Artificial Intelligence in Education: Boon or Bane for Academic Development?

Authors

  •   Amol Bhanudas Nawale Assistant Professor, Agasti Institute of Management, Computer Application and Research (AIMCAR)
  •   Jay Pandharinath Pathave Assistant Professor, Agasti Institute of Management, Computer Application and Research (AIMCAR)

DOI:

https://doi.org/10.17697/ibmrd/2025/v14i2/174478

Keywords:

Artificial Intelligence (AI), Education Technology, Academic Development, Cognitive Skills, AI Ethics in Education, Digital Learning Tools, AI-Driven Pedagogy, Educational Equity, Over-Reliance on AI

Abstract

As a result of AI's incorporation into education, there is a growing interest in how it affects students' academic progress. Concerns about cognitive capacities, dependence, and ethical problems continue despite the fact that artificial intelligence offers improved academic performance and tailored education. In order to understand how AI affects students' academic performance, this study will examine how students perceive, use, and weigh the pros and cons of AI-enhanced learning environments. Through the use of tailored feedback, engagement, and performance evaluation, AI systems improve students' academic growth. Problems with data privacy, academic dishonesty, reliance on AI systems too much, and a decline in critical thinking abilities are major worries. Students emphasised the need for a structured approach when it comes to AI-driven instruction. Artificial intelligence has the potential to improve students' learning and performance in the long run. Ethical standards and pedagogical oversight are necessary for its implementation to reduce potential dangers. If we want to see fair and long-lasting progress in education, we need to use AI in a way that is both thorough and student-centered.

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Published

2025-09-30

How to Cite

Nawale, A. B., & Pathave, J. P. (2025). Artificial Intelligence in Education: Boon or Bane for Academic Development?. IBMRD’s Journal of Management & Research, 14(2), 5–9. https://doi.org/10.17697/ibmrd/2025/v14i2/174478

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