Pengaruh AI Terhadap Penerimaan dan Adopsi Sistem dalam Penggunaan Rumah Pendidikan

Romdan Romdan; Yulianti Yulianti; Amri Amri

doi:10.31004/riggs.v5i1.6639

Authors

Romdan Romdan ISB Atma Luhur Pangkal Pinang
Yulianti Yulianti ISB Atma Luhur Pangkal Pinang
Amri Amri ISB Atma Luhur Pangkal Pinang

DOI:

https://doi.org/10.31004/riggs.v5i1.6639

Keywords:

Artificial Intelegence, Penerimaan Teknologi, Adopsi Sistem, Rumah Pendidikan, Transformasi Digital Pendidikan

Abstract

Transformasi digital pendidikan mendorong pemerintah mengembangkan platform layanan terpadu berbasis teknologi, salah satunya melalui implementasi Rumah Digital/Rumah Pendidikan. Integrasi Artificial Intelligence (AI) dalam platform ini diyakini mampu meningkatkan kualitas layanan, kemudahan penggunaan, serta efektivitas pengelolaan pendidikan. Namun demikian, tingkat penerimaan dan adopsi sistem oleh pengguna di daerah masih menunjukkan variasi, termasuk di Kabupaten Bangka. Penelitian ini bertujuan menganalisis pengaruh Artificial Intelligence terhadap penerimaan dan adopsi sistem dalam penggunaan Rumah pendidikanpada satuan pendidikan di Kabupaten Bangka. Penelitian menggunakan pendekatan kuantitatif dengan desain explanatory research melalui survei cross-sectional terhadap 214 responden yang terdiri atas guru, tenaga kependidikan, operator sekolah, dan kepala sekolah. Teknik analisis data menggunakan Structural Equation Modeling (SEM) berbasis Partial Least Square (PLS) dengan bantuan SmartPLS. Hasil penelitian menunjukkan bahwa Artificial Intelligence berpengaruh positif dan signifikan terhadap perceived usefulness dan perceived ease of use. Selanjutnya, kedua variabel tersebut berpengaruh signifikan terhadap behavioral intention yang berdampak langsung pada adoption/use behavior. Pengaruh tidak langsung AI melalui variabel mediasi terbukti lebih dominan dibanding pengaruh langsung. Temuan ini menegaskan bahwa persepsi kemanfaatan menjadi faktor paling menentukan dalam membentuk adopsi sistem. Penelitian menyimpulkan bahwa integrasi Artificial Intelligence berperan strategis dalam meningkatkan penerimaan dan adopsi sistem Rumah pendidikandi Kabupaten Bangka. Implikasi penelitian menekankan pentingnya penguatan fitur AI berbasis kebutuhan pengguna, peningkatan literasi digital, serta dukungan infrastruktur teknologi dalam mendorong keberhasilan transformasi digital pendidikan daerah.

Downloads

Download data is not yet available.

References

K ementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, Peta Jalan Transformasi Digital Pendidikan Indonesia 2021–2024. Jakarta: Kemendikbudristek, 2021.

Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, Arsitektur dan Ekosistem Platform Teknologi Pendidikan (Rumah Pendidikan). Jakarta: Kemendikbudristek, 2023.

O. Zawacki-Richter and I. Jung, “Artificial intelligence and inclusive education: A systematic review,” Computers and Education: Artificial Intelligence, vol. 4, 2023.

M. Chen, Y. Wu, and Y. Liu, “Artificial intelligence in education: Applications, challenges, and future directions,” IEEE Access, vol. 10, pp. 75264–75278, 2022.

A. M. Al-Adwan, N. Al-Rahmi, and M. Albelbisi, “Artificial intelligence driven e-learning adoption: An empirical study,” Education and Information Technologies, vol. 27, no. 6, pp. 9011–9035, 2022.

S. K. Sharma and M. Sharma, “Examining the role of AI in technology acceptance in digital learning environments,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, 2023.

H. Taherdoost, “A review of technology acceptance and adoption models and theories,” Procedia Computer Science, vol. 181, pp. 960–967, 2021.

R. Scherer, F. Siddiq, and D. Sánchez-Viveros, “The role of teachers’ digital competence in technology acceptance,” Computers & Education, vol. 179, 2022.

M. Bond, S. Bedenlier, V. I. Marín, and O. Zawacki-Richter, “Digital transformation in education: A systematic review,” Educational Technology Research and Development, vol. 70, no. 4, pp. 1911–1945, 2022.

Y. K. Dwivedi et al., “Metaverse, AI, and the future of digital platforms: Implications for education,” International Journal of Information Management, vol. 65, 2022.

P. Ifinedo, “Artificial intelligence and user satisfaction in digital platforms,” Information Systems Frontiers, vol. 25, no. 3, pp. 987–1003, 2023.

S. Papagiannidis, E. Harris, and M. Morton, “AI adoption and trust in digital systems,” Technological Forecasting and Social Change, vol. 179, 2022.

A. Haleem, M. Javaid, and R. P. Singh, “Artificial intelligence applications in education: A review,” Sustainable Operations and Computers, vol. 2, pp. 1–11, 2021.

UNESCO, AI and Education: Guidance for Policy-Makers (Updated Edition). Paris: UNESCO, 2022.

World Bank, Digital Transformation in Education Systems: Global Perspectives. Washington, DC: World Bank, 2023.

J. W. Creswell and J. D. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th ed. Thousand Oaks, CA: Sage, 2021.

V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology,” MIS Quarterly, vol. 46, no. 2, pp. 1–28, 2022.

R. Kumar, Research Methodology: A Step-by-Step Guide for Beginners, 5th ed. London: Sage, 2021

J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed. Thousand Oaks, CA: Sage, 2022.

S. K. Sharma and M. Sharma, “Role of artificial intelligence in technology acceptance,” International Journal of Educational Technology in Higher Education, vol. 20, no. 1, 2023.

A. M. Al-Adwan et al., “AI-driven learning adoption model,” Education and Information Technologies, vol. 27, 2022.

L. Cohen, L. Manion, and K. Morrison, Research Methods in Education, 9th ed. London: Routledge, 2021.

J. Henseler, C. M. Ringle, and M. Sarstedt, “Testing measurement models in PLS-SEM,” Journal of Marketing Analytics, vol. 9, no. 3, pp. 177–195, 2021.

C. M. Ringle, S. Wende, and J. Becker, SmartPLS 4 User Guide. Boenningstedt: SmartPLS GmbH, 2023.

M. Sarstedt, J. F. Hair, J. Cheah, H. Becker, and C. M. Ringle, “Structural model evaluation in PLS-SEM,” Industrial Management & Data Systems, vol. 121, no. 12, pp. 2631–2650, 2021.

J. F. Hair, M. Sarstedt, C. M. Ringle, and S. P. Gudergan, Advanced Issues in Partial Least Squares Structural Equation Modeling. Thousand Oaks, CA: Sage, 2021.

N. Marangunić and A. Granić, “Technology acceptance model in educational contexts: Recent developments,” Education and Information Technologies, vol. 27, 2022.

M. Chen, Y. Wu, and Y. Liu, “Artificial intelligence adoption in education: Empirical evidence,” vol. 10, 2022.