The Influence of Education and Self-Perceived Tech Savviness on AI Reliance: The Role of Trust
Biswas, Mriganka and Murray, John (2025) The Influence of Education and Self-Perceived Tech Savviness on AI Reliance: The Role of Trust. In: Foundations of Computer Science and Frontiers in Education: Computer Science and Computer Engineering. CSCE 2024. Communications in Computer and Information Science ((CCIS,volume 2261)) (2261). Springer, pp. 3-17. ISBN 978-3-031-85930-4
| Item Type: | Book Section |
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Abstract
The increasing pervasiveness of Artificial Intelligence (AI) within our daily lives necessitates a deeper understanding of how trust shapes our evolving relationship with technology. This mixed-methods study investigates how edu-cational level, self-perceived ‘tech-savviness’, and emotional responses to tech absence, influences individual reliance on, and trust in, AI. Quantitative analysis reveals that individuals who have been in educational institutions for longer pe-riods (e.g. obtained master’s degrees) demonstrate greater trust in AI, leading to stronger reliance, particularly for recommendations scores. Qualitative analysis uncovers a complex interplay between trust, convenience, task-specific trust, and concerns about autonomy. A striking finding is the prevalence of negative emo-tions, including a sense of "incompleteness," during tech absence, especially among the tech-savvy group. This highlights how reliance can extend beyond functional utility and become psychologically intertwined with trust, potentially impacting self-perception. These findings challenge traditional models of tech-nology adoption and emphasize the need for frameworks that consider the psy-chological dimensions of trust in understanding AI reliance. The interplay be-tween education, trust-based reliance, and psychological factors will be crucial for individual adaptability and resilience in an increasingly AI-driven world.
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| Additional Information: This version of the book chapter has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-85930-4_1] |
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| Depositing User: John Murray |
Identifiers
| Item ID: 18363 |
| Identification Number: 10.1007/978-3-031-85930-4_1 |
| ISBN: 978-3-031-85930-4 |
| URI: https://sure.sunderland.ac.uk/id/eprint/18363 |
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| Date Deposited: 15 Oct 2024 17:47 |
| Last Modified: 18 Dec 2025 14:27 |
| Author: |
Mriganka Biswas
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| Author: |
John Murray
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| Author: | Mriganka Biswas |
| Author: | John Murray |
University Divisions
Faculty of Business and Technology > School of Computer Science and EngineeringSubjects
Computing > Artificial IntelligenceComputing > Human-Computer Interaction
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