Academic Journal

Beyond Automation: How Agentic AI Is Transforming Educational Ecosystems.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Beyond Automation: How Agentic AI Is Transforming Educational Ecosystems.
Συγγραφείς: Reddiwar, Rakesh1, Roy, Sunil Kumar2 sunilkumarroy@mru.edu.in
Πηγή: Journal of Theoretical Accounting Research. 2026 Special Issue, Vol. 22, p160-169. 10p.
Θεματικοί όροι: *Intelligent agents, *Algorithmic bias, Individualized instruction, Student engagement, Data privacy, Classroom environment, Teaching methods, Teacher development
Περίληψη: The rapid evolution of agentic artificial intelligence (AI), characterized by autonomous decision-making, adaptive learning, and goal-driven behavior, is reshaping teaching and learning paradigms in school education. This study assesses the impact of agentic AI on instructional practices, student engagement, and learning outcomes across primary and secondary educational contexts. This qualitative research combines global articles published in renowned journals to understand artificial Intelligence impacting global education in both positive as well as negative ways. The findings suggest that agentic AI systems enhance personalized learning by dynamically adjusting at regular intervals and feedback to individual students. Improved efficiency in lesson planning, assessment and classroom management reported by teachers globally. Progress from students in self-directed learning and improved conceptual understanding when interacting with technology and learning patterns. However, the study also highlights key challenges like data privacy, less human interaction, the need for teacher upskilling and algorithmic bias. In these environments, role of an educator shift from just teacher to ethical guide while utilizing technology. The paper concludes with balanced approach while using technology with guidance from policy makers and continuous development from tool makers. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Theoretical Accounting Research is the property of Journal of Theoretical Accounting Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The rapid evolution of agentic artificial intelligence (AI), characterized by autonomous decision-making, adaptive learning, and goal-driven behavior, is reshaping teaching and learning paradigms in school education. This study assesses the impact of agentic AI on instructional practices, student engagement, and learning outcomes across primary and secondary educational contexts. This qualitative research combines global articles published in renowned journals to understand artificial Intelligence impacting global education in both positive as well as negative ways. The findings suggest that agentic AI systems enhance personalized learning by dynamically adjusting at regular intervals and feedback to individual students. Improved efficiency in lesson planning, assessment and classroom management reported by teachers globally. Progress from students in self-directed learning and improved conceptual understanding when interacting with technology and learning patterns. However, the study also highlights key challenges like data privacy, less human interaction, the need for teacher upskilling and algorithmic bias. In these environments, role of an educator shift from just teacher to ethical guide while utilizing technology. The paper concludes with balanced approach while using technology with guidance from policy makers and continuous development from tool makers. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Theoretical Accounting Research is the property of Journal of Theoretical Accounting Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Algorithmic bias
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      – SubjectFull: Individualized instruction
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      – SubjectFull: Classroom environment
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      – SubjectFull: Teaching methods
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      – SubjectFull: Teacher development
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              Text: 2026 Special Issue
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