Bridging Academic Performance and Employability Skills through AI Adoption: Evidence from Vocational Higher Education in Indonesia

Retta Farah Pramesti, Febrynahl Fitra Fadilla, Raden Rifqi Dwisanto

Abstract

The rapid diffusion of artificial intelligence (AI) in higher education has intensified attention on technology-enhanced learning practices, particularly regarding their potential to strengthen employability skills in vocational education. The main challenge lies in understanding how AI-enabled personalized learning contributes to employability development beyond improving academic efficiency alone. This study aims to examine the effect of AI-enabled personalized learning on employability skills among vocational higher education students in Indonesia by integrating Self-Determination Theory and Human Capital Theory. A quantitative causal approach was applied using cross-sectional survey data collected from 260 vocational students at Universitas Padjadjaran and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results show that employability skills are significantly influenced by relatedness, student engagement, academic performance, and institutional support, while autonomy and competence do not exert direct effects. Furthermore, AI-enabled personalized learning significantly enhances students’ autonomy, competence, relatedness, engagement, and academic performance, and has a direct positive effect on employability skills. Mediation analysis indicates that relatedness and academic performance are the primary mechanisms through which AI contributes to employability. These findings highlight that AI most effectively enhances employability when embedded within socially interactive and performance-oriented vocational learning environments.

 

Keywords: Artificial intelligence; Employability; Vocational education; Motivation; Academic performance.

 

DOI https://doi.org/10.55463/issn.1674-2974.53.4.6


Full Text:

PDF


References


Aboagye, E., Yawson, J. A., & Appiah, K. N. (2020). COVID-19 and E-Learning: The Challenges of Students in Tertiary Institutions. Social Education Research, 1–8. https://doi.org/10.37256/ser.212021422

Adewale, M. D., Azeta, A., Abayomi-Alli, A., & Sambo-Magaji, A. (2024). Impact of artificial intelligence adoption on students’ academic performance in open and distance learning: A systematic literature review. Heliyon, 10(22), e40025. https://doi.org/10.1016/j.heliyon.2024.e40025

Auerbach, P., & Green, F. (2025). Reformulating the Critique of Human Capital Theory. Journal of Economic Surveys, 39(5), 1839–1851. https://doi.org/10.1111/joes.12675

Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education (3rd ed.). University of Chicago Press.

Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18(1), 50. https://doi.org/10.1186/s41239-021-00282-x

Capone, R., & Lepore, M. (2022). From Distance Learning to Integrated Digital Learning: A Fuzzy Cognitive Analysis Focused on Engagement, Motivation, and Participation During COVID-19 Pandemic. Technology, Knowledge and Learning, 27(4), 1259–1289. https://doi.org/10.1007/s10758-021-09571-w

Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), 2. https://doi.org/10.1080/15391523.2021.1891998

Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2024). Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 32(7), 3240–3256. https://doi.org/10.1080/10494820.2023.2172044

Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies, 29(14), 18695–18744. https://doi.org/10.1007/s10639-024-12599-x

Daniel, G. R., Hanssen, T.-E. S., & Roos, M. (2025). Can generative AI revolutionise academic skills development in higher education? A systematic literature review. European Journal of Education, 60(1), e70036. https://doi.org/10.1111/ejed.70036

Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Dekker, I., De Jong, E. M., Schippers, M. C., De Bruijn-Smolders, M., Alexiou, A., & Giesbers, B. (2020). Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting. Frontiers in Psychology, 11, 1063. https://doi.org/10.3389/fpsyg.2020.01063

Dwivedi, S., & Vig, S. (2024). Blockchain adoption in higher-education institutions in India: Identifying the main challenges. Cogent Education, 11(1), 2292887. https://doi.org/10.1080/2331186X.2023.2292887

Ellikkal, A., & Rajamohan, S. (2025). AI-enabled personalized learning: Empowering management students for improving engagement and academic performance. VILAKSHAN - XIMB Journal of Management, 22(1), 28–44. https://doi.org/10.1108/XJM-02-2024-0023

Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of research in interactive marketing, 17(5), 663-680.

Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886

Jackson, D., & Tomlinson, M. (2022). The relative importance of work experience, extra-curricular and university-based activities on student employability. Higher Education Research & Development, 41(4), 1119–1135. https://doi.org/10.1080/07294360.2021.1901663

Jeno, L. M., Grotle Rundereim, K., & Grytnes, J. A. (2025). An experimental comparison between two mobile apps for species identification from the lens of self-determination theory. Technology, Pedagogy and Education, 1–19. https://doi.org/10.1080/1475939X.2025.2511039

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information systems journal, 28(1), 227-261.

Li, Y., Zhou, X., & Chiu, T. K. F. (2025). Systematics review on artificial intelligence chatbots and ChatGPT for language learning and research from self-determination theory (SDT): What are the roles of teachers? Interactive Learning Environments, 33(3), 1850–1864. https://doi.org/10.1080/10494820.2024.2400090

Mehmood, K., Verleye, K., De Keyser, A., & Lariviere, B. (2024). The transformative potential of AI-enabled personalization across cultures. Journal of Services Marketing, 38(6), 711-730.

Ng, J. Y. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., & Williams, G. C. (2012). Self-Determination Theory Applied to Health Contexts: A Meta-Analysis. Perspectives on Psychological Science, 7(4), 325–340. https://doi.org/10.1177/1745691612447309

Portocarrero Ramos, H. C., Cruz Caro, O., Sánchez Bardales, E., Quiñones Huatangari, L., Campos Trigoso, J. A., Maicelo Guevara, J. L., & Chávez Santos, R. (2025). Artificial intelligence skills and their impact on the employability of university graduates. Frontiers in Artificial Intelligence, 8, 1629320. https://doi.org/10.3389/frai.2025.1629320

Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., Allam, M., Alvarez-Risco, A., Amorim, J. P., Andونrsons, A., Arthur, Y. D., Aydın, F., Badiozaman, I. F. bt. A., Bakry, A., Balbontín-Alvarado, R., … Aristovnik, A. (2025). Higher education students' perceptions of ChatGPT: A global study of early reactions. PLOS ONE, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011

Rokkones, R. K., & Giannakos, M. (2025). Toward hybrid teaching intelligence: Investigating the potential of teacher–AI collaboration using large language models. Behaviour & Information Technology, 1–19. https://doi.org/10.1080/0144929X.2025.2564368

Rothwell, A., Herbert, I., & Rothwell, F. (2008). Self-perceived employability: Construction and initial validation of a scale for university students. Journal of Vocational Behavior, 73(1), 1–12. https://doi.org/10.1016/j.jvb.2007.12.001

Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860

Suleman, F. (2018). The employability skills of higher education graduates: Insights into conceptual frameworks and methodological options. Higher Education, 76(2), 263–278. https://doi.org/10.1007/s10734-017-0207-0

Truong, Y., & Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review and future research agenda. Technological Forecasting and Social Change, 183, 121852. https://doi.org/10.1016/j.techfore.2022.121852

Wang, Y., Liu, J., & Zhang, H. (2025). The influence of employability skills on quality of employment in AI-driven labour market transformations: The roles of academic achievement and motivation. Humanities and Social Sciences Communications, 12, 1612. https://doi.org/10.1057/s41599-025-05872-y

Yoto, Y., Marsono, M., Suyetno, A., Nurhadi, D., Romadin, A., Paryono, P., & Nurhadi, D. (2025). Unlocking workforce readiness through digital employability skills in vocational education graduates: A PLS-SEM analysis based on human capital theory. Social Sciences & Humanities Open, 11, 101625. https://doi.org/10.1016/j.ssaho.2025.101625


Refbacks

  • There are currently no refbacks.