ECONOMIC EFFICIENCY OF AI ALGORITHMS FOR EDUCATIONAL CONTENT GENERATION IN DISTANCE LEARNING
Keywords:
artificial intelligence, distance learning, economic efficiency, educational content generation, e-learning ROI, adaptive learning, large language models, digital transformation, EdTechAbstract
This article analyses the economic efficiency of artificial intelligence (AI) algorithms applied to the generation of educational content in distance learning environments. Drawing on industry analyses by Grand View Research, Global Growth Insights, Custom Market Insights, and Mordor Intelligence, on corporate case studies of IBM, Cisco, Dow Chemical, Khan Academy, and Coursera, and on peer-reviewed research published in 2024–2026, the paper documents that the global distance-learning market is expected to grow from USD 52.43 billion in 2024 to USD 479.04 billion by 2034 at a CAGR of 24.02%, while the AI-in-education segment scales from USD 5.88 billion to USD 32.27 billion by 2030 (CAGR 31.2%). On the demand side, corporate users report savings of up to USD 200 million (IBM), cost-per-learner reductions of 88% (Dow Chemical from USD 95 to USD 11), and a USD 30:1 return on every dollar invested in e-learning. On the academic side, the 2025 Gallup–Walton Family Foundation survey of 2,232 U.S. K-12 teachers demonstrates that weekly AI users recover 5.9 hours per week — the equivalent of six working weeks per school year — while AI-personalised distance-learning courses lift completion rates from the 7.5% MOOC industry average to 47–62%. The paper proposes a four-component framework for evaluating the economic efficiency of AI in distance learning — direct cost reduction, productivity gains, quality and completion improvements, and strategic competitiveness — and identifies four conditions for its responsible scaling: investment in multilingual educational corpora, regulatory readiness, faculty AI literacy, and explainable, auditable governance.
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