Authors :
Subrat Chetia
Volume/Issue :
Volume 10 - 2025, Issue 7 - July
Google Scholar :
https://tinyurl.com/5e8skzx3
Scribd :
https://tinyurl.com/jnnnuy8p
DOI :
https://doi.org/10.38124/ijisrt/25jul1905
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
The rapid advancement of digital technologies has transformed traditional educational paradigms, with Artificial
Intelligence (AI) emerging as a pivotal force in shaping modern classrooms. AI-driven tools such as intelligent tutoring
systems, automated grading, adaptive learning platforms, and virtual teaching assistants are increasingly integrated into
educational ecosystems to enhance both teaching efficacy and student learning experiences. This paper critically analyses
the impact of AI-driven technology integration on classroom teaching and learning dynamics.
The purpose of this study is to evaluate how AI influences pedagogical approaches, reshapes teacher roles, supports
personalized learning, and addresses challenges such as student engagement, equity, and ethical concerns. The research
draws from an extensive review of contemporary academic literature, case studies, and real-world examples across various
educational settings to assess the multifaceted implications of AI adoption in classrooms.
Findings reveal that AI holds significant potential to personalize education, automate administrative burdens, and
support differentiated instruction. However, the integration of AI also presents challenges related to data privacy, teacher
de-skilling, unequal access to technology, and algorithmic bias. The paper underscores the need for a balanced and ethical
framework for AI implementation, supported by continuous teacher training and equitable access strategies.
This critical analysis provides insights for educators, policymakers, and technologists aiming to leverage AI responsibly
in shaping the future of digital education.
Keywords :
AI in Education, Digital Learning, Classroom Teaching, Edtech, Personalized Learning, Adaptive Learning, Ethical AI.
References :
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- Chen, X., Xie, H., Cheng, S., & Wang, F. (2020). A review of artificial intelligence in education. Educational Research Review, 31, 100358.
- Garcia, E., & Singh, A. (2021). Ethical implications of AI in digital classrooms. Journal of Educational Technology Systems, 50(1), 32–47.
- Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
- Jordan, S., & Mitchell, T. (2020). Automated assessment and feedback in education. Assessment & Evaluation in Higher Education, 45(2), 165–181.
- Kumar, S., & Raj, A. (2021). Bridging the AI access gap in developing countries. International Journal of Educational Development, 82, 102377.
- Lee, S. (2020). Natural language processing in educational AI: Challenges and applications. Computer Applications in Education, 29(3), 211–225.
- Li, X., & Zhang, Y. (2021). Adaptive learning systems: A machine learning approach to personalized education. Computers & Education, 163, 104083.
- Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
- Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.
- Nguyen, H., & Brown, C. (2022). Inequity in AI-based education: A global south perspective. Education and Information Technologies, 27(5), 6253–6271.
- Patel, R., Sharma, K., & Desai, M. (2023). Teacher training for AI integration: A framework for professional development. Journal of Digital Learning, 9(2), 44–59.
- Piaget, J. (1972). The psychology of the child. Basic Books.
- Singh, R., & Chen, X. (2022). Data ethics and surveillance in AI-mediated classrooms. AI & Society, 37(2), 417–429.
- Smith, J., & Lee, A. (2021). AI-enabled teacher productivity: Myths and realities. Educational Technology & Society, 24(1), 55–66.
- Sinha, S. (2018). Personalized learning with Khan Academy: A case study. Journal of Learning Analytics, 5(3), 45–58.
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
- Wang, Y., & Garcia, M. (2021). Evaluating AI's long-term impact on student performance. Computers in Human Behavior, 117, 106666.
- Woolf, B. P., Burleson, W., Arroyo, I., Dragon, T., Cooper, D. G., & Picard, R. W. (2021). Affect-aware tutors: Recognizing and responding to student affect. International Journal of Artificial Intelligence in Education, 31(3), 399–421.
- Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence in education: The role of educational technology. International Journal of Educational Technology in Higher Education, 16(1), 1–27.
The rapid advancement of digital technologies has transformed traditional educational paradigms, with Artificial
Intelligence (AI) emerging as a pivotal force in shaping modern classrooms. AI-driven tools such as intelligent tutoring
systems, automated grading, adaptive learning platforms, and virtual teaching assistants are increasingly integrated into
educational ecosystems to enhance both teaching efficacy and student learning experiences. This paper critically analyses
the impact of AI-driven technology integration on classroom teaching and learning dynamics.
The purpose of this study is to evaluate how AI influences pedagogical approaches, reshapes teacher roles, supports
personalized learning, and addresses challenges such as student engagement, equity, and ethical concerns. The research
draws from an extensive review of contemporary academic literature, case studies, and real-world examples across various
educational settings to assess the multifaceted implications of AI adoption in classrooms.
Findings reveal that AI holds significant potential to personalize education, automate administrative burdens, and
support differentiated instruction. However, the integration of AI also presents challenges related to data privacy, teacher
de-skilling, unequal access to technology, and algorithmic bias. The paper underscores the need for a balanced and ethical
framework for AI implementation, supported by continuous teacher training and equitable access strategies.
This critical analysis provides insights for educators, policymakers, and technologists aiming to leverage AI responsibly
in shaping the future of digital education.
Keywords :
AI in Education, Digital Learning, Classroom Teaching, Edtech, Personalized Learning, Adaptive Learning, Ethical AI.