Using Generative AI to Support Inclusion: Insights from a Psychological Perspective

Authors

  • Melody M Terras

    Division of Psychology and Social Work, School of Education and Social Sciences, University of the West of Scotland, PA1 2BE Paisley, UK

  • Lynne Beveridge

    Division of Psychology and Social Work, School of Education and Social Sciences, University of the West of Scotland, PA1 2BE Paisley, UK

  • Graham S Scott

    Division of Psychology and Social Work, School of Education and Social Sciences, University of the West of Scotland, PA1 2BE Paisley, UK

  • Naeem Ramzan

    Division of Computing, School of Computing, Engineering and Physical Sciences, University of the West of Scotland, PA1 2BE Paisley, UK

DOI:

https://doi.org/10.30564/jiep.v8i2.12254
Received: 21 July 2025 | Revised: 5 September 2025 | Accepted: 13 September 2025 | Published Online: 20 September 2025

Abstract

This article considers the insights of viewing (Artificial Intelligence) AI personalization from a psychological perspective by offering detailed consideration of the psychological constraints, both cognitive and socio-emotional, to the current and future application of personalized AI solutions in higher educational settings. It maps the relationship between AI personalized solutions and psychological substrates to illustrate how and why AI is an effective way to promote inclusion. Consideration of psychological infrastructure and the skills that support effective learning highlights the importance of individual differences and reminds us that diversity is present in all students, not only those with disabilities. Learner profiles are variable, and the application of a psychological lens provides a way to conceptualise and operationalise this variability by utilising the concept of psychological barriers and enablers. Psychological attributes such as working memory, executive function, metacognition, self-esteem and motivation can influence learning in a positive or negative manner depending on the degree to which they are present; for example, low working memory capacity is a barrier, but high capacity is an enabler. This variability allows them to be targeted and developed. Human intelligence, unlike artificial intelligence, is psychologically constrained, so AI personalisation must be psychologically informed to maximise its educational and inclusive potential.

Keywords:

Psychology; Personalisation; AI; Inclusion

References

[1] Bhullar, P.S., Joshi, M., Chugh, R., 2024. ChatGPT in higher education-a synthesis of the literature and a future research agenda. Education and Information Technologies. 29(16), 21501–21522.

[2] 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. DOI: https://doi.org/10.1016/j.lindif.2023.102274

[3] Beveridge, L., Scott, G.G., Terras, M., 2025. AI for good in HE? Leveraging AI and GenAI for note-taking and studying from lecture material. The Journal of Inclusive Practice in Further and Higher Education. 17(1), 157–169.

[4] Disabled Students UK (DSUK), 2024. The 2024 Access Insights Report. Available from: https://disabledstudents.co.uk/wp-content/uploads/2024/12/2024-Access-Insights-Report.pdf (cited 5 July 2025).

[5] McNicholl, A., Casey, H., Desmond, D., et al., 2021. The impact of assistive technology use for students with disabilities in higher education: a systematic review. Disability and Rehabilitation: Assistive Technology. 16(2), 130–143.

[6] Beveridge, L., Terras, M., Scott, G.G., et al., 2025. GenAI’s personalised learning could be a game-changer for disabled and neurodivergent students. Available from: https://www.timeshighereducation.com/campus/genais-personalised-learning-could-be-gamechanger-disabled-and-neurodivergent-students (cited 5 July 2025).

[7] Terras, M.M., Ramsay, J., 2012. The five central psychological challenges facing effective mobile learning. British Journal of Educational Technology. 43(5), 820–832.

[8] Terras, M.M., Boyle, E.A., Ramsay, J., et al., 2018. The opportunities and challenges of serious games for people with disabilities: a psychological perspective. British Journal of Educational Technology. 49(4), 690–700. DOI: https://doi.org/10.1111/bjet.12638

[9] Seale, J., 2024. Constructions of disability and technology and the shaping of future research. In A Research Agenda for Disability and Technology. Edward Elgar Publishing: Cheltenham, UK.

[10] Ramsay, J., Terras, M.M., 2015. The pendulum swing of user instruction and interaction: The resurrection of ‘how to use’ technology to learn in the 21st century. E-Learning and Digital Media. 12(3–4), 372–390.

[11] Sweller, J., 2011. Cognitive load theory. In: Psychology of Learning and Motivation, Vol. 55. Academic Press: San Diego, CA, USA. pp. 37–76.

[12] Price, M., 2025. From ‘I hate school’ to ‘Can we skip summer?’: Alpha’s motivation formula. Alpha School. Available from: https://alpha.school/blog/from-i-hate-school-to-can-we-skip-summer-alphas-motivation-formula/ (cited 5 July 2025).

[13] Vygotsky, L.S., 1978. Mind in society: The development of higher psychological processes. Harvard University Press: Cambridge, CA, USA.

[14] Terras, M.M., Ramsay, J., Boyle, E., 2013. Learning and new technology within higher education: A psychological perspective. Journal of E-Learning and Digital Media. 10(2), 162–174.

[15] Karan, B., Chakma, C., 2025. Influence of higher education students’ perceived behaviour on their artificial intelligence acceptance: an empirical investigation using technology acceptance model. Journal of Applied Research in Higher Education. Ahead-of-print. DOI: https://doi.org/10.1108/JARHE-11-2023-0535

[16] Nahana, P.K.,Rajeev, K.N., 2024. Cognitive offloading: A review. The International Journal of Indian Psychology. 12(2), 4668–4681. Available from: https://ijip.in/wp-content/uploads/2024/07/18.01.417.20241202.pdf

[17] Romeo, G., Conti, D., 2025. Exploring automation bias in human-AI collaboration: a review and implications for explainable AI. AI & Society. DOI: https://doi.org/10.1007/s00146-025-02422-7

[18] Rodríguez-Ortiz, M.Á., Santana-Mancilla, P.C., Anido-Rifón, L.E., 2025. Machine learning and generative AI in learning analytics for higher education: a systematic review of models, trends, and challenges. Applied Sciences. 15(15), 8679. Available from: https://www.researchgate.net/publication/394312843_Machine_Learning_and_Generative_AI_in_Learning_Analytics_for_Higher_Education_A_Systematic_Review_of_Models_Trends_and_Challenges

[19] Jin, Y., Martinez-Maldonado, R., Gašević, D., Yan, L., 2025. GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence. 9, 100436. DOI: https://doi.org/10.1016/j.caeai.2025.100436

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How to Cite

Terras, M. M., Beveridge, L., Scott, G. S., & Ramzan, N. (2025). Using Generative AI to Support Inclusion: Insights from a Psychological Perspective . Journal of International Education and Practice, 8(2), 45–49. https://doi.org/10.30564/jiep.v8i2.12254

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COMMUNICATION