SELF-AWARENESS SKILLS AS A PREDICTOR FOR ARTIFICIAL INTELLIGENCE AND DELIVERY OF BUSINESS EDUCATION IN THE UNIVERSITIES IN SOUTH-SOUTH NIGERIA

Dr Sampson Wagbara, Frank Odiri Egajivwie

Abstract


This study was conducted to examine self-awareness skills as a predictor for AI and delivery of business education programme. The study adopted correlation research design. The population of the study comprised 176 lecturers in business education programme in universities in South-South Nigeria. Census sampling was adopted for the study because it is of manageable size. The instrument for data collection was a 27-item structured questionnaire partitioned into two parts, titiled: Self Awareness Skills Questionnaire (SASQ), and Artificial Intelligence and Delivery Business Education programme Questionnaire (AIDBEPQ), and which was structured on four point rating scale. The instrument was validated by three experts. The reliability of the instrument was established using Cronbach Alpha method, and coefficient of 0.67 and 0.69 was obtained. Google form questionnaire was used and administered by the researchers through online. A total of 169 respondents out of the 176 of the population responded to the questionnaire. The data collected were analyzed using Pearson Product Moment Correlation Coefficient, while Simple Linear Regression Analysis was used to test the hypotheses at 0.05 level of significance. The findings revealed among others that business educators’ ability to adapt to and implement AI tools in business education programmes is attributed to their emotional, physical and cognitive self awareness skills. it was concluded that emotional, physical and cognitive self-awareness skills are veritable tools for effective AI delivery in business education programmes, the skills are crucial to better utilization of AI tools in teaching and learning as well as effective learner’s engagement.. It was recommended among others that emotional self-awareness skills should be developed and integrated into modules or workshops focused on both business educators and the learners.

Keywords: Self-Awareness, Predictor, Artificial Intelligence, Delivery, Business Education,


Full Text:

PDF

References


Agboola, J. O., (2018). User perception of electronic resources in the university of Ilorin, Nigeria.

Journal of Emerging Trends in Computing and Information Science, 2(11), 554-562.

Andrychowicz, M., Denil, M., Gonzalez, J., et al. (2016). Learning to Learn by Gradient Descent.

Proceedings of the 33rd International Conference on Machine Learning (ICML).

Bar-On, R., & Parker, J. D. A. (2022) The Handbook of Emotional Intelligence: Theory, Development, Assessment, and Application at Home, School, and in the Workplace. Jossey-Bass.

Blikstein, P., Worsley, M., Piech, C., Sahami, M., & Cooper, S., (2019). AI and Education: Automatic Assistance for Teachers and Students. IEEE Intelligent Systems, 34(6), 76-80

Brown, A., L. K. Smith, & S. Jones. (2017). Enhancing learning through simulation and experiential tools. Educational Technology Journal, 52(4), 35-50.

Cai, Z., & Lee, Y. (2021). Emotional intelligence and adaptive learning: Impact on student engagement. Journal of Educational Technology & Society, 24(1), 58-70.

Chen, Z., & Lee, D. (2020). Emotional Intelligence in Business Education: Enhancing Learning and Engagement. Journal of Business Education, 45(3), 213-229.

Chen, X., Wang, Y., & Chen, X. (2020). The role of Self-Awareness in online learning environments: A Review. Journal of Educational Technology and Society, 23(2), 56-68.

Chou, P.-N., & Min, H.-T. (2019). User-entered design for educational AI Systems. International Journal of Artificial Intelligence in Education, 29(3), 437-460.

Huang, Y., & Liu, X. (2023). Ergonomics in digital education: Enhancing physical comfort with AI tools. Educational Technology Research and Development, 71(2), 215-230.

Duval, S., &Wicklund, R. A. (2020). A theory of objective self-awareness. Academic Press. Egajivwie,F.O.(2024). Business Education Lecturers Perception of Soft skills acquisition by

Business Education Students to Fit into Technology-Driven World. Nigerian Journal of Business Education (NIGJBED), 11 (2), 367-375

Flavell, J. H. (1979). "Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. Journal of American Psychologist, 34(10), 906-911.

Floridi, L., Cowls, J., Taddeo, M., &Chiriatti, S. (2020). How to design AI for social good. Journal of Science and Engineering Ethics, 26(1), 393-416.

Gellersen, H. W., et al. (2019) Context-aware computing and physical self-awareness in human- computer interaction. ACM Transactions on Computer-Human Interaction, 26(2), 1-25.

Goleman, D. (2013). Focus: The Hidden Driver of Excellence. HarperCollins.

Gonzalez, R., et al. (2019)The impact of physical health on academic performance: A review of recent evidence. Educational Psychology Review, 31(3), 425-448.

Heath, C. (2020) Cognitive processes in AI development: implications for human-AI interaction.

Artificial Intelligence Review, 53(2), 245-267.

Holmes, W., Bialik, M., &Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Kumar, V., et al. (2022) Emotion recognition and AI: Techniques and applications. IEEE Transactions on Affective Computing, 13(1), 45-59.

Lin, J., Zhang, Y., & Li, Z. (2021). Cognitive self-awareness and adaptive learning: The role of AI.

Journal of Learning Analytics, 8(2), 45-61.

Li, X. & Li, S. (2018). Personalized learning with Artificial Intelligence: A case study. Journal of Computers & Education, 2(7), 232-245.

Luckin, R., Holmes, W., Griffiths, M., &Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson. https://www.pearson.com/uk/educators/higher-education- educators/ai-education.html

Morin, A. (2011). Self-awareness and the self-reflective mind. Journal of Educational Psychology, 20(1), 64-74.

Ogunode, N. J., Kingsley, E. & Okolie, R. C. (2023). Artificial Intelligence and tertiary education management. Electronic Research Journal of Social Sciences and Humanities, 5(4), 18 – 31.

Pardo, A., et al. (2018)Wearable health technologies and AI: implications for personalized health Monitoring*. Journal of Healthcare Engineering, 2(18), 1-12.

Raji, W. I. & Amadi, N.S. (2023). Integration of smart farming in agricultural education for students’ relevance in high tech world in tertiary institutions in Rivers State. International Journal of Advanced Research and Learning, 2(4), 1-14 S

Scherer, K. R. (2019). Emotional self-awareness and AI in education. Emotion Researcher,11(1), 23-34.

Sutton, R. S., &Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press. VanLehn, K. (2019). The future of intelligent tutoring systems.Journal of Artificial Intelligence in

Education, 29(3), 357-376.

Wang, T., Xu, D., & Wang, X. (2021). Iterative development and continuous improvement in AI educational tools. Journal of Learning Analytics, 8(1), 56-72.

Woolf, B. P., et al. (2018) Intelligent tutoring systems and personalized learning: advances in AI education. Journal of Educational Data Mining, 10(1), 1-15.

Xie, I., Zhang, K., & Ding, X. (2021). Enhancing learner engagement through adaptivel learning systems. Educational Technology Research and Development, 69(4), 1047-1065.

Zhao, X., Chen, J., & Li, M. (2022). Feedback and self-awareness: improving learning outcomes with AI. Journal of Learning Analytics, 9(1), 87-102.

Zhao, X., Zhang, J., & Liu, X. (2020). Customizing AI for personalized learning. Educational Data Mining, 12(1), 89-106.


Refbacks

  • There are currently no refbacks.