Understanding Student Acceptance of AI in Mojokerto Regency High Schools and a Framework for Effective Integration

Usmanur Dian Iswanti; Tri Lathif Mardi Suryanto; Doddy Ridwandono; Asif Faroqi; Mohammad Yasser Chuttur

doi:10.29407/intensif.v9i2.24993

Authors

Usmanur Dian Iswanti Universitas Pembangunan Nasional Veteran Jawa Timur https://orcid.org/0009-0000-4083-5211
Tri Lathif Mardi Suryanto Universitas Pembangunan Nasional Veteran Jawa Timur https://orcid.org/0000-0001-7532-2440
Doddy Ridwandono Universitas Pembangunan Nasional Veteran Jawa Timur https://orcid.org/0000-0003-2169-2096
Asif Faroqi Universitas Pembangunan Nasional Veteran Jawa Timur https://orcid.org/0000-0002-3881-6705
Mohammad Yasser Chuttur University of Mauritius https://orcid.org/0000-0002-6049-8272

DOI:

https://doi.org/10.29407/intensif.v9i2.24993

Keywords:

Artificial Intelligence, Acceptance, AI Based Learning, UTAUT2

Abstract

Background: The use of AI in education is growing rapidly, especially in adaptive learning and automated feedback. Recent studies show widespread adoption of AI in higher education, but research at the secondary school level is limited. Factors such as ease of use, motivation, and institutional support play an important role accepting these technologies. Objective: The objective of this study is to investigate the acceptance and usage of the Question.AI application among high school students in Mojokerto Regency, to identify the factors that influence its adoption and effectiveness in enhancing learning outcomes. Methods: The methodology adopted for this research comprises a quantitative study design using a probability sampling method, specifically the Stratified Random Sampling technique. A total of 400 high school students from Mojokerto Regency participated. Data collection was conducted through structured questionnaires designed to evaluate factors influencing the adoption of the Question.AI application. Result: The result revealed that Facilitating Conditions (FC), Habit (H), and Hedonic Motivation (HM) significantly influence students' behavioral intention to use the Question.AI application. Among these, Habit and Hedonic Motivation showed the strongest effect, indicating that students are more likely to adopt AI tools when their use becomes routine and satisfied. Conclusion: These results support the UTAUT2 framework and highlight the need for enjoyable user experiences and adequate support systems to drive sustained adoption. The findings contribute to understanding AI acceptance at the secondary education level and offer practical insights for integrating AI applications more effectively into school environments.

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Author Biographies

Usmanur Dian Iswanti, Universitas Pembangunan Nasional Veteran Jawa Timur

Department of Information System, Universitas Pembangunan Nasional Veteran Jawa Timur
Tri Lathif Mardi Suryanto, Universitas Pembangunan Nasional Veteran Jawa Timur

Department of Information System, Universitas Pembangunan Nasional Veteran Jawa Timur
Doddy Ridwandono, Universitas Pembangunan Nasional Veteran Jawa Timur

Department of Information System, Universitas Pembangunan Nasional Veteran Jawa Timur
Asif Faroqi, Universitas Pembangunan Nasional Veteran Jawa Timur

Department of Information System, Universitas Pembangunan Nasional Veteran Jawa Timur
Mohammad Yasser Chuttur, University of Mauritius

Department of Software and Information Systems, University of Mauritius, Republic of Mauritius

References

[1] K. Seo, J. Tang, I. Roll, S. Fels, and D. Yoon, “The impact of artificial intelligence on learner–instructor interaction in online learning,” Int. J. Educ. Technol. High. Educ., vol. 18, no. 1, pp. 1–23, Dec. 2021, doi: 10.1186/S41239-021-00292-9/TABLES/7.

[2] L. Chen, P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.

[3] K. Islam, “Deep Learning for Video-based Person Re-Identification: A Survey,” SSRN Electron. J., Mar. 2023, doi: 10.2139/ssrn.4313788.

[4] C. McGrath, T. Cerratto Pargman, N. Juth, and P. J. Palmgren, “University teachers’ perceptions of responsibility and artificial intelligence in higher education - An experimental philosophical study,” Comput. Educ. Artif. Intell., vol. 4, no. March, p. 100139, 2023, doi: 10.1016/j.caeai.2023.100139.

[5] M. Nestor et al., “Artificial Intelligence Index Report Introduction to the AI Index Report 2023,” Human-centered Artif. Intell., pp. 1–386, 2023.

[6] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – where are the educators?,” Int. J. Educ. Technol. High. Educ. 2019 161, vol. 16, no. 1, pp. 1–27, Oct. 2019, doi: 10.1186/S41239-019-0171-0.

[7] S. Wang, F. Wang, Z. Zhu, J. Wang, T. Tran, and Z. Du, “Artificial intelligence in education: A systematic literature review,” Expert Syst. Appl., vol. 252, p. 124167, Oct. 2024, doi: 10.1016/J.ESWA.2024.124167.

[8] I. Celik, M. Dindar, H. Muukkonen, and S. Järvelä, “The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research,” TechTrends, vol. 66, no. 4, pp. 616–630, Jul. 2022, doi: 10.1007/S11528-022-00715-Y/FIGURES/6.

[9] J. K. Opele, T. T. Alade, and R. O. Ajifowoke, “The Impact Of Artificial Intelligence (Ai) On Teaching And Research Experience Of University Lecturers: A Review,” UNIZIK J. Educ. Res. Policy Stud., vol. 17, no. 1, pp. 24–35, Feb. 2024, Accessed: Mar. 01, 2025. [Online]. Available: https://unijerps.org/index.php/unijerps/article/view/596

[10] W. Holmes, M. Bialik, and C. Fadel, “Artificial Intelligence In Education Promises and Implications for Teaching and Learning,” 2019, Accessed: Mar. 01, 2025. [Online]. Available: http://bit.ly/AIED-

[11] C. Neves, T. Oliveira, F. Cruz-Jesus, and V. Venkatesh, “Extending the unified theory of acceptance and use of technology for sustainable technologies context,” Int. J. Inf. Manage., vol. 80, no. August 2024, p. 102838, 2025, doi: 10.1016/j.ijinfomgt.2024.102838.

[12] A. A. Hernandez, M. B. Abisado, R. L. Rodriguez, and J. M. R. Imperial, “Predicting the Use Behavior of Higher Education Students on ChatGPT: Evidence from the Philippines,” in 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), IEEE, Nov. 2023, pp. 1–7. doi: 10.1109/TALE56641.2023.10398324.

[13] J. M. Romero-Rodríguez, M. S. Ramírez-Montoya, M. Buenestado-Fernández, and F. Lara-Lara, “Use of ChatGPT at University as a Tool for Complex Thinking: Students’ Perceived Usefulness,” Cult. los Cuid., vol. 12, no. 2, pp. 323–339, 2023, doi: 10.7821/naer.2023.7.1458.

[14] A. Tlili et al., “What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education,” Smart Learn. Environ., vol. 10, no. 1, 2023, doi: 10.1186/s40561-023-00237-x.

[15] S. H. Alshammari and M. H. Alshammari, “Factors Affecting the Adoption and Use of ChatGPT in Higher Education,” Int. J. Inf. Commun. Technol. Educ., vol. 20, no. 1, pp. 1–16, 2024, doi: 10.4018/IJICTE.339557.

[16] N. U. Ain, K. Kaur, and M. Waheed, “The influence of learning value on learning management system use: An extension of UTAUT2,” Inf. Dev., vol. 32, no. 5, pp. 1306–1321, 2016, doi: 10.1177/0266666915597546.

[17] A. Faroqi, S. Apol Pribadi, and M. S. Tri Lathif, “Exploring Online Shoppers’ Acceptance of Electronic Marketplace Using UTAUT and the Flow Theory,” J. Phys. Conf. Ser., vol. 1569, no. 2, Jul. 2020, doi: 10.1088/1742-6596/1569/2/022051.

[18] M. U. H. Uzir et al., “Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context,” Heliyon, vol. 9, no. 8, p. e18666, Aug. 2023, doi: 10.1016/J.HELIYON.2023.E18666.

[19] J. A. Kumar and B. Bervell, “Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students,” Educ. Inf. Technol., vol. 24, no. 2, pp. 1793–1817, 2019, doi: 10.1007/s10639-018-09858-z.

