Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification

Artemis Hadjiloizou; Jan Treur; Peter H.M.P. Roelofsma

doi:10.1007/978-3-032-13977-1_16

Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification

Artemis Hadjiloizou, Jan Treur^*, Peter H.M.P. Roelofsma

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

This paper examines whether AI coaching can improve the quality, reliability, and social validity of cybersecurity response decisions in a government surveillance context, in contrast to a human-only model. Two simulations were created using an adaptive network modelling framework, one with solely human decision making and the other with AI coaching judgement without taking away agency. The models were assessed on long-term decision making, public sentiment assessment, and threat detection accuracy. Results indicate that AI coaching significantly enhances decision consistency and quality, particularly in the face of shifting public pressure. The AI-coach enabled model decreased decision unpredictability while maintaining a stronger alignment with public ideals. According to these findings, if accountability and openness are maintained, AI-guided systems have the potential to responsibly support human decision-making in government settings.

Original language	English
Title of host publication	Biologically Inspired Cognitive Architectures 2025 - Proceedings of the 16th Annual Meeting of the BICA Society
Editors	Alexei V. Samsonovich, Félix Ramos, Tingting Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	203-227
Number of pages	25
ISBN (Print)	9783032139764
DOIs	https://doi.org/10.1007/978-3-032-13977-1_16
Publication status	Published - 2026
Event	16th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2025 - Puerto Vallarta, Mexico Duration: 6 Aug 2025 → 10 Aug 2025

Publication series

Name	Studies in Computational Intelligence
Volume	1243 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	16th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2025
Country/Territory	Mexico
City	Puerto Vallarta
Period	6/08/25 → 10/08/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Adaptive network model
AI Coaching
Government Surveillance

Access to Document

10.1007/978-3-032-13977-1_16

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Hadjiloizou, A., Treur, J., & Roelofsma, P. H. M. P. (2026). Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification. In A. V. Samsonovich, F. Ramos, & T. Liu (Eds.), Biologically Inspired Cognitive Architectures 2025 - Proceedings of the 16th Annual Meeting of the BICA Society (pp. 203-227). (Studies in Computational Intelligence; Vol. 1243 SCI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-13977-1_16

Hadjiloizou, Artemis ; Treur, Jan ; Roelofsma, Peter H.M.P. / Using AI Coaching to Improve Cybersecurity Decision-Making : An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification. Biologically Inspired Cognitive Architectures 2025 - Proceedings of the 16th Annual Meeting of the BICA Society. editor / Alexei V. Samsonovich ; Félix Ramos ; Tingting Liu. Springer Science and Business Media Deutschland GmbH, 2026. pp. 203-227 (Studies in Computational Intelligence).

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abstract = "This paper examines whether AI coaching can improve the quality, reliability, and social validity of cybersecurity response decisions in a government surveillance context, in contrast to a human-only model. Two simulations were created using an adaptive network modelling framework, one with solely human decision making and the other with AI coaching judgement without taking away agency. The models were assessed on long-term decision making, public sentiment assessment, and threat detection accuracy. Results indicate that AI coaching significantly enhances decision consistency and quality, particularly in the face of shifting public pressure. The AI-coach enabled model decreased decision unpredictability while maintaining a stronger alignment with public ideals. According to these findings, if accountability and openness are maintained, AI-guided systems have the potential to responsibly support human decision-making in government settings.",

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Hadjiloizou, A, Treur, J & Roelofsma, PHMP 2026, Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification. in AV Samsonovich, F Ramos & T Liu (eds), Biologically Inspired Cognitive Architectures 2025 - Proceedings of the 16th Annual Meeting of the BICA Society. Studies in Computational Intelligence, vol. 1243 SCI, Springer Science and Business Media Deutschland GmbH, pp. 203-227, 16th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2025, Puerto Vallarta, Mexico, 6/08/25. https://doi.org/10.1007/978-3-032-13977-1_16

Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification. / Hadjiloizou, Artemis; Treur, Jan; Roelofsma, Peter H.M.P.
Biologically Inspired Cognitive Architectures 2025 - Proceedings of the 16th Annual Meeting of the BICA Society. ed. / Alexei V. Samsonovich; Félix Ramos; Tingting Liu. Springer Science and Business Media Deutschland GmbH, 2026. p. 203-227 (Studies in Computational Intelligence; Vol. 1243 SCI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Hadjiloizou, Artemis

AU - Treur, Jan

AU - Roelofsma, Peter H.M.P.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - This paper examines whether AI coaching can improve the quality, reliability, and social validity of cybersecurity response decisions in a government surveillance context, in contrast to a human-only model. Two simulations were created using an adaptive network modelling framework, one with solely human decision making and the other with AI coaching judgement without taking away agency. The models were assessed on long-term decision making, public sentiment assessment, and threat detection accuracy. Results indicate that AI coaching significantly enhances decision consistency and quality, particularly in the face of shifting public pressure. The AI-coach enabled model decreased decision unpredictability while maintaining a stronger alignment with public ideals. According to these findings, if accountability and openness are maintained, AI-guided systems have the potential to responsibly support human decision-making in government settings.

AB - This paper examines whether AI coaching can improve the quality, reliability, and social validity of cybersecurity response decisions in a government surveillance context, in contrast to a human-only model. Two simulations were created using an adaptive network modelling framework, one with solely human decision making and the other with AI coaching judgement without taking away agency. The models were assessed on long-term decision making, public sentiment assessment, and threat detection accuracy. Results indicate that AI coaching significantly enhances decision consistency and quality, particularly in the face of shifting public pressure. The AI-coach enabled model decreased decision unpredictability while maintaining a stronger alignment with public ideals. According to these findings, if accountability and openness are maintained, AI-guided systems have the potential to responsibly support human decision-making in government settings.

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Hadjiloizou A, Treur J, Roelofsma PHMP. Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification. In Samsonovich AV, Ramos F, Liu T, editors, Biologically Inspired Cognitive Architectures 2025 - Proceedings of the 16th Annual Meeting of the BICA Society. Springer Science and Business Media Deutschland GmbH. 2026. p. 203-227. (Studies in Computational Intelligence). doi: 10.1007/978-3-032-13977-1_16

Using AI Coaching to Improve Cybersecurity Decision-Making: An Adaptive Network Model to Analyse AI-Supported Government Surveillance, Trust, and Threat Identification

Abstract

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