From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles

Xinwei Huang

doi:10.47297/taposatWSP2633-456929.20250609

PDF
导出
分享
收藏
专辑

From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles

6卷第9期

浏览量: 0 Downloads: 3

From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles
From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles
Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang, 315000, China
2025年6卷第9期页码：145-149
纸质出版：2025-09-30
DOI：10.47297/taposatWSP2633-456929.20250609
Accepted：

Scan QR Code

Xinwei Huang. (2025). From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles. 科技理论与实践（英文版）, 6(9), 145-149.

Huang Xinwei.From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles[J].Theory and Practice of Science and Technology,2025,06(09):145-149.
Xinwei Huang. (2025). From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles. 科技理论与实践（英文版）, 6(9), 145-149. DOI： 10.47297/taposatWSP2633-456929.20250609.

Huang Xinwei.From General to Personal: A Review of the Human-AI Interaction Frameworks through the Lens of Cognitive Styles[J].Theory and Practice of Science and Technology,2025,06(09):145-149. DOI： 10.47297/taposatWSP2633-456929.20250609.

摘要

Abstract

Human-Computer Interaction (HCI) has rapidly evolved into Human-AI Interaction (HAII) in recent years

transforming the emphasis from standardised

general design to personalised frameworks. This article reviewed the evolution of interaction design frameworks through the lens of cognitive styles. The paper first outlines the limitations of general design frameworks

such as GOMS and KLM. These frameworks emphasise efficiency and predictability

but ignore individual differences. It then explores the rise of recommender systems

which introduce personalised interactions in the phase of behaviour. This review highlights the recent advancements in Large Language Models (LLMs) and multimodal AI systems. These systems can provide real-time responses

explainability

and customised responses tailored to users' cognitive styles. By organising the existing literature

this article ensures the key advantages

gaps

and challenges of integrating cognitive style into HAII frameworks. Future research should focus on further developing cross-field methodologies that combine cognitive psychology and AI-driven personalisation

and then create an HAII system with adaptability

explainability

and trustworthiness.

关键词

Keywords

references

M. Kozhevnikov, 'Cognitive styles in the context of modern psychology: Toward an integrated framework of cognitive style.', Psychol. Bull., vol. 133 , no. 3 , pp. 464 – 481 , May 2007.

Y. Shin, K. Ranjan, M. C. Kowalski, and J. Yoon, 'Toward personalized AI-powered recommender systems to support users' daily music choice experiences', Int. J. Hum.-Comput. Stud., vol. 203 , p. 103574 , Sept. 2025.

C. Zhao, 'Human-AI Interaction Design Standards', 2025.

M. Virvou, 'The Emerging Era of Human-AI Interaction: Keynote Address', in 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), July 2022, pp. 1 – 10 . .

'Zotero', Zotero. [Online]. Available: https://www.zotero.org/

D. Akoumianakis and C. Stephanidis, 'Universal Design in HCI: A critical review of current research and practice'.

C. Stephanidis, D. Akoumianakis, and A. Savidis, 'Universal Design in Human-Computer Interaction'.

W. Xu, 'Toward human-centered AI: a perspective from human-computer interaction', Interactions, vol. 26 , no. 4 , pp. 42 – 46 , June 2019.

W. Xu, L. Ge, and Z. Gao, 'Human-AI interaction An emerging interdisciplinary domain for enabling human-centered AI', Oct. 2021 .

Interaction Design Foundation, 'What is Human-AI Interaction (HAX)?', What is Human-AI Interaction (HAX)? Accessed: Aug. 06, 2025. [Online]. Available: https://www.interaction-design.org/literature/topics/human-ai-interaction.

G. Bansal, B. Nushi, E. Kamar, D. S. Weld, W. S. Lasecki, and E. Horvitz, 'Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff', Proc. AAAI Conf. Artif. Intell., vol. 33 , no. 01 , pp. 2429 – 2437 , July 2019.

R. C. A. Bendall, A. Galpin, L. P. Marrow, and S. Cassidy, 'Cognitive Style: Time to Experi ment', Front. Psychol., vol. 7 , Nov. 2016 .

M. Kozhevnikov, C. Evans, and S. M. Kosslyn, 'Cognitive Style as Environmentally Sensitive Individual Differences in Cognition: A Modern Synthesis and Applications in Education, Business, and Management'.

R. Riding and I. Cheema, 'Cognitive Styles—an overview and integration', Educ. Psychol., vol. 11 , no. 3–4, pp. 193 – 215 , Jan. 1991 .

T. Neumayr and M. Augstein, 'A Systematic Review of Personalized Collaborative Systems', Front. Comput. Sci., vol. 2 , p. 562679 , Nov. 2020 .

M. Zanker, L. Rook, and D. Jannach, 'Measuring the impact of online personalisation: Past, present and future', Int. J. Hum.-Comput. Stud., vol. 131 , pp. 160 – 168 , Nov. 2019 .

K. Hardcastle, L. Vorster, and D. M. Brown, 'Understanding Customer Responses to AI-Driven Personalized Journeys: Impacts on the Customer Experience', J. Advert., vol. 54 , no. 2 , pp. 176 – 195 , Mar. 2025 .

S. K. Card, T. P. Moran, and A. Newell, The psychology of human-computer interaction, Repr. Mahwah, NJ: Erlbaum, 1983.

F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh, 'Recommendation systems: Principles, methods and evaluation', Egypt. Inform. J., vol. 16 , no. 3 , pp. 261 – 273 , Nov. 2015 .

P. Resnick and H. R. Varian, 'Recommender systems'.

R. Mendoza et al., 'Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization', Sept. 25, 2024, arXiv: arXiv: 2409.16973 .

J. Chen et al., 'When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities', World Wide Web, vol. 27 , no. 4 , p. 42 , July 2024.

X. Li, R. Zhou, Z. C. Lipton, and L. Leqi, 'Personalized Language Modeling from Personalized Human Feedback', Dec. 09, 2024, arXiv: arXiv: 2402.05133 .

文章被引用时，请邮件提醒。

Submit

EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling

Related Author

Yujia Du School of Information Engineering, Xi'an Eurasia University

Related Institution

School of Information Engineering, Xi'an Eurasia University

⁰