This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Mona Quaisar,
Naved Ahmed,
- Professor, Department of Electronics and Communication Engineering, Al-Kabir Institute of Management & Technology, Jharkhand, India
- Professor, Department of Electronics and Communication Engineering, Al-Kabir Institute of Management & Technology, Jharkhand, India
Abstract
Artificial Intelligence (AI) is moving quickly from a supporting tool to systems that can learn, decide, and act with very little human help. In the coming years, if Super intelligent AI becomes common, it can strongly affect education, employment, and social mobility. This paper explains these changes in a simple way, focusing on how powerful language-based AI can reshape learning and work. In education, AI can provide personalized learning plans, explain topics in local languages, and check assignments quickly. Virtual tutors can be available anytime, and teachers can use AI to track student progress and give better support. However, over-dependence on AI may reduce practice of basic skills, and unequal access to devices and the internet can create a new learning gap. In jobs, routine work that depends on reading, writing, reporting, customer support, and standard decision- making may decline. At the same time, new roles may grow in AI design, data handling, safety testing, auditing, and human–AI coordination. Workers will need continuous upskilling, not just one-time training in college. For social mobility, AI can help people from small towns learn faster and access better jobs, but only if training, infrastructure, and fair policies are provided. This study highlights the need to protect human values like creativity, emotional intelligence, and ethical judgment while planning an inclusive AI-driven future.
Keywords: AI evolution, foundation models, education, employment, social mobility
References
[1] Dario Amodei, Chris Olah, Jacob Steinhardt, et al. Concrete problems in ai safety. arXiv preprint
arXiv:1606.06565, 2016.
1
[2] Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al. On the opportunities and risks of foundation
models. arXiv preprint arXiv:2108.07258, 2021.
[3] Nick Bostrom. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014.
[4] Tom B. Brown, Benjamin Mann, Nick Ryder, et al. Language models are few-shot learners. Advances
in Neural Information Processing Systems (NeurIPS), 2020.
[5] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep
bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019.
[6] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolu-
tional neural networks. In Advances in Neural Information Processing Systems (NeurIPS), 2012.
[7] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to
document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
[8] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521:436–444, 2015.
[9] Frank Rosenblatt. The perceptron: A probabilistic model for information storage and organization in
the brain. Psychological Review, 65(6):386–408, 1958.
[10] David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by back-
propagating errors. In Nature, volume 323, pages 533–536, 1986.
[11] Stuart Russell. Human Compatible: Artificial Intelligence and the Problem of Control. Viking, 2019.
[12] Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 3 edition,
2010.
[13] Alan M. Turing. Computing machinery and intelligence. Mind, 59(236):433–460, 1950.
[14] Ashish Vaswani, Noam Shazeer, Niki Parmar, et al. Attention is all you need. In Advances in Neural
Information Processing Systems (NeurIPS), 2017.
[15] Jason Wei et al. Chain-of-thought prompting elicits reasoning in large language models. arXiv preprint
arXiv:2201.11903, 2022.
Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 18/04/2026 | |
| Accepted | 21/07/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 165 Days |