Exploring the Ethical and Practical Implications of AI in Psychology Education and Training within State Universities and Colleges

Maria Mamba; Jesusa Naui; Jona Cambri; Helena Daya; Sylvia Leigh Quigao; Leinard Bangayan

doi:10.63931/ijchr.v7iSI3.321

Authors

Maria Mamba Cagayan State University, Tuguegarao City https://orcid.org/0000-0002-6676-6151
Jesusa Naui Nueva Vizcaya State University https://orcid.org/0009-0004-2124-8838
Jona Cambri Cagayan State University, Tuguegarao City https://orcid.org/0000-0002-0377-7646
Helena Daya Southern Luzon State University
Sylvia Leigh Quigao https://orcid.org/0009-0004-9518-6502
Leinard Bangayan Cagayan State University https://orcid.org/0000-0002-0140-3034

DOI:

https://doi.org/10.63931/ijchr.v7iSI3.321

Keywords:

state universities and colleges, artificial intelligence, psychology education, ethical and practical implications, AI tools

Abstract

The rapid advancement of Artificial Intelligence (AI) in education has created new opportunities for innovation, particularly in psychology where teaching methodologies, curriculum development, and learning personalization are being transformed. AI-powered tools such as adaptive learning systems, intelligent tutoring platforms, and data-driven feedback mechanisms provide educators with powerful means to enhance student engagement, identify learning challenges, and deliver tailored support. Despite these benefits, the integration of AI into psychology education presents profound ethical and practical challenges that must be critically addressed. Concerns regarding the protection of sensitive psychological data, the potential for algorithmic bias, and the issue of fairness in access remain central to debates surrounding AI adoption in higher education. This study investigated the ethical dilemmas and practical applications of AI in psychology education within state universities and colleges, with the goal of developing a framework for responsible and inclusive integration. Using qualitative and mixed-method approaches such as interviews with faculty and students, surveys, and an extensive review of existing literature, the research identified recurring ethical issues, examined the effectiveness of AI tools in improving learning outcomes, and formulated guidelines for institutions. The findings underscore the importance of robust data governance policies to safeguard privacy, systematic measures to mitigate bias in AI-driven assessments, and equitable strategies to ensure all students benefit from these technologies regardless of socioeconomic background. By aligning AI adoption with institutional objectives, ethical standards, and student-centered practices, this study contributes to the promotion of a responsible, transparent, and inclusive use of AI in psychology education.

References

[1] Binns, R. (2021). On the apparent conflict between individual and group fairness. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 514–524. https://doi.org/10.1145/3442188.3445897 DOI: https://doi.org/10.1145/3351095.3372864

[2] Chen, X., Xie, H., Zou, D., & Hwang, G. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002 DOI: https://doi.org/10.1016/j.caeai.2020.100002

[3] Chen, X., Zhang, L., & Zhao, T. (2020). Artificial intelligence in education: Applications and ethical challenges. Educational Technology & Society, 23(4), 56–67.

[4] Crawford, K., & Paglen, T. (2019). Excavating AI: The politics of images in machine learning training sets. International Journal of Communication, 13, 1–40.

[5] Cui, G., & Zhang, Y. (2022). The digital divide in education: AI applications and access inequalities. Education and Information Technologies, 27(3), 3317–3335. https://doi.org/10.1007/s10639-021-10737-3 DOI: https://doi.org/10.1007/s10639-021-10737-3

[6] Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 DOI: https://doi.org/10.1007/s11023-018-9482-5

[7] Haider, Y., & Frensch, P. (2022). AI-driven tools in psychology education: A review of opportunities and challenges. Psychology Learning & Teaching, 21(3), 145–159. https://doi.org/10.1177/14757257221031522

[8] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

[9] Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., … Santos, O. C. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 31(4), 641–662. https://doi.org/10.1007/s40593-021-00239-7

[10] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.

[11] Mehrabi, N., Morstatter, F., & Lerman, K. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607 DOI: https://doi.org/10.1145/3457607

[12] Smith, J., Green, A., & White, P. (2019). Data privacy in education: Ensuring security in the age of AI. Journal of Educational Technology, 18(2), 112–130.

[13] Xu, K., & Yang, H. (2020). Innovative pedagogies and AI: Transforming curriculum design. Computers and Education, 149, 103832. https://doi.org/10.1016/j.compedu.2020.103832 DOI: https://doi.org/10.1016/j.compedu.2020.103832

[14] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0