Transforming Higher Education with Adaptive AI Driven-Learning: Challenges and Opportunities

• Vera, Fernando ^[1]
  1. [1] University of the Basque/Euskal Herriko Unibertsitatea, España
• Localización: Transformar, ISSN-e 2735-6302, Vol. 5, Nº. 2, 2024, págs. 36-52
• Idioma: español
• Títulos paralelos:
  • Challenges and Opportunities
• Enlaces
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• Resumen

  • Artificial Intelligence (AI) for adaptative learning is revolutionizing higher education by offering personalized learning experiences tailored to individual student needs. By leveraging real-time data analysis, AI-driven platforms can adapt content delivery, provide targeted feedback, and suggest customized learning paths based on each student’s strengths and weaknesses. In this context, this study investigates the effectiveness and perceptions of adaptive AI-driven learning systems among nursing faculty at a private university in Chile (n= 66). Using a quantitative, non-experimental design, a Likert-type questionnaire with 20 items was administered to a group of instructors to find out the systems' impact on student engagement, motivation and learning outcomes. Results reveal that adaptive AI-driven learning is highly regarded for improving conceptual understanding and information retention. The findings highlight strengths in enhancing student engagement and motivation, while identifying areas for further refinement. These insights contribute to understanding the practical implications of integrating adaptive AI into higher education and offer recommendations for optimizing system design and implementation.