[20] A. Strzelecki, “Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology,” Innov. High. Educ., no. 0123456789, 2023, doi: 10.1007/s10755-023-09686-1.

[21] R. Masa’deh et al., “Antecedents of adoption and usage of ChatGPT among Jordanian university students: Empirical study,” Int. J. Data Netw. Sci., vol. 8, no. 2, pp. 1099–1110, 2024, doi: 10.5267/j.ijdns.2023.11.024.

[22] N. Bozorgkhou, “An internet shopping user adoption model using an integrated TTF and UTAUT: Evidence from Iranian consumers,” Manag. Sci. Lett., vol. 5, no. 2, pp. 199–204, 2015, doi: 10.5267/j.msl.2014.12.017.

[23] E. M. Abu-Taieh et al., “Continued Intention to Use of M-Banking in Jordan by Integrating UTAUT, TPB, TAM and Service Quality with ML,” J. Open Innov. Technol. Mark. Complex., vol. 8, no. 3, 2022, doi: 10.3390/joitmc8030120.

[24] A. Pratita, S. Tri Lathif Mardi, P. Arista, and A. Wibowo, “ChatGPT in Education: Investigating Students Online Learning Behaviors,” Int. J. Inf. Educ. Technol., vol. 15, no. 3, pp. 510–524, 2025, doi: 10.18178/ijiet.2025.15.3.2262.

[25] M. Alhwaiti, “Acceptance of Artificial Intelligence Application in the Post-Covid Era and Its Impact on Faculty Members’ Occupational Well-being and Teaching Self Efficacy: A Path Analysis Using the UTAUT 2 Model,” Appl. Artif. Intell., vol. 37, no. 1, Dec. 2023, doi: 10.1080/08839514.2023.2175110.

[26] D. Pal, P. Roy, C. Arpnikanondt, and H. Thapliyal, “The effect of trust and its antecedents towards determining users’ behavioral intention with voice-based consumer electronic devices,” Heliyon, vol. 8, no. 4, p. e09271, 2022, doi: 10.1016/j.heliyon.2022.e09271.

[27] B. N. Obenza et al., “Analyzing University Students’ Attitude and Behavior Toward AI Using the Extended Unified Theory of Acceptance and Use of Technology Model,” Am. J. Appl. Stat. Econ., vol. 3, no. 1, pp. 99–108, May 2024, doi: 10.54536/ajase.v3i1.2510.

[28] G. García-Murillo, P. Novoa-Hernández, and R. Serrano Rodríguez, “On the Technological Acceptance of Moodle by Higher Education Faculty—A Nationwide Study Based on UTAUT2,” Behav. Sci. (Basel)., vol. 13, no. 5, p. 419, May 2023, doi: 10.3390/bs13050419.

[29] A. Habibi, M. Muhaimin, B. K. Danibao, Y. G. Wibowo, S. Wahyuni, and A. Octavia, “ChatGPT in higher education learning: Acceptance and use,” Comput. Educ. Artif. Intell., vol. 5, p. 100190, 2023, doi: 10.1016/j.caeai.2023.100190.

[30] V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer Acceptance and Use of Information Technology Extending The Unified Theory of Acceptance and Use of Technology,” MIS Q., vol. 36, no. 1, pp. 157–178, 2012.

[31] M. S. Farooq et al., “Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2,” Interact. Technol. Smart Educ., vol. 14, no. 4, pp. 329–348, 2017, doi: 10.1108/ITSE-06-2016-0015.

[32] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User Acceptance of Information: Towar a Unified View,” MIS Q., vol. 27, no. 3, pp. 425–478, 2003.

[33] C. S. Chai, P.-Y. Lin, M. S. Jong, Y. Dai, T. K. F. Chiu, and B. Huang, “Factors Influencing Students’ Behavioral Intention to Continue Artificial Intelligence Learning,” in 2020 International Symposium on Educational Technology (ISET), IEEE, Aug. 2020, pp. 147–150. doi: 10.1109/ISET49818.2020.00040.

[34] M. Kante and B. Michel, “Computers in Human Behavior Reports Use of partial least squares structural equation modelling ( PLS-SEM ) in privacy and disclosure research on social network sites : A systematic review,” vol. 10, no. April, 2023.

