The Impact of Education Level on AI Reliance, Habit Formation, and Usage

Mriganka Biswas; John Murray

doi:10.1109/ICAC61394.2024.10718860

The Impact of Education Level on AI Reliance, Habit Formation, and Usage

Mriganka Biswas, John Murray

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

7 Citations (Scopus)

Abstract

This study examines the relationship between education level and various aspects of AI technology adoption. Findings indicate that individuals with higher education exhibit significantly greater reliance on AI-powered recommendations, while other AI functionalities (e.g., prediction, assistance) showed minimal links to educational background. The results suggest that while higher education may promote greater trust and openness to specific AI features, broader AI literacy initiatives are needed to ensure equitable adoption and minimize potential divides. These findings highlight the importance of inclusive AI design and targeted educational interventions to bridge the potential gap in AI usage based on educational disparities.

Original language	English
Title of host publication	ICAC 2024 - 29th International Conference on Automation and Computing
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9798350360882
ISBN (Print)	9798350360899
DOIs	https://doi.org/10.1109/ICAC61394.2024.10718860
Publication status	Published - 23 Oct 2024
Externally published	Yes
Event	29th International Conference on Automation and Computing - Sunderland, United Kingdom Duration: 28 Aug 2024 → 30 Aug 2024 Conference number: 29 https://ieeexplore.ieee.org/xpl/conhome/10718699/proceeding

Conference

Conference	29th International Conference on Automation and Computing
Abbreviated title	ICAC 2024
Country/Territory	United Kingdom
City	Sunderland
Period	28/08/24 → 30/08/24
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10718699/proceeding

Access to Document

10.1109/ICAC61394.2024.10718860Licence: Unspecified

Cite this

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title = "The Impact of Education Level on AI Reliance, Habit Formation, and Usage",

abstract = "This study examines the relationship between education level and various aspects of AI technology adoption. Findings indicate that individuals with higher education exhibit significantly greater reliance on AI-powered recommendations, while other AI functionalities (e.g., prediction, assistance) showed minimal links to educational background. The results suggest that while higher education may promote greater trust and openness to specific AI features, broader AI literacy initiatives are needed to ensure equitable adoption and minimize potential divides. These findings highlight the importance of inclusive AI design and targeted educational interventions to bridge the potential gap in AI usage based on educational disparities.",

keywords = "AI Adoption, Education, Habit formation, Technology Acceptance",

author = "Mriganka Biswas and John Murray",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 29th International Conference on Automation and Computing, ICAC 2024 ; Conference date: 28-08-2024 Through 30-08-2024",

year = "2024",

month = oct,

day = "23",

doi = "10.1109/ICAC61394.2024.10718860",

language = "English",

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booktitle = "ICAC 2024 - 29th International Conference on Automation and Computing",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Biswas, M & Murray, J 2024, The Impact of Education Level on AI Reliance, Habit Formation, and Usage. in ICAC 2024 - 29th International Conference on Automation and Computing., 10718860, Institute of Electrical and Electronics Engineers Inc., 29th International Conference on Automation and Computing, Sunderland, United Kingdom, 28/08/24. https://doi.org/10.1109/ICAC61394.2024.10718860

TY - GEN

T1 - The Impact of Education Level on AI Reliance, Habit Formation, and Usage

AU - Biswas, Mriganka

AU - Murray, John

N1 - Conference code: 29

PY - 2024/10/23

Y1 - 2024/10/23

N2 - This study examines the relationship between education level and various aspects of AI technology adoption. Findings indicate that individuals with higher education exhibit significantly greater reliance on AI-powered recommendations, while other AI functionalities (e.g., prediction, assistance) showed minimal links to educational background. The results suggest that while higher education may promote greater trust and openness to specific AI features, broader AI literacy initiatives are needed to ensure equitable adoption and minimize potential divides. These findings highlight the importance of inclusive AI design and targeted educational interventions to bridge the potential gap in AI usage based on educational disparities.

AB - This study examines the relationship between education level and various aspects of AI technology adoption. Findings indicate that individuals with higher education exhibit significantly greater reliance on AI-powered recommendations, while other AI functionalities (e.g., prediction, assistance) showed minimal links to educational background. The results suggest that while higher education may promote greater trust and openness to specific AI features, broader AI literacy initiatives are needed to ensure equitable adoption and minimize potential divides. These findings highlight the importance of inclusive AI design and targeted educational interventions to bridge the potential gap in AI usage based on educational disparities.

KW - AI Adoption

KW - Education

KW - Habit formation

KW - Technology Acceptance

UR - https://www.scopus.com/pages/publications/85208624216

U2 - 10.1109/ICAC61394.2024.10718860

DO - 10.1109/ICAC61394.2024.10718860

M3 - Conference contribution

AN - SCOPUS:85208624216

SN - 9798350360899

BT - ICAC 2024 - 29th International Conference on Automation and Computing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th International Conference on Automation and Computing

Y2 - 28 August 2024 through 30 August 2024

ER -

The Impact of Education Level on AI Reliance, Habit Formation, and Usage

Abstract

Conference

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