Integrating Artificial Intelligence in Vocational Higher Education: A Case Study at Politeknik Penerbangan Makassar
Abstract
This study seeks to gauge the implementation, effectiveness, and lecturers’ perceptions of artificial intelligence (AI) integration in teaching at Politeknik Penerbangan Makassar, Indonesia. A mixed-methods approach was employed, utilizing Likert-scale questionnaires to measure implementation and effectiveness, and open-ended questionnaires to assess lecturers’ perceptions. The quantitative findings indicate that AI implementation is generally effective (mean = 4.00), with institutional policies and opportunities for student innovation being key factors, while infrastructure presents an obstacle. Qualitative results indicate that lecturers view AI as a transformative tool for enhancing teaching efficiency, providing instant and consistent feedback, and fostering creativity and collaboration. However, despite its benefits, lecturers emphasize the essential nature of human guidance, such as contextual feedback, emotional support, or ethical oversight. The novelty of the study’s approach lies in its focus on vocational higher education, a subject that has relatively little empirical evidence, and demonstrates how the introduction of AI can supplement rather than replace pedagogues’ work in learning. The implications of the findings provide practical guidance for policymakers and educators seeking more comprehensive institutional policies, targeted faculty development, and investment in technological infrastructure to leverage AI in teaching and learning effectively.
PDF Download: 32
SIMILARITY CHECK Download: 17
References
Alamri, W. A. (2019). Communicative language teaching: Possible alternative approaches to CLT and teaching contexts. English Language Teaching, 12(8), 73–93. https://doi.org/10.5539/elt.v11n10p132 DOI: https://doi.org/10.5539/elt.v11n10p132
Alrawashdeh, A. H., & Al-Zayed, N. N. (2019). The effect of a computer program based on virtual reality on students’ achievement in physics. Journal of Education and Learning, 8(1), 23–35. https://doi.org/10.5539/jel.v8n1p23
Alsaiari, O., Baghaei, N., Lahza, H., Lodge, J., Boden, M., & Khosravi, H. (2024). Emotionally Enriched Feedback via Generative AI. http://arxiv.org/abs/2410.15077
Al-Samarraie, H., & Saeed, N. (2018). A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment. Computers & Education, 124, 77–91. https://doi.org/10.1016/j.compedu.2018.05.016 DOI: https://doi.org/10.1016/j.compedu.2018.05.016
Authors. (2025). Effects of an AI-supported approach to peer feedback in EFL contexts. [Details incomplete].
Authors. (2025). Emotionally enriched feedback study. [Details incomplete].
Atoulloh, A., Fitriani, A., & Daryono, R. W. (2024). Exploring the Influence of Game-Based Learning and School Environment on Learning Achievement: Does the Mediation of Self-Intention Matter, International Journal of Recent Educational Research (IJORER), 5(3). DOI: https://doi.org/10.46245/ijorer.v5i3.597
Aydin, S. (2019). Technology and foreign language teaching: A literature review. Journal of Language and Linguistic Studies, 15(1), 1–12.
Bahari, A. (2021). Artificial intelligence in education: Opportunities and challenges. Journal of Artificial Intelligence in Education, 31(2), 215–232. https://doi.org/10.1007/s40593-021-00258-0
Boholano, H. B. (2017). Smart social networking: 21st century teaching and learning skills. Research in Pedagogy, 7(1), 21–29. https://doi.org/10.17810/2015.45 DOI: https://doi.org/10.17810/2015.45
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 10(3), 1–15. http://dx.doi.org/10.18608/jla.2019.62.1 DOI: https://doi.org/10.18608/jla.2019.62.1
Cabot, J., & Ciurana, E. (2019). Web 2.0 design patterns. Springer. https://doi.org/10.1007/978-1-4842-4191-8
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3 DOI: https://doi.org/10.1186/s41239-023-00408-3
Creswell, J. W., & Creswell, J. D. (2022). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Crompton, H., Edmett, A., Ichaporia, N., & Burke, D. (2024). AI and English language teaching: Affordances and challenges. British Journal of Educational Technology, 55(6), 2503–2529. https://doi.org/10.1111/bjet.13460 DOI: https://doi.org/10.1111/bjet.13460
Çukurova, M., Luckin, R., & Clark-Wilson, A. (2019). Creating the golden triangle of evidence-informed education technology with EDUCATE. British Journal of Educational Technology, 50(2), 490–504. https://doi.org/10.1111/bjet.12727 DOI: https://doi.org/10.1111/bjet.12727
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 DOI: https://doi.org/10.1080/14703297.2023.2190148
Dignum, V. (2018). Ethics in artificial intelligence: Introduction to the special issue. Ethics and Information Technology, 20(1), 1–3. https://doi.org/10.1007/s10676-018-9450-z DOI: https://doi.org/10.1007/s10676-018-9450-z
Fitria, T. N. (2021). Artificial intelligence in education: A study of ChatGPT in academic writing. Indonesian Journal of English Language Teaching and Applied Linguistics, 6(2), 245–260. https://doi.org/10.21093/ijeltal.v6i2.1112
Fryer, L. K., & Bovee, H. N. (2018). Staying motivated to learn: Personality, academic orientation and teachers’ competence. Educational Psychology, 38(6), 743–760. https://doi.org/10.1080/01443410.2018.1457775
Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084 DOI: https://doi.org/10.3390/mti9080084
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. M. T., Cukurova, M., & Bittencourt, I. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 31(4), 1–23. https://doi.org/10.1007/s40593-021-00239-1 DOI: https://doi.org/10.1007/s40593-021-00239-1
Indonesian Ministry of Education and Culture. (2003). Undang-Undang Republik Indonesia Nomor 20 Tahun 2003 tentang Sistem Pendidikan Nasional.
