in digital education, yet it also surfaces that privacy risks and technical complexity remain unresolved. As a review of existing work, it does not provide new experimental effect sizes or long-term outcome data for specific student populations.
limitation: Findings are synthesized from prior literature rather than a new controlled trial, and the review itself flags unresolved boundaries around privacy and system complexity.
tag: Evidence-backed gain
key_points: Systematic literature review analyzed 63 articles published from 2010 onwards on AI/ML in e-learning. | Adaptive learning algorithms were evaluated for impact on student engagement, retention, and performance. | Authors conclude integration significantly contributes to personalization and effectiveness despite noted challenges.
rundown: The authors conducted a systematic review of 63 articles from 2010 onward to map how AI and machine learning are deployed in e-learning for adaptive learning, focusing on algorithm types and educational implications.

Across the reviewed studies, adaptive systems adjusted content and pathways to individual learner needs, with reported effects on engagement, retention, and performance, while the review also documented persistent implementation barriers.
sources:
- peer_reviewed | Education Sciences | https://doi.org/10.3390/educsci13121216 | 2023-12-06
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