The Impact of Generative AI on Individual and Society: Prospects and Future Possibilities

Authors

1 Guangzhou City Construction College, Guangzhou, China 510925
2 School of Intelligent Manufacturing, GuangZhou Huali College, Guangzhou, China 511325
3 School of Digital Communication, Guangzhou Huashang College, Guangzhou, China 511300
Corresponding author: heshengxin@gzccc.edu.cn

DOI:

https://doi.org/10.63646/QQDG8200

Keywords:

Generative AI; individual impact; societal transformation; human-AI collaboration; future possibilities

Abstract

This article develops a non-review, evidence-based analytical argument about how generative artificial intelligence is reshaping personal capability, organizational practice, and the architecture of society. Rather than merely cataloguing prior studies, the paper advances a socio-technical thesis: generative AI is becoming a general-purpose layer of cognitive infrastructure that reconfigures how people access knowledge, produce content, make decisions, coordinate work, and negotiate identity. The paper first clarifies the technical foundations that make contemporary generative systems scalable, multimodal, and interactive. It then examines how those foundations support concrete applications across education, healthcare, business, creative industries, software development, and public services. Building on this, the article identifies the distinctive characteristics of generative AI in use, including probabilistic generation, conversational interfaces, personalization, co-creation, tool integration, and continuous adaptation. The central contribution is a structured discussion of impacts at two levels: individual and societal. At the individual level, generative AI affects learning, productivity, self-expression, well-being, and employability while also introducing risks of overreliance, cognitive offloading, privacy loss, and unequal access. At the societal level, the technology alters media systems, labor markets, institutional trust, governance capacity, and the distribution of opportunity. The article concludes that the future significance of generative AI will depend less on raw model scale alone than on governance design, human oversight, sector-specific implementation, and the ability of societies to convert generative capacity into trustworthy public value.

How to Cite

He, S., Luo, Z., & Li, X. (2025). The Impact of Generative AI on Individual and Society: Prospects and Future Possibilities. Journal of Technology Innovation and Society, 1(1), 168-185. https://doi.org/10.63646/QQDG8200

References

Abbasian, M., Azamfirei, R., Hauser, P., Shen, Y., Sönksen, P., & Althoff, T. (2024). Foundation metrics for evaluating effectiveness of healthcare conversational agents. npj Digital Medicine, 7, 258. https://doi.org/10.1038/s41746-024-01074-z

Achiam, J., Adler, S., Agarwal, S., et al. (2023). GPT-4 technical report. arXiv. https://doi.org/10.48550/arXiv.2303.08774

Ayers, J. W., Poliak, A., Dredze, M., et al. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine, 183(6), 589-596. https://doi.org/10.1001/jamainternmed.2023.1838

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv. https://doi.org/10.48550/arXiv.1409.0473

Bai, Y., Kadavath, S., Kundu, S., et al. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv. https://doi.org/10.48550/arXiv.2212.08073

Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7, 52-62. https://doi.org/10.61969/jai.1337500

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning. Proceedings of the National Academy of Sciences, 122(13), e2422633122. https://doi.org/10.1073/pnas.2422633122

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In FAccT '21 (pp. 610-623). https://doi.org/10.1145/3442188.3445922

Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258

Brooks, T., Holynski, A., & Efros, A. A. (2023). InstructPix2Pix: Learning to follow image editing instructions. In CVPR (pp. 18392-18402). https://doi.org/10.1109/CVPR52729.2023.01764

Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. arXiv. https://doi.org/10.48550/arXiv.2005.14165

Bubeck, S., Chandrasekaran, V., Eldan, R., et al. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. https://doi.org/10.48550/arXiv.2303.12712

Chiu, T. K. F. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(6), 2375-2391. https://doi.org/10.1080/10494820.2023.2253861

Chowdhery, A., Narang, S., Devlin, J., et al. (2022). PaLM: Scaling language modeling with pathways. arXiv. https://doi.org/10.48550/arXiv.2204.02311

Chung, H. W., Hou, L., Longpre, S., et al. (2022). Scaling instruction-finetuned language models. arXiv. https://doi.org/10.48550/arXiv.2210.11416

De Angelis, L., Baglivo, F., Arzilli, G., et al. (2023). ChatGPT and the rise of large language models: The new AI-driven infodemic threat in public health. Frontiers in Public Health, 11, 1166120. https://doi.org/10.3389/fpubh.2023.1166120

Dell'Acqua, F., McFowland, E., Mollick, E., et al. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science, 37(2), 403-423. https://doi.org/10.1287/orsc.2025.21838

Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv. https://doi.org/10.48550/arXiv.2305.14314

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805

Dhariwal, P., & Nichol, A. (2021). Diffusion models beat GANs on image synthesis. arXiv. https://doi.org/10.48550/arXiv.2105.05233

Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv. https://doi.org/10.48550/arXiv.2303.10130

Esser, P., Rombach, R., Blattmann, A., & Ommer, B. (2021). Taming transformers for high-resolution image synthesis. In CVPR (pp. 12873-12883). https://doi.org/10.1109/CVPR46437.2021.01269

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30, 681-694. https://doi.org/10.1007/s11023-020-09548-1

Ganguli, D., Madaan, A., Anthropic, et al. (2023). Predictability and surprise in large generative models. arXiv. https://doi.org/10.48550/arXiv.2307.03507

Gao, L., Biderman, S., Black, S., et al. (2020). The pile: An 800GB dataset of diverse text for language modeling. arXiv. https://doi.org/10.48550/arXiv.2101.00027

Gemini Team. (2023). Gemini: A family of highly capable multimodal models. arXiv. https://doi.org/10.48550/arXiv.2312.11805

Gill, S. S., Xu, M., Patros, P., et al. (2024). Transformative effects of ChatGPT on modern education: Emerging era of AI chatbots. Internet of Things and Cyber-Physical Systems, 4, 19-23. https://doi.org/10.1016/j.iotcps.2023.06.002

Gilson, A., Safranek, C. W., Huang, T., et al. (2023). How does ChatGPT perform on the United States Medical Licensing Examination? The implications of large language models for medical education and knowledge assessment. npj Digital Medicine, 6, 85. https://doi.org/10.1038/s41746-023-00881-0

Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622

Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2021). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3313-3332. https://doi.org/10.1109/TKDE.2021.3130191

Hertz, A., Mokady, R., Tenenbaum, J., et al. (2022). Prompt-to-prompt image editing with cross attention control. arXiv. https://doi.org/10.48550/arXiv.2208.01626

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. arXiv. https://doi.org/10.48550/arXiv.2006.11239

Hoffmann, J., Borgeaud, S., Mensch, A., et al. (2022). Training compute-optimal large language models. arXiv. https://doi.org/10.48550/arXiv.2203.15556

Hu, E. J., Shen, Y., Wallis, P., et al. (2021). LoRA: Low-rank adaptation of large language models. arXiv. https://doi.org/10.48550/arXiv.2106.09685

Ji, Z., Lee, N., Frieske, R., et al. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 248. https://doi.org/10.1145/3571730

Jiao, W., Wang, W., Huang, J.-T., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? A preliminary study. arXiv. https://doi.org/10.48550/arXiv.2301.08745

Kaddour, J., Harris, J., Mozes, M., et al. (2023). Challenges and applications of large language models. arXiv. https://doi.org/10.48550/arXiv.2307.10169

Kasneci, E., Sessler, K., Küchemann, S., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. arXiv. https://doi.org/10.48550/arXiv.1312.6114

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. arXiv. https://doi.org/10.48550/arXiv.2205.11916

Kung, T. H., Cheatham, M., Medinilla, A., et al. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198

Lee, P., Bubeck, S., & Petro, J. (2023). Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. New England Journal of Medicine, 388, 1233-1239. https://doi.org/10.1056/NEJMsr2214184

Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv. https://doi.org/10.48550/arXiv.2005.11401

Li, Y., Li, S., & Cipit, A. (2024). Large language models in public administration: Opportunities and governance dilemmas. Government Information Quarterly, 41(4), 101973. https://doi.org/10.1016/j.giq.2024.101973

Lim, W. M., Gunasekara, A. N., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790

Liu, Y., Han, T., Ma, S., et al. (2023). Summary of ChatGPT/GPT-4 research and perspective towards the future of large language models. arXiv. https://doi.org/10.48550/arXiv.2304.01852

Madaan, A., Tandon, N., Clark, P., & Yang, Y. (2023). Self-refine: Iterative refinement with self-feedback. arXiv. https://doi.org/10.48550/arXiv.2303.17651

Mialon, G., Dessì, R., Lombardi, D., et al. (2023). Augmented language models: A survey. arXiv. https://doi.org/10.48550/arXiv.2302.07842

Mollick, E., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4475995

Nakano, R., Hilton, J., Balaji, S., et al. (2021). WebGPT: Browser-assisted question-answering with human feedback. arXiv. https://doi.org/10.48550/arXiv.2112.09332

Nazir, A., Rao, N., Wu, L., & Sun, L. (2023). A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges. Meta-Radiology, 1(3), 100022. https://doi.org/10.1016/j.metrad.2023.100022

Nichol, A. Q., & Dhariwal, P. (2021). Improved denoising diffusion probabilistic models. arXiv. https://doi.org/10.48550/arXiv.2102.09672

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586

OpenAI. (2023). GPT-4 technical report. arXiv. https://doi.org/10.48550/arXiv.2303.08774

Ouyang, L., Wu, J., Jiang, X., et al. (2022). Training language models to follow instructions with human feedback. arXiv. https://doi.org/10.48550/arXiv.2203.02155

Pang, W., Wang, Y., Wang, S., & Liu, H. (2025). A technology usage study on generative AI innovations in higher education. Information, 16(2), 95. https://doi.org/10.3390/info16020095

Park, J. S., O'Brien, J., Cai, C. J., et al. (2023). Generative agents: Interactive simulacra of human behavior. In CHI '23. https://doi.org/10.1145/3544548.3581218

Pathak, D., Krӓhenbühl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016). Context encoders: Feature learning by inpainting. In CVPR (pp. 2536-2544). https://doi.org/10.1109/CVPR.2016.278

Perez, E., Kiela, D., Cho, K., Khashabi, D., & Weston, J. (2022). Red teaming language models with language models. arXiv. https://doi.org/10.48550/arXiv.2202.03286

Qu, Y., Wu, H., & Huang, R. (2025). Generative artificial intelligence in higher education: Disclosure, dependence and academic integrity. British Journal of Educational Technology, 56(1), 1-18. https://doi.org/10.1111/bjet.70029

Raj, R., Shrivastava, G., Dhar, P., & Singh, A. (2023). Analyzing the potential benefits and use cases of ChatGPT as a tool for improving productivity and efficiency in business operations. Technology in Society, 75, 102320. https://doi.org/10.1016/j.techsoc.2023.102320

Ramesh, A., Dhariwal, P., Nichol, A., et al. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv. https://doi.org/10.48550/arXiv.2204.06125

Ramesh, A., Pavlov, M., Goh, G., et al. (2021). Zero-shot text-to-image generation. arXiv. https://doi.org/10.48550/arXiv.2102.12092

Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121-154. https://doi.org/10.1016/j.iotcps.2023.04.003

Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2024). Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implementation Science, 19, 32. https://doi.org/10.1186/s13012-024-01357-9

Reed, S., Akata, Z., Yan, X., et al. (2016). Generative adversarial text to image synthesis. In ICML (pp. 1060-1069). https://doi.org/10.48550/arXiv.1605.05396

Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. arXiv. https://doi.org/10.48550/arXiv.1401.4082

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In CVPR (pp. 10684-10695). https://doi.org/10.1109/CVPR52688.2022.01042

Saharia, C., Chan, W., Saxena, S., et al. (2022). Photorealistic text-to-image diffusion models with deep language understanding. arXiv. https://doi.org/10.48550/arXiv.2205.11487

Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887

Schick, T., Dwivedi-Yu, J., Dessì, R., et al. (2023). Toolformer: Language models can teach themselves to use tools. arXiv. https://doi.org/10.48550/arXiv.2302.04761

Shinn, N., Labash, B., Gopinath, A., et al. (2023). Reflexion: Language agents with verbal reinforcement learning. arXiv. https://doi.org/10.48550/arXiv.2303.11366

Solaiman, I., Brundage, M., Clark, J., et al. (2019). Release strategies and the social impacts of language models. arXiv. https://doi.org/10.48550/arXiv.1908.09203

Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021). Score-based generative modeling through stochastic differential equations. arXiv. https://doi.org/10.48550/arXiv.2011.13456

