SWOT Analysis of AI-Based Learning Recommendation Systems for Student Engagement
DOI:
https://doi.org/10.33050/italic.v4i1.894Keywords:
Learning, Artificial Intelligence, SWOT, Recommendation Systems, Student EngagementAbstract
This study presents a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) of AI-based learning recommendation systems for students. These innovative systems hold significant potential in supporting Sustainable Development Goal (SDG) 4 Quality Education by personalizing learning pathways, enhancing access to resources, and boosting student engagement. Their primary strengths include increased learning efficiency, adaptive content delivery, and instant feedback mechanisms. Nevertheless, weaknesses such as potential algorithmic bias, data privacy concerns, and over reliance on technology warrant careful consideration. Emerging opportunities encompass expanding educational access for underserved populations, facilitating lifelong learning, and integrating diverse educational platforms. However, threats like the digital divide and the need for robust ethical guidelines must be addressed to ensure equitable access. This analysis underscores the necessity of a balanced approach in developing and deploying these AI systems, maximizing their educational benefits while mitigating risks to achieve more inclusive and equitable quality education for all. Quantitatively, the synthesis of reviewed studies reveals that adaptive AI-based recommendation systems improve student engagement by up to 18% and content relevancy by approximately 22% compared to conventional systems. Moreover, the SWOT analysis indicates that the strength to threat ratio (S/T) exceeds 2.1, implying that institutional readiness and technological innovation significantly outweigh identified implementation risks. These findings confirm the robust potential of AI-LRS in higher education.
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