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Understanding the determinants of the outcomes for artificial intelligence in personalized learning: the rule of task completion time
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| 1.36 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Artificial intelligence (AI) for personalized learning is an emerging field that has attracted
growing attention from educational technology researchers. Recent developments have
emphasized the importance of system use, user satisfaction, and educational outcomes. We
propose a model combining the DeLone & McLean IS success model and the belief-actionoutcome (B-A-O) model to evaluate the impact of AI-based personalized learning systems on
both humanistic and instrumental outcomes. The empirical approach is based on data
collected from 301 student interviews across diverse educational settings. The results reveal
that use and user satisfaction are critical antecedents of perceived task performance,
individual performance, and quality of life. Furthermore, we demonstrate the moderating role
of task completion time, which significantly influences the strength of the relationships
between system use, satisfaction, and the final outcomes. Understanding the influence of AIdriven learning platforms on student performance and well-being offers valuable insights for
developers, educators, and policymakers aiming to optimize adaptive educational
technologies. We provide both theoretical and practical implications based on our findings.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Digital Transformation
Palavras-chave
Artificial intelligence DeLone & McLean model B-A-O model SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure