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REVIEW ARTICLE
Generalized psychology and human wellness in the era of artificial intelligence: A review

Shuyi Jia1, Yanming Ren1, Michael Shengtao Wu2,*, Kaiping Peng3,*

1 Faculty of Health and Wellness, City University of Macau, Macau 999078, China

2 School of Philosophy and Sociology, Jilin University, Changchun 130015, Jilin Proviince, China

3 Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 10084, China



Well-being Sciences Review 2026, 2(2),93-101; https://doi.org/10.54844/wsr.2025.1113
Submitted08 Dec 2025
Revised23 Dec 2025
Accepted30 Dec 2025
Published30 Apr 2026
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Cite This Article
Abstract

As the development of artificial intelligence (AI) advances, a generalized psychology is emerging that encompasses the minds and behaviors of humans, animals, and intelligent agents; in the AI era, cognition and well-being are increasingly shaped by human-AI interaction, so the framework of a generalized object of psychology must expand from human individuals and animals to intelligent agents and human wellness. Moreover, we highlight human advantages in the AI era, including Aesthetics, Creativity, and Empathy (ACE), along with their measurable outcomes and boundary conditions. Regarding AI for wellness, we then explore hybrid intelligence across multiple domains such as mental health, healthcare, education, work, and social relationships. In addition, we discuss cross-cutting governance considerations, including trust calibration, bias control, privacy-by-design, and cultural alignment, in line with emerging global guidance and reporting standards. Overall, we aim to link theory, measures, applications, and governance to verifiable gains in human wellness.

REFERENCES

Adler, J. M., Lodi-Smith, J., Philippe, F. L., & Houle, I. (2016). The incremental validity of narrative identity in predicting well-being: A review of the field and recommendations for the future. Personality and Social Psychology Review, 20(2), 142–175. https://doi.org/10.1177/1088868315585068

Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: progress, challenges, and future directions. Humanities and Social Sciences Communications, 11, 1568. https://doi.org/10.1057/s41599-024-04044-8

Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166

Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., & Smith, D. M. (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

Baillifard, A., Gabella, M., Lavenex, P. B., & Martarelli, C. S. (2025). Effective learning with a personal AI tutor: A case study. Education and Information Technologies, 30(1), 297–312. https://doi.org/10.1007/s10639-024-12888-5

Baumeister, R. F. (2011). Self and identity: A brief overview. In M. R. Leary & J. P. Tangney (Eds.), Handbook of self and identity (2nd ed., pp. 69–81). Guilford Press.

Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., Christensen, A., & Seli, P. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research, 8(1), 42. https://doi.org/10.1186/s41235-023-00499-6

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044

Bryson, J. J. (2010). Robots should be slaves. In Y. Wilks (Ed.), Close engagements with artificial companions: Key social, psychological, ethical and design issues (pp. 63–74). John Benjamins Publishing.

Bryson, J. J. (2019). The past decade and future of AI's impact on society. In Turner (Ed.), Towards a new enlightenment? A transcendent decade (Vol. 11). Turner.

Chen, J., & Bornstein, A. M. (2024). The causal structure and computational value of narratives. Trends in Cognitive Sciences, 28(8), 769–781. https://doi.org/10.1016/j.tics.2024.04.003

Clark, A. (2003). Natural-born cyborgs: Minds, technologies, and the future of human intelligence. Oxford University Press.

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., Boulesteix, A.-L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., ... Logullo, P. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, e078378. https://doi.org/10.1136/bmj-2023-078378

De Freitas, J., Uğuralp, A. K., Uğuralp, Z., & Puntoni, S. (2025). AI companions reduce loneliness. Journal of Consumer Research. https://doi.org/10.1093/jcr/ucaf040

Dekker, I., De Jong, E. M., Schippers, M. C., De Bruijn-Smolders, M., Alexiou, A., & Giesbers, B. (2020). Optimizing students' mental health and academic performance: AI-enhanced life crafting. Frontiers in Psychology, 11, 1063. https://doi.org/10.3389/fpsyg.2020.01063

Delello, J. A., Sung, W., Mokhtari, K., Hebert, J., Bronson, A., & De Giuseppe, T. (2025). AI in the classroom: Insights from educators on usage, challenges, and mental health. Education Sciences, 15(2), 113. https://doi.org/10.3390/educsci15020113

Demszky, & D., Liu J. (2023). M-powering teachers: Natural language processing powered feedback improves 1:1 instruction and student outcomes. The Tenth ACM Conference on Learning @ Scale, 59–69. https://doi.org/10.1145/3573051.3593379

Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., Jones, M., Krettek-Cobb, D., Lai, L., Jones-Mitchell, N., Ong, D. C., Dweck, C. S., Gross, J. J., & Pennebaker, J. W. (2023). Using large language models in psychology. Nature Reviews Psychology, 2, 688–701. https://doi.org/10.1038/s44159-023-00241-5

de Vries, L. P., Baselmans, B. M. L., & Bartels, M. (2021). Smartphone-based ecological momentary assessment of well-being: A systematic review and recommendations for future studies. Journal of Happiness Studies, 22(5), 2361–2408. https://doi.org/10.1007/s10902-020-00324-7

Diener, E., Lucas, R. E., & Oishi, S. (2018). Advances and open questions in the science of subjective well-being. Collabra: Psychology, 4(1), 15. https://doi.org/10.1525/collabra.115

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290

Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115(44), 11203–11208. https://doi.org/10.1073/pnas.1802331115

Epstein, Z., & Hertzmann, A. (2023). Art and the science of generative AI. Science, 380, 1110–1111. https://doi.org/10.1126/science.adh4451

Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785

Gao, X., & Feng, H. (2023). AI-driven productivity gains: Artificial intelligence and firm productivity. Sustainability, 15(11), 8934. https://doi.org/10.3390/su15118934

Ghafouri, M. (2024). ChatGPT: The catalyst for teacher-student rapport and grit development in L2 class. System, 120, 103209. https://doi.org/10.1016/j.system.2023.103209

Horton, C. B. Jr, White, M. W., & Iyengar, S. S. (2023). Bias against AI art can enhance perceptions of human creativity. Scientific Reports, 13, 19001. https://doi.org/10.1038/s41598-023-45202-3

Hubert, K. F., Awa, K. N., & Zabelina, D. L. (2024). The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks. Scientific Reports, 14(1), 3440. https://doi.org/10.1038/s41598-024-53303-w

Ke, L., Tong, S., Cheng, P., & Peng, K. (2025). Exploring the frontiers of LLMs in psychological applications: A comprehensive review. Artificial Intelligence Review, 58(10), 305. https://doi.org/10.1007/s10462-025-11297-5

Koivisto, M., & Grassini, S. (2023). Best humans still outperform artificial intelligence in a creative divergent thinking task. Scientific Reports, 13, 13601. https://doi.org/10.1038/s41598-023-40858-3

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837

Li, X., Li, Y., Liu, L., Bing, L., & Joty, S. (2022). Does GPT-3 demonstrate psychopathy? Evaluating large language models from a psychological perspective. ArXiv, arXiv:2212.10529v3. https://doi.org/10.48550/arXiv.2212.10529

Löchner, J., Carlbring, P., Schuller, B., Torous, J., & Sander, L. B. (2025). Digital interventions in mental health: An overview and future perspectives. Internet Interventions, 40, 100824. https://doi.org/10.1016/j.invent.2025.100824

Maghsudi, S., Lan, A., Xu, J., & van der Schaar, M. (2021). Personalized education in the artificial intelligence era: What to expect next. IEEE Signal Processing Magazine, 38(3), 37–50. https://doi.org/10.1109/MSP.2021.3055032

Mihalcea, R., Ignat, O., Bai, L., Borah, A., Chiruzzo, L., Jin, Z., Kwizera, C., Nwatu, J., Poria, S., & Solorio, T. (2025). Why AI is WEIRD and shouldn't be this way: Towards AI for everyone, with everyone, by everyone. Proceedings of the AAAI Conference on Artificial Intelligence, 39(27), 28657–28670. https://doi.org/10.1609/aaai.v39i27.35092

Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans (1st ed.). Farrar, Straus and Giroux.

Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23–47. https://doi.org/10.1146/annurev-clinpsy-032816-044949

Nannicelli, T. (2025). Mass AI-art: A moderately skeptical perspective. The Journal of Aesthetics and Art Criticism, kpaf026. https://doi.org/10.1093/jaac/kpaf026

Ni, Y., & Jia, F. (2025). A scoping review of AI-driven digital interventions in mental health care: Mapping applications across screening, support, monitoring, prevention, and clinical education. Healthcare, 13(10), 1205. https://doi.org/10.3390/healthcare13101205

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

Onyebuchi, N., Ayeni, O., Hamad, N., Osawaru, B., & Adewusi, O. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18, 261–271. https://doi.org/10.30574/gscarr.2024.18.2.0062

Rashkin, H., Smith, E. M., Li, M., & Boureau, Y. L. (2019). Towards empathetic open-domain conversation models: A new benchmark and dataset. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 5370–5381. https://doi.org/10.18653/v1/p19-1534

Reis, M., Reis, F., & Kunde, W. (2024). Influence of believed AI involvement on the perception of digital medical advice. Nature Medicine, 30(11), 3098–3100. https://doi.org/10.1038/s41591-024-03180-7

