AI Literacy, Technical Skills, and Ethical Awareness in Predicting Students’ Learning Performance
DOI:
https://doi.org/10.58524/oler.v6i1.565Keywords:
AI Ethics, AI Literacy, Learning Performance, PLS-SEM , Technical SkillsAbstract
The increasing integration of AI systems into various sectors of the economy has also raised ethical concerns. Even as education in AI has developed to ensure that learners have the appropriate technical skills, the existing systems have failed to address the issue of ethics. As a way of addressing the problem, the current study aims at investigating learning about AI literacy and ethical reasoning. The author in this research applied the Partial Least Squares Structural Equation Modeling (PLS-SEM), from a questionnaire consisting of 400 university students (conducted of informatics and computer engineering department) to examine the relevance of AI literacy (AI-DAIB theories/basics), technical skills (TS), learning performance (LPER and EAI as moderating effect on AI perception (AIP). The findings found interesting result that AI literacy and technical skills have significant effects on learning performance and the moderating effect of AI ethics also increases the added value. This study indicates the need for a wider framework of all educational activities focusing on the development of technical skills, AI literacy and (semi)AI ethics to respond effectively to gaps in both development and moral responsibility of AI technologies
References
Alkhawaja, L., Idris, M., Al-Sayyed, S., & Al Jaber, A. M. (2025). Exploring the impact of artificial intelligence on students’ skills for sustainable development in education. Frontiers in Education, 10, Article 1691148. https://doi.org/10.3389/feduc.2025.1691148
sAmiruddin, A., Baharuddin, F. R., Setialaksana, W., Takbir, T., & Hasim, M. (2023). Structural model of pedagogy–andragogy–heutagogy continuum on pedagogical competencies of Indonesian vocational high school teacher. In Proceedings of the 5th International Conference on Vocational Education and Technology (IConVET 2022). EAI. https://doi.org/10.4108/eai.6-10-2022.2327435
Babashahi, L., Barbosa, C. E., Lima, Y., Lyra, A., Salazar, H., Argôlo, M., Almeida, M. A. de, & Souza, J. M. de. (2024). AI in the workplace: A systematic review of skill transformation in the industry. Administrative Sciