AI in higher education: Faculty perspective towards  artificial intelligence through UTAUT approach

Ryan Clifford L. Perez

doi:10.46223/HCMCOUJS.soci.en.14.4.2851.2024

14(4)2024

AI in higher education: Faculty perspective towards artificial intelligence through UTAUT approach

Author - Affiliation:

Ryan Clifford L. Perez - Marinduque State College-Main Campus, Marindque

Corresponding author: Ryan Clifford L. Perez - ryancliffordperez@gmail.com

DOI:10.46223/HCMCOUJS.soci.en.14.4.2851.2024

Submitted: 13-07-2023
Accepted: 06-12-2023
Published: 02-04-2024

Abstract

Applications of AI in higher education have been around for several years, and numerous studies have looked at the pedagogical potential that these applications can offer to support the learning processes. However, there are still concerns and misunderstandings about acceptance in higher education, particularly among faculty members, despite the growing number of studies and their opportunities for supporting the educational and learning process. This paper aims to investigate the Behavioral Intention (BI) of Higher Education Institution faculty (HEI-faculty) towards adopting AI from a pedagogical perspective. The hypotheses in this paper were tested using a technology acceptance model with four major constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC), as well as the effects of four mediating variables: age, gender, experience, and voluntariness. The result has shown that the Behavioral Intention (BI) of adopting AI among HEI faculty has a strong positive significant correlation with PE, EE, SI, and FC. Interestingly, the social influence of adopting AI from colleagues has a strong influence on the use of AI for education. Thus, one of the proposed hypotheses was disproven. Furthermore, the result of this paper also suggests considerations for the future development of AI applications for HE.

Keywords

artificial intelligence; behavioral intention; effort expectancy; facilitating condition; higher education; performance expectancy; social influence

Full Text:

PDF

Cite this paper as:

Perez, R. C. L. (2024). AI in higher education: Faculty perspective towards artificial intelligence through UTAUT approach. Ho Chi Minh City Open University Journal of Science – Social Sciences, 14(4), 32-50. doi:10.46223/HCMCOUJS.soci.en.14.4.2851.2024

References

Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205-7224.

Ali, M., & Abdel-Haq, M. K. (2021). Bibliographical analysis of artificial intelligence learning in higher education: Is the role of the human educator and educated a thing of the past? In Fostering communication and learning with underutilized technologies in higher education (pp. 36-52). Hershey, PA: IGI Global Publisher.

Ang, S. M. O., & Aragon, M. J. D. (2020). Development of AI-enhanced information technology program: Preparing today's students in ai era (No. 4049). Manchester, UK: EasyChair.

Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted. Exploring the future of artificial intelligence in schools and colleges. Retrieved May 10, 2023, from https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf

Beltramini, E. (2019). Life 3.0. Being human in the age of artificial intelligence, by Max Tegmark. Religion and Theology, 26(1/2), 169-171.

Chao, C. M. (2019). Factors determining the behavioural intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652-1666.

Cheah, C. W. (2021). Developing a gamified AI-enabled online learning application to improve students’ perception of university physics. Computers and Education: Artificial Intelligence, 2, Article 100032

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98.

Dilmurod, R., & Fazliddin, A. (2021). Prospects for the introduction of artificial intelligence technologies in higher Education. ACADEMICIA: An International Multidisciplinary Research Journal, 11(2), 929-934.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1), 39-50.

Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in Education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134-147.

Hao, Z., Miao, E., & Yan, M. (2021). Research on school principals’ willingness to adopt artificial intelligence education and related influencing factors. In Tenth International Conference of Educatiossssnal Innovation through Technology (EITT) (pp. 356-361). Piscataway, NJ: IEEE.

Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75-84. doi:10.1016/j.ijmedinf.2017.02.002

Huang, S. (2021). Analysis of psychological teaching assisted by artificial intelligence in sports training courses. Journal of Applied Science and Engineering, 24(5), 743-748.

Ilić, M. P., Păun, D., Šević, N. P., Hadžić, A., & Jianu, A. (2021). Needs and performance analysis for changes in higher education and implementation of artificial intelligence, machine learning, and extended reality. Education Sciences, 11(10), 568 -572.

Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computer in Human Behavior, 70(29), 460-474. doi:10.1016/j.chb.2017.01.001

Khare, K., Stewart, B., & Khare, A. (2018). Artificial intelligence and the student experience: An institutional perspective. The International Academic Forum (IAFOR), 6(3), 63-78. doi:10.22492/ije.6.3.04

Kuleto, V., Ilić, M., Dedić, V., & Raketić, K. (2021). Application of artificial intelligence and machine learning in higher education, available platforms and examining students’ awareness. Journal of Education, 1(3), 24-28.

Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), Article 10424.

Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 13(18), 20-23.

Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96.

Li, X. (2017). The construction of intelligent English teaching model based on artificial intelligence. International Journal of Emerging Technologies in Learning (iJET), 12(12), 35-44.

Li, Y., & Zhao, M. (2021). A study on the influencing factors of continued intention to use MOOCs: UTAUT model and CCC moderating effect. Frontiers in Psychology, 12, Article 528259.

Lin, P. H., Wooders, A., Wang, J. T. Y., & Yuan, W. M. (2018). Artificial intelligence, the missing piece of online Education? IEEE Engineering Management Review, 46(3), 25-28.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. London, UK: Pearson Education Press.

Marikyan, D., & Papagiannidis, S. (2021) Unified theory of acceptance and use of technology: A review. In S. Papagiannidis (Ed.), TheoryHub book.

Miles, J. C., & Walker, A. J. (2006, September). The potential application of artificial intelligence in transport. IEE Proceedings-Intelligent Transport Systems, 153(3), 183-198.

Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13-39.

Mohanty, S., Rashid, M. H. A., Mridul, M., Mohanty, C., & Swayamsiddha, S. (2020). Application of Artificial Intelligence in COVID-19 drug repurposing. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1027-1031.

Momani, A. M. (2020). The unified theory of acceptance and use of technology: A new approach in technology acceptance. International Journal of Sociotechnology and Knowledge Development (IJSKD), 12(3), 79-98.

Nain, H. (2021, December). Understanding students’ behavioural intentions to use social media learning in education: A Utaut model approach. In International Conference on Computational Performance Evaluation (ComPE) (pp. 256-261). Piscataway, NJ: IEEE.

Pedró, F. (2020). Applications of artificial intelligence to higher education: Possibilities, evidence, and challenges. Open Journal of IUL Univesity, 1(1), 61-76.

Philippine Statistics Authority. (2017). Working age population. Retrieved May 10, 2023, from psa.gov.ph/ISSiP/concepts-and-definitions/161196

Qazi, W., Raza, S. A., Khan, K., & Salam, J. (2021). Adoption of e-learning system in higher education environments: Evidence from modified UTAUT model. ASR: Chiang Mai University Journal of Social Sciences and Humanities, 7(1), 1-24. doi:10.12982/CMUJASR.2020.003

Rampton, V., Mittelman, M., & Goldhahn, J. (2020). Implications of artificial intelligence for medical Education. The Lancet Digital Health, 2(3), e111-e112.

Shuli, Z. (2017). Designing and practice of a college english teaching platform based on artificial intelligence [J]. Journal of Computational and Theoretical Nanoence, 14(1), 104-108.

Sousa, M., Dal Mas, F., Pesqueira, A., Lemos, C., Verde, J. M., & Cobianchi, L. (2021). The potential of AI in health higher Education to increase the students’ learning outcomes. Technology Education Management Informatics, 2, 488-497.

Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: differences in UTAUT determinants between preand post-adopters. Computer and Human Behavavior, 64(71), 602-620. doi:10.1016/j.chb.2016.07.037

Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., ... Ho, C. S. (2021). Determinants of intention to use artificial intelligence-based diagnosis support system among prospective physicians. Frontiers in Public Health, 26(9), Article 755644.

Venkatesh, V., Davis, F., & Morris, M. (2007). Dead or alive? The development, trajectory and future of technology adoption research. Journal of the Association for Information Systems, 84, 267-286.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540

Vo, T. H. P., Hart, J. E., Laden, F., & Chiang, Y. Y. (2018). Emerging trends in geospatial artificial intelligence (geoAI): Potential applications for environmental epidemiology. Environmental Health, 17(1), 1-6.

Wang, B. H. (2012). An overview on the theory of artificial intelligence. Value Engineering, 13(4).

Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: An empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13(3389), 54-67.

Yang, C., Huan, S., & Yang, Y. (2020). A practical teaching mode for colleges supported by artificial intelligence. International Journal of Emerging Technologies in Learning (iJET), 15(17), 195-206.

Yang, R. (2020). Artificial intelligence-based strategies for improving the teaching effect of art major courses in colleges. International Journal of Emerging Technologies in Learning (iJET), 15(22), 146-160.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher Education - where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27.

Zhang, W. (2020). A study on the user acceptance model of artificial intelligence music based on UTAUT. Journal of the Korea Society of Computer and Information, 25(6), 25-33.

DOI: https://doi.org/10.46223/HCMCOUJS.soci.en.14.4.2851.2024