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Paper ID:
IJSREM65469
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence and Machine Learning
V.V.P. Polytechnic, Solapur
Artificial Intelligence and Machine Learning
V.V.P. Polytechnic, Solapur
Artificial Intelligence and Machine Learning
V.V.P. Polytechnic, Solapur
Artificial Intelligence and Machine Learning
V.V.P. Polytechnic, Solapur
Artificial intelligence and machine learning have moved from research labs into ordinary classrooms faster than most institutions expected, and education is one of the sectors feeling that shift most directly. This paper looks at how these technologies are reshaping teaching and learning, from adaptive tutoring systems that respond to a student's pace, to automated grading that frees teachers from repetitive paperwork. We trace the basic ideas behind AI and ML, work through the main categories of machine learning and why each one suits a different kind of educational data, and summarise where intelligent systems are already being used inside schools and colleges - tutoring, administrative automation, predictive analytics, and institution-wide planning among them. We then turn to the harder question of what gets lost or put at risk along the way: student data exposed, biased outcomes baked into an algorithm, or learners leaning on AI tools so heavily that they stop building their own problem-solving skills. Drawing on existing literature and the day-to-day experience of running a technical programme, the paper argues that AI works best in education as a support for teachers rather than a stand-in for them, and that adopting these tools without an ethical framework - covering fairness, privacy, human agency, and accountability - is not a sustainable path. The discussion closes with practical considerations for institutions weighing how, and how much, to bring AI into their classrooms, and what safeguards ought to come first.
KEYWORDS
AI and machine learning are already changing how teaching and learning happen, from personalised lesson content to administrative tasks that used to take hours each week. Used well, they free teachers to spend more time on the parts of their job that machines cannot do - mentoring, motivating, and noticing when a student needs something a dashboard will never show. The examples discussed in this paper, from intelligent tutoring to predictive analytics to large-scale curriculum planning, suggest that the technology has already moved well past the experimental stage in many institutions. That said, the gains are not automatic. Data privacy, unequal access to technology, algorithmic bias, and the steady need to retrain educators as tools evolve are all real costs of adoption, and addressing them will take cooperation between policymakers, technologists, and the schools actually using these systems day to day. The institutions that benefit most from AI in the coming years are likely to be the ones that built an ethical framework into their adoption plan from the start, rather than treating it as an afterthought once a problem surfaces. Handled with that kind of care, AI's role in education looks set to keep growing - and to keep raising new questions alongside the opportunities, which is, on balance, a reasonable trade for the improvements already on offer.
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Publication Date
6/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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