Work

Artificial Intelligence-driven Adaptive E-learning Systems for Personalized Student Engagement in Digital Education Platforms

Year: 2026

Type: article

Abstract: Adaptive e-learning systems increasingly use artificial intelligence, yet discussion often treats personalization as a proxy for student engagement. Existing accounts remain fragmented across learner modeling, platformconstraints, and engagement language, which encourages overbroad claims. This paper proposes a conceptual framework that links learner data signals, learner models, categories of adaptive action, engagement dimensions,and contextual guardrails such as privacy, fairness, and transparency. The framework is illustrated using literature-backed platform patterns to show how different adaptations target distinct engagement components rather than simple activity metrics. The contribution is a disciplined organizing logic that clarifies definitions, separates adaptive design from effectiveness claims, and supports interpretable engagement reasoning without new data. The framework is intended for AI in education researchers, digital learning designers, and higher-education practitioners working in digital education platforms.

Source: International online journal of education and teaching

Authors: Ghodke, Prasad, Totare, Reshma Yogesh, Mandlik, Sachin Bhimraj, Ingale, Kiran, Bhowmik, Mahua, Singh, Surendra Pal, Kumari, Poonam

Institutions: Savitribai Phule Pune University, AISSMS Institute of Information Technology, Vishwakarma Institute of Technology, Dharma Samaj College, Noida International University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W7216635514

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