[35] K. K. Wong, “Partial Least Squares Structural Equation Modeling ( PLS-SEM ) Techniques Using SmartPLS,” 2013.

[36] A. Anekawati, B. W. Otok, Purhadi, and Sutikno, “Structural Equation Modelling with Three Schemes Estimation of Score Factors on Partial Least Square (Case Study: The Quality of Education Level SMA/MA in Sumenep Regency),” J. Phys. Conf. Ser., vol. 855, no. 1, 2017, doi: 10.1088/1742-6596/855/1/012006.

[37] A. Ayaz and M. Yanartaş, “An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS),” Comput. Hum. Behav. Reports, vol. 2, no. September, 2020, doi: 10.1016/j.chbr.2020.100032.

[38] N. I. Mohd Rahim, N. A. Iahad, A. F. Yusof, and M. A. Al-Sharafi, “AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach,” Sustain., vol. 14, no. 19, 2022.

[39] D. Dajani and A. S. Abu Hegleh, “Behavior intention of animation usage among university students,” Heliyon, vol. 5, no. 10, p. e02536, 2019, doi: 10.1016/j.heliyon.2019.e02536.

[40] A. A. Arain, Z. Hussain, W. H. Rizvi, and M. S. Vighio, “Extending UTAUT2 toward acceptance of mobile learning in the context of higher education,” Univers. Access Inf. Soc., vol. 18, no. 3, pp. 659–673, 2019, doi: 10.1007/s10209-019-00685-8.

[41] K. Nikolopoulou, V. Gialamas, and K. Lavidas, “Acceptance of mobile phone by university students for their studies: an investigation applying UTAUT2 model,” Educ. Inf. Technol., vol. 25, no. 5, pp. 4139–4155, 2020, doi: 10.1007/s10639-020-10157-9.

[42] M. El-Masri and A. Tarhini, “Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2),” Educ. Technol. Res. Dev., vol. 65, no. 3, pp. 743–763, 2017, doi: 10.1007/s11423-016-9508-8.

[43] A. Ameri, R. Khajouei, A. Ameri, and Y. Jahani, “Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model,” Educ. Inf. Technol., vol. 25, no. 1, pp. 419–435, 2020, doi: 10.1007/s10639-019-09965-5.

[44] A. A. A. Zwain, “Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2,” Interact. Technol. Smart Educ., vol. 16, no. 3, pp. 239–254, 2019, doi: 10.1108/ITSE-09-2018-0065.

[45] M. A. Ayanwale and M. Ndlovu, “Investigating factors of students’ behavioral intentions to adopt chatbot technologies in higher education: Perspective from expanded diffusion theory of innovation,” Comput. Hum. Behav. Reports, vol. 14, p. 100396, May 2024, doi: 10.1016/j.chbr.2024.100396.

[46] F. Kotamena, P. Sinaga, N. Sudibjo, and D. Hidayat, “Student use behavior in determining majors: Is it determined by self-congruity, social influence, information usefulness, through mediating information adoption, and behavioral intention,” Comput. Hum. Behav. Reports, vol. 14, no. June 2023, p. 100383, 2024, doi: 10.1016/j.chbr.2024.100383.

[47] P. A. Ertmer and A. T. Ottenbreit-Leftwich, “Teacher Technology Change,” J. Res. Technol. Educ., vol. 42, no. 3, pp. 255–284, 2010, doi: 10.1080/15391523.2010.10782551.

[48] K. vanLehn, “The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems,” Educ. Psychol., vol. 46, no. 4, pp. 197–221, 2011, doi: 10.1080/00461520.2011.611369.

[49] J. Hamari, D. J. Shernoff, E. Rowe, B. Coller, J. Asbell-Clarke, and T. Edwards, “Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning,” Comput. Human Behav., vol. 54, pp. 170–179, Jan. 2016, doi: 10.1016/J.CHB.2015.07.045.

[50] K. J. Baker-Doyle and S. A. Yoon, “The social side of teacher education: Implications of social network r esearch for the design of professional development,” Int. J. Educ. Res., vol. 101, p. 101563, doi: 10.1016/j.ijer.2020.101563.