Roshanaei, M., Olivares, H., & Lopez, R. R. (2023). Harnessing AI to foster equity in education: Opportunities, challenges, and emerging strategies. Journal of Intelligent Learning Systems and Applications, 15(4), 123–143. https://doi.org/10.4236/jilsa.2023.154009

Song, X., & Lin, Z. (2025). Beyond the existence-utility binary: How AI reveals our hybrid self. AI & Society. https://doi.org/10.1007/s00146-025-02521-5

Sorin, V., Brin, D., Barash, Y., Konen, E., Charney, A., Nadkarni, G., & Klang, E. (2024). Large language models and empathy: Systematic review. Journal of Medical Internet Research, 26, e52597. https://doi.org/10.2196/52597

Swann, W. B. Jr, & Buhrmester, M. D. (2012). Self as functional fiction. Social Cognition, 30(4), 415–430. https://doi.org/10.1521/soco.2012.30.4.415

Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024). Cultural bias and cultural alignment of large language models. PNAS Nexus, 3(9), pgae346. https://doi.org/10.1093/pnasnexus/pgae346

Tang, X. F., Wang, C. M., Sun, X. D., & Zhang, E. Z. (2025). Impact of trusting humanoid intelligent robots on employees' job dedication intentions: An investigation based on the classification of human-robot trust. Acta Psychologica Sinica, 57(11), 1933–1950. https://doi.org/10.3724/SP.J.1041.2025.1933

Tang, Z., & Liao, J. (2025). Unlocking emotional resilience: Exploring the impact of AI-enhanced support systems on EFL teachers' burnout and EFL students' well-being in modern classrooms. Acta Psychologica, 260, 105672. https://doi.org/10.1016/j.actpsy.2025.105672

Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., Anandkumar, A., Bergen, K., Gomes, C. P., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T. Y., Manrai, A., Marks, D., Ramsundar, B., Song, L., Sun, J., Tang, J., Veličković, P., Welling, M., Zhang, L., Coley, C. W., Bengio, Y., & Zitnik, M. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47–60. https://doi.org/10.1038/s41586-023-06221-2

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167

Wang, Y., Li, X., Zhang, Q., Yeung, D., & Wu, Y. (2025). Effect of a cognitive behavioral therapy-based AI chatbot on depression and loneliness in Chinese university students: Randomized controlled trial with financial stress moderation. JMIR MHealth and UHealth, 13, e63806. https://doi.org/10.2196/63806

World Health Organization. (2024). Ethics and governance of artificial intelligence for health: Guidance on large multimodal models. World Health Organization. Retrieved Dec. 29, 2025, from https://iris.who.int/handle/10665/376977

Wu, M. S., & Peng, K. P. (2025). Human advantages and psychological transformations in the era of artificial intelligence. Acta Psychologica Sinica, 57(11), 1879–1884. https://doi.org/10.3724/SP.J.1041.2025.1879

Wundt, W. (1897). Outlines of psychology (C. H. Judd, Trans.). W. Engelmann. (Original work published 1896)

Xu, L., Zhao, Y., & Yu, F. (2025). Employees adhere less to advice on moral behavior from artificial intelligence supervisors than human. Acta Psychologica Sinica, 57(11), 2060–2082. https://doi.org/10.3724/SP.J.1041.2025.2060

Yang, F., Chen, Z., Jiang, Z., Cho, E., Huang, X., & Lu, Y. (2023). PALR: Personalization aware LLMs for recommendation. ArXiv, arXiv:2305.07622. https://doi.org/10.48550/arXiv.2305.07622

You, J. K. (2021). A critique of the "as–if" approach to machine ethics. AI and Ethics, 1, 545–552. https://doi.org/10.1007/s43681-021-00070-3

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J. B., & Yuan, J., Li Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021(1), 8812542. https://doi.org/10.1155/2021/8812542

Zhang, T., & Liu, X. (2025). Tracking the evolving impact of AI-driven learning platforms on EFL students' burnout, emotional challenges, and well-being: A longitudinal growth curve analysis. Innovation in Language Learning and Teaching, 1–21. https://doi.org/10.1080/17501229.2025.2503889

Zhao, Y., & Sun, P. (2025). Artificial intelligence in the promotion of human well-being: Current trends and future directions. Well-Being Sci Rev, 1(1), 3–8. https://doi.org/10.54844/wsr.2025.0978

Zhou, L., Gao, J., Li, D., & Shum, H. (2020). The design and implementation of XiaoIce, an empathetic social chatbot. Computational Linguistics, 46(1), 53–93. https://doi.org/10.1162/coli_a_00368


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