Authors :
Muka Kabeya Arsene; Oshasha Oshasha Fiston; Musas A Musas Andre; Kabeya Mukosayi Jospeh; Tshielo Koka Souvient; Kobalanga Liamba Pathy Cedrick
Volume/Issue :
Volume 11 - 2026, Issue 5 - May
Google Scholar :
https://tinyurl.com/3jj8h6ts
Scribd :
https://tinyurl.com/uhnrex44
DOI :
https://doi.org/10.38124/ijisrt/26May696
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The pace of change in artificial intelligence (AI) has shifted software systems from merely passive computational tools
to active agents of human cognition, behavior, and decision-making. Yet, the vast majority of existing systems still rely on static
models, unaware of the modulation potential of human cognitive and emotional states. We propose a new class of neuroadaptive software systems, which can modulate their behaviour on the basis of online cognitive and affective signals. Building
on cognitive neuroscience, affective computing and machine learning, this framework allows software systems to intelligently
react to users´ mental states. The study presents a conceptual architecture, introduces a methodological approach to be followed
for the implementation and discusses use cases and ethical considerations. This work advances human-centered artificial
intelligence by converging computational systems and human cognition.
References :
- R. W. Picard, Affective Computing, MIT Press, 1997.
- B. P. Woolf, Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning, Morgan Kaufmann, 2010.
- E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Principles of Neural Science, 5th ed., McGraw-Hill, 2014.
- C. O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Crown Publishing Group, 2016.
- R. Calvo and S. D’Mello, “Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications,” IEEE Transactions on Affective Computing, vol. 1, no. 1, pp. 18–37, 2010.
- J. R. Anderson, “Cognitive Psychology and Its Implications,” 7th ed., Worth Publishers, 2010.
- OECD, “Artificial Intelligence in Education: Challenges and Opportunities,” OECD Publishing, 2021.
The pace of change in artificial intelligence (AI) has shifted software systems from merely passive computational tools
to active agents of human cognition, behavior, and decision-making. Yet, the vast majority of existing systems still rely on static
models, unaware of the modulation potential of human cognitive and emotional states. We propose a new class of neuroadaptive software systems, which can modulate their behaviour on the basis of online cognitive and affective signals. Building
on cognitive neuroscience, affective computing and machine learning, this framework allows software systems to intelligently
react to users´ mental states. The study presents a conceptual architecture, introduces a methodological approach to be followed
for the implementation and discusses use cases and ethical considerations. This work advances human-centered artificial
intelligence by converging computational systems and human cognition.