Indonesian Ministry of Education and Culture. (2013). Peraturan Menteri Pendidikan dan Kebudayaan Republik Indonesia Nomor 70 Tahun 2013 tentang Kerangka Dasar dan Struktur Kurikulum Sekolah Menengah Kejuruan/Madrasah Aliyah Kejuruan.
Indonesian Ministry of Education, Culture, Research, and Technology. (2022). Kurikulum Merdeka: Buku saku.
M. Kandlhofer, G. Steinbauer, S. Hirschmugl-Gaisch and P. Huber, "Artificial intelligence and computer science in education: From kindergarten to university," 2016 IEEE Frontiers in Education Conference (FIE), Erie, PA, USA, 2016, pp. 1-9, doi: 10.1109/FIE.2016.7757570. DOI: https://doi.org/10.1109/FIE.2016.7757570
Kukulska-Hulme, A. (2020). Mobile-assisted language learning [MALL]: A selected annotated bibliography (2005–2019). Language Learning & Technology, 24(2), 1–23. https://doi.org/10125/44782
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420 DOI: https://doi.org/10.3102/0034654315581420
Lin, C. C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. In Smart Learning Environments (Vol. 10, Issue 1). Springer. https://doi.org/10.1186/s40561-023-00260-y DOI: https://doi.org/10.1186/s40561-023-00260-y
Li, Y., Zhang, X., & Chen, Z. (2025). Impact of Industry and Education Integration on Employment Quality in Higher Vocational Colleges: Moderating Role of Faculty Qualifications and Curriculum Development Capacity. Education Sciences, 15(10), 1316. https://www.mdpi.com/2227-7102/15/10/1316 DOI: https://doi.org/10.3390/educsci15101316
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. NPJ Science of Learning, 10(1), 29. https://doi.org/10.1038/s41539-025-00320-7 DOI: https://doi.org/10.1038/s41539-025-00320-7
Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781108894333 DOI: https://doi.org/10.1017/9781108894333.003
Nguyen, A., & Bui, T. (2022). Artificial intelligence in education: Opportunities, challenges, and policy implications. Journal of Educational Technology & Society, 25(1), 1–14.
Novianti, I. (2025). An exploratory study on the challenges of AI technology in education and its practical recommendations. International Journal of Social Science Research and Review, 8(1), 26–37. https://doi.org/10.47814/ijssrr.v8i3.2481 DOI: https://doi.org/10.47814/ijssrr.v8i3.2481
OECD. (2021). AI and the future of skills: Policy implications. OECD Publishing. https://doi.org/10.1787/5ee7f09b-en
Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. DOI: https://doi.org/10.1016/j.caeai.2021.100033
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8 DOI: https://doi.org/10.1186/s41039-017-0062-8
Qassrawi, R. (2025). A meta-analysis of AI-based feedback systems in education. Journal of Educational Computing Research. Advance online publication. https://doi.org/10.1007/978-981-96-2532-1_20 DOI: https://doi.org/10.1007/978-981-96-2532-1_20
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Rosyadi, M. I., Kustiawan, I., Tetehfio, E. O., & Joshua, Q. (2023). The Role of AI in Vocational Education: A Systematic Literature Review. Journal of Vocational Education Studies (JOVES), 6(2), 122–136. https://doi.org/10.12928/joves.v6i2.9032 DOI: https://doi.org/10.12928/joves.v6i2.9032
Shi, L., & Aryadoust, V. (2024). Automated written feedback and its impact on procedural writing skills. Language Learning & Technology, 28(1), 45–64. https://doi.org/10125/73901
UNESCO. (2021). AI and education: Guidance for policymakers. UNESCO Publishing.
Yang, X., Gao, Y., & Shen, J. (2024). Student interaction with AI-generated feedback: Opportunities and challenges. Computers & Education, 200, 104790. https://doi.org/10.1016/j.compedu.2023.104790 DOI: https://doi.org/10.1016/j.compedu.2023.104790
Zheng, Y., Li, X., & Wang, H. (2024). Simulation platform for intelligent manufacturing education: Reinforcing technical competencies through robotics experimentation. International Journal of Engineering Education, 40(2), 120–134.
Copyright (c) 2025 M. Agung Raharjo, Ahmad Rossydi, Fachrurrazy Burhanuddin (Author); Adhitya Octavianie; Nining Idyaningsih (Author); Sukarman

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
PDF downloaded = 32 times
SIMILARITY CHECK downloaded = 17 times
























