Innovation Utilizing AI-Based Virtual Assistants for Employee Training and Development

Authors

  • Tri Cicik Wijayanti Universitas Gajayana
  • Aditya Dandy Firatama Universitas Gajayana
  • M Jamal Abdul Nasir Universitas Gajayana
  • Aji Saptaji Universitas Gajayana

Keywords:

Artificial Intelligence, Employee Training, Human Resource Development, Human Resource Management, Virtual Assistent

Abstract

This study examines the effectiveness of using AI-based virtual assistants in employee training and development. Employing a quantitative approach, data were collected through an online survey of 150 respondents working in various companies in Gresik Regency. The study aims to evaluate employees' perceptions of AI-based virtual assistants, focusing on their effectiveness, ease of use, learning experience, motivation, and impact on skill enhancement and productivity. Descriptive analysis revealed positive perceptions across all measured aspects, with average Likert scale scores above 4.0, indicating strong acceptance of thistechnology. Pearson correlation analysis showed significant positive relationships among the variables, suggesting that a positive perception of one aspect correlates with positive perceptions of others. Multiple linear regression analysis identified "Learning Experience through Virtual Assistants" as the most significant predictor of employee performance improvement. These findings highlight the potential of AI in enhancing employee training and development programs, providing valuable insights for HR practitioners and policymakers in designing more effective training strategies.

References

Almulla, M. A., & Al-Rahmi, W. M. (2023). Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability. Sustainability (Switzerland), 15(5). https://doi.org/10.3390/su15053978

Atoubi, E. M., & Jahidi, R. (2024). Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling. Intelligent Information Management, 16(02), 104–120. https://doi.org/10.4236/iim.2024.162007

Bucher, A., Schenk, B., & Schwabe, G. (2024). When Generative Ai Meets Workplace Learning-Creating a Realistic & Motivating Learning Experience With a Generative Pca. Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024), Ecis, 1–16.

Duricic, T., Müllner, P., Weidinger, N., ElSayed, N., Kowald, D., & Veas, E. (2024). AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments. Frontiers in Artificial Intelligence and Applications, 392, 4491–4494. https://doi.org/10.3233/FAIA241037

Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(7), 378–392. https://doi.org/10.1038/s44159-022-00056-w

Ganji, S., & Rao, V. S. (2024). Training and Development Needs Analysis with Special Reference to Software Companies in Hyderabad. International Journal For Multidisciplinary Research, 6(3), 1–14. https://doi.org/10.36948/ijfmr.2024.v06i03.19476

Hussain, M. A. (2024). The Impact of Artificial Intelligence on Education. International Journal of Innovative Research in Multidisciplinary Education, 03(04), 11–21. https://doi.org/10.58806/ijirme.2024.v3i4n01

Jayakarthika K, Jisha Aravind, Ilakkiya N, Karthiga G, Ameera Farha, & Varun C M. (2024). Unlocking the Potential: HRM Integration of AI in the Gig Economy for Enhanced ERP Performance. International Research Journal on Advanced Engineering Hub (IRJAEH), 2(06), 1683–1687. https://doi.org/10.47392/irjaeh.2024.0232

Kudus, Norliah, Sedek, Muliati, Abu Hassan, & Mahadi, Ab Rahman, Z. (2023). Factors Influencing Effectivess of Traning, Resilience Factor and Development on Employee Performance. Journal of Namibian Studies : History Politics Culture, 34, 2411–2433. https://doi.org/10.59670/jns.v34i.1519

Kurdoğlu, M., & Khaki, A. (2024). The Use of Artificial Intelligence in Urogynecology. International Journal of Women’s Health and Reproduction Sciences, 12(1), 1–2. https://doi.org/10.21037/gpm-21-41

Lee, Z. (2023). Overcoming Challenges in Corporate Training: A Framework for Effective Training Initiatives. Open Journal of Business and Management, 11(05), 2472–2487. https://doi.org/10.4236/ojbm.2023.115137

Manik, S., Sembiring, M., Padang, I., & Manurung, L. (2022). Theory of Bandura’s Social Learning in The Process Of Teaching at SMA Methodist Berastagi Kabupaten Karo. Jurnal Visi Pengabdian Kepada Masyarakat, 3(2), 85–96. https://doi.org/10.51622/pengabdian.v3i2.729

Meyer, J. P., & Gagnè, M. (2008). Employee Engagement From a Self-Determination Theory Perspective. Industrial and Organizational Psychology, 1(1), 60–62. https://doi.org/10.1111/j.1754-9434.2007.00010.x

Mr. Bhushan Girase, & Mr. Pranjal Bobade. (2024). The Implications of Artificial Intelligence on the Employment Sector. International Journal of Advanced Research in Science, Communication and Technology, 319–327. https://doi.org/10.48175/ijarsct-18934

Navya, S. T., & Chandrasha, R. (2023). Impact of Training and Development on Organizational Effectiveness: A Study on Private Sector Bank. GBS Impact: Journal of Multi Disciplinary Research, 9(1), 94–102. https://doi.org/10.58419/gbs.v9i1.912310

Ostin, V. (2024). Navigating the future of soft skills: Integrating Arficial Intelligence for employee training success. Proceedings of the 17th International Scientific Conference INPROFORUM, 17, 305–310. https://doi.org/10.32725/978-80-7694-053-6.46

Palade, M., & Carutasu, G. (2023). “Organizational Readiness for Artificial Intelligence Adoption.” Scientific Bulletin of the Politehnica University of Timişoara Transactions on Engineering and Management, 7(1–2), 30–35. https://doi.org/10.59168/fdms6321

Pauluzzo, R. (2020). Learning tools to develop cultural intelligence for SMFEs: the role of social cognitive processes. Sinergie, 38(2), 133–150. https://doi.org/10.7433/s112.2020.07

Peer, S., Journals, R., & Publishing, B. (2023). The Effect of Training on Employee Performance in the Public Sector of Rwanda, A Case Study of The Ministry of Finance and Economic Planning. Journal of Human Resource &Leadership, 7(6), 130–140. https://doi.org/10.53819/81018102t2295

Prasad, K. D. V., Vaidya, R., & Rani, R. (2023). A critical investigation analysing the key determinants of artificial intelligence in enhancing employee engagement in multinational companies. Humanities and Social Sciences Letters, 11(2), 228–237. https://doi.org/10.18488/73.v11i2.3388

Sharma, A. (2023). Challenges for Human Resource Management in the Era of Dynamically Changing Technology: A Quantitative Investigation. PsychologyandEducation, 55(1), 478–485. https://doi.org/10.48047/pne.2018.55.1.59

Weitz, K., Dang, C. T., & André, E. (2023). Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees’ Perspective. http://arxiv.org/abs/2210.03527

Werens, S., & von Garrel, J. (2023). Implementation of artificial intelligence at the workplace, considering the work ability of employees. Zeitschrift Fur Technikfolgenabschatzung in Theorie Und Praxis / Journal for Technology Assessment in Theory and Practice, 32(2), 43–49. https://doi.org/10.14512/tatup.32.2.43

Published

10-01-2026

How to Cite

Cicik Wijayanti, T., Firatama, A. D., Abdul Nasir, M. J., & Saptaji, A. (2026). Innovation Utilizing AI-Based Virtual Assistants for Employee Training and Development. Jurnal Manajemen, 17(1). Retrieved from http://150.107.142.43/index.php/manajemen/article/view/22008

Issue

Vol. 17 No. 1 (2026): Inpress Edisi Februari 2026 Jurnal Manajemen (Edisi Elektronik)

Section

Articles

License

Copyright (c) 2026 Jurnal Manajemen

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).