Adoption of Artificial Intelligence (AI) Components to Improve Entrepreneurship Study in Oyo State Tertiary Institutions, Oyo State, Nigeria

Studying entrepreneurship imparts a significant role for self-development, self-reliance, self-employment among others for every individual including people with special needs. Extant studies largely focused on factors influencing entrepreneurship study, with little consideration for adaptation of AI components to improve entrepreneurship study. The purpose of this study was to adopt AI components to improve entrepreneurship study. The study used descriptive of survey type as research design. The target population was undergraduate study in Oyo State Tertiary Institutions, Oyo State, Nigeria. A purposive sampling technique was used to select 1250 undergraduate students used in the study. The instruments used were standardized scales named Entrepreneurship Studies Questionnaire (r=.86), AI Checklist instrument, and Questionnaire of AI use Motives (r =.81). Four research questions were raised and answered. Data were analyzed using frequency count, simple percentage and t-test tested at 0.05 level of significant. Findings showed significant high influence of teaching and learning of entrepreneurship study with average mean (3.88) and standard deviation (0.75) while some items had no significant high mean scores. The result revealed that planning and scheduling 898(71.8%) is the most component of AI use in teaching and learning entrepreneurship study followed by knowledge representation 741 (59.3%)

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Publication Details

Journal
International Journal of Scientific Multidisciplinary Research
Published
2025-07-30
DOI
https://doi.org/10.55927/ijsmr.v3i7.434
Primary Topic
Smart Systems and Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Adoption of Artificial Intelligence (AI) Components to Improve Entrepreneurship Study in Oyo State Tertiary Institutions, Oyo State, Nigeria

Ibrahim Ojo Rasheed, Salihu Abdulwaheed Adelabu, Agbaje Fausiat Olajumoke, Oluwole Eyitayo Adewale
International Journal of Scientific Multidisciplinary Research
Smart Systems and Machine Learning
article

Adoption of Artificial Intelligence (AI) Components to Improve Entrepreneurship Study in Oyo State Tertiary Institutions, Oyo State, Nigeria

Ibrahim Ojo Rasheed, Salihu Abdulwaheed Adelabu, Agbaje Fausiat Olajumoke, Oluwole Eyitayo Adewale
article en

Abstract

Studying entrepreneurship imparts a significant role for self-development, self-reliance, self-employment among others for every individual including people with special needs. Extant studies largely focused on factors influencing entrepreneurship study, with little consideration for adaptation of AI components to improve entrepreneurship study. The purpose of this study was to adopt AI components to improve entrepreneurship study. The study used descriptive of survey type as research design. The target population was undergraduate study in Oyo State Tertiary Institutions, Oyo State, Nigeria. A purposive sampling technique was used to select 1250 undergraduate students used in the study. The instruments used were standardized scales named Entrepreneurship Studies Questionnaire (r=.86), AI Checklist instrument, and Questionnaire of AI use Motives (r =.81). Four research questions were raised and answered. Data were analyzed using frequency count, simple percentage and t-test tested at 0.05 level of significant. Findings showed significant high influence of teaching and learning of entrepreneurship study with average mean (3.88) and standard deviation (0.75) while some items had no significant high mean scores. The result revealed that planning and scheduling 898(71.8%) is the most component of AI use in teaching and learning entrepreneurship study followed by knowledge representation 741 (59.3%)

International Journal of Scientific Multidisciplinary ResearchVol. 3(7)
University of Ibadan (NG), Ibrahim Badamasi Babangida University (NG), Lead City University (NG)
Decent work and economic growth
Openalex Percentile: Top 78%
Smart Systems and Machine Learning
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