Beyond Technology Acceptance: Exploring the Cognitive, Ethical, and Motivational Factors Shaping ChatGPT Engagement in Cybersecurity Education

Okon, Milena and Allison, Jordan ORCID: https://orcid.org/0000-0001-8513-4646 (2026) Beyond Technology Acceptance: Exploring the Cognitive, Ethical, and Motivational Factors Shaping ChatGPT Engagement in Cybersecurity Education. In: 6th International Conference on Educational Technology and Online Learning (ICETOL) Proceedings. ICETOL. (In Press)

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Abstract

The increasing integration of generative artificial intelligence tools such as ChatGPT into computing education raises important questions about their relationship with students’ critical thinking and problem-solving skill development. This study explores associations between ChatGPT usage and students perceived development of higher-order thinking skills, including critical thinking and problem-solving, within a cybersecurity education context. This context is particularly relevant because computing and cybersecurity students regularly engage with tasks such as debugging or threat analysis, which require analytical reasoning, evaluation, and independent decision-making. As AI becomes increasingly embedded in these activities, understanding how students engage with such tools is important for determining their role within learning processes that traditionally depend on independent reasoning. Drawing on survey data from 84 students, we employ partial least squares structural equation modelling (PLS-SEM) to examine a multi-construct model incorporating usage behaviour, perceived usefulness, self-efficacy, ethical awareness, and perceived skill development. The study used a cross-sectional observational design based on students’ self-reported perceptions and experiences. The model was used to explore both the relationship between ChatGPT usage and perceived skill development and the psychological and institutional factors associated with students’ engagement with the tool. The structural model explained 70.9% of the variance in ChatGPT usage and 45.7% of the variance in perceived critical thinking and problem-solving skill development. ChatGPT usage demonstrated a significant positive association with perceived skill development (β = 0.506, p < .001). However, the factors associated with ChatGPT engagement revealed more complex patterns than conventional technology acceptance perspectives suggest. Perceived usefulness (β = 0.267, p = .042) and motivation for learning (β = 0.220, p = .008) were positively associated with ChatGPT usage, whereas self-efficacy (β = -0.219, p = .043) and ethical awareness (β = -0.245, p = .004) demonstrated significant negative associations. University support was positively associated with perceived skill development (β = 0.261, p = .007), suggesting that institutional guidance may contribute to how students perceive the educational value of AI-supported learning. This study contributes to educational technology research by providing an exploratory model that reveals both enabling and constraining factors associated with students' engagement with generative AI. Although ChatGPT usage was positively associated with perceived critical thinking and problem-solving skill development, higher levels of ethical awareness and self-efficacy were negatively associated with usage. These findings suggest that students' engagement with generative AI is more nuanced than simple technology acceptance models imply and reinforce the importance of pedagogical approaches that encourage critical and responsible AI use. Limitations related to the use of self-reported measures and a relatively small sample size are acknowledged, indicating the need for further research incorporating behavioural and performance-based assessments.

Item Type:	Book Section
Uncontrolled Keywords:	Generative artificial intelligence; ChatGPT; Cybersecurity education; Higher-order thinking skills; Technology acceptance
Related URLs:	Organisation
Subjects:	L Education > LC Special aspects of education Q Science > Q Science (General) > Q336 Artificial intelligence Q Science > QA Mathematics > QA76 Computer software Q Science > QA Mathematics > QA76 Computer software > QA76.9 Other topics > QA76.9.H85 Human-computer interaction
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Kamila Niekoraniec
Date Deposited:	05 Nov 2026 11:42
Last Modified:	08 Nov 2026 09:30
URI:	https://eprints.glos.ac.uk/id/eprint/16629

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