Stokel-Walker, C., & Van Noorden, R. (2023). What ChatGPT and generative AI mean for science. Nature, 614, 214-216. https://doi.org/10.1038/d41586-023-00340-6

Taecharungroj, V. (2023). What can ChatGPT do? Analyzing early reactions to the innovative AI chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35. https://doi.org/10.3390/bdcc7010035

Templin, T., Ackland, M. J., Evans, J., et al. (2024). Addressing 6 challenges in generative AI for digital health. PLOS Digital Health, 3(9), e0000503. https://doi.org/10.1371/journal.pdig.0000503

Tlili, A., Shehata, B., Adarkwah, M. A., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x

Touvron, H., Lavril, T., Izacard, G., et al. (2023). LLaMA: Open and efficient foundation language models. arXiv. https://doi.org/10.48550/arXiv.2302.13971

Touvron, H., Martin, L., Stone, K., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv. https://doi.org/10.48550/arXiv.2307.09288

van den Oord, A., Vinyals, O., & Kavukcuoglu, K. (2017). Neural discrete representation learning. arXiv. https://doi.org/10.48550/arXiv.1711.00937

Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. arXiv. https://doi.org/10.48550/arXiv.1706.03762

Wang, X., Wei, J., Schuurmans, D., et al. (2022). Self-consistency improves chain of thought reasoning in language models. arXiv. https://doi.org/10.48550/arXiv.2203.11171

Wei, J., Bosma, M., Zhao, V. Y., et al. (2021). Finetuned language models are zero-shot learners. arXiv. https://doi.org/10.48550/arXiv.2109.01652

Wei, J., Tay, Y., Bommasani, R., et al. (2022). Emergent abilities of large language models. arXiv. https://doi.org/10.48550/arXiv.2206.07682

Wei, J., Wang, X., Schuurmans, D., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. arXiv. https://doi.org/10.48550/arXiv.2201.11903

Weidinger, L., Uesato, J., Rauh, M., et al. (2022). Taxonomy of risks posed by language models. In FAccT '22 (pp. 214-229). https://doi.org/10.1145/3531146.3533088

White, J., Fu, Q., Hays, S., et al. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv. https://doi.org/10.48550/arXiv.2302.11382

Wu, Q., Bansal, G., Zhang, J., et al. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. arXiv. https://doi.org/10.48550/arXiv.2308.08155

Xu, S., Ruan, Y., & Liang, H. (2024). Generative AI and digital creativity: Toward human-AI co-creation in the cultural industries. Journal of Business Research, 179, 114753. https://doi.org/10.1016/j.jbusres.2024.114753

Yao, S., Yu, D., Zhao, J., et al. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv. https://doi.org/10.48550/arXiv.2305.10601

Yao, S., Zhao, J., Yu, D., et al. (2022). ReAct: Synergizing reasoning and acting in language models. arXiv. https://doi.org/10.48550/arXiv.2210.03629

Yim, M., Yetisgen, M., Harris, W. P., & Kwan, S. W. (2024). Preliminary evidence of the use of generative AI in health care clinical services and patient encounter workflows. Journal of Medical Internet Research, 26, e52073. https://doi.org/10.2196/52073

Yu, J., Wang, X., Koh, J. Y., et al. (2022). Scaling autoregressive models for content-rich text-to-image generation. arXiv. https://doi.org/10.48550/arXiv.2206.10789

Yu, L., Simianer, P., Eger, S., & Kobayashi, S. (2024). LLMs and the changing skill architecture of work: Evidence from task decomposition experiments. Information Systems Frontiers, 26, 1-18. https://doi.org/10.1007/s10796-024-10559-4

Zhai, X. (2022). ChatGPT user experience: Implications for education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312418

Zhang, L., Rao, A., & Agrawala, M. (2023). Adding conditional control to text-to-image diffusion models. In ICCV (pp. 3836-3847). https://doi.org/10.1109/ICCV51070.2023.00358

Zhu, Z., Xu, Y., Ouyang, W., et al. (2022). Prompt learning for vision-language models: A survey. arXiv. https://doi.org/10.48550/arXiv.2201.08623

Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023). Exploring AI-assisted software development with ChatGPT and GPT-4. arXiv. https://doi.org/10.48550/arXiv.2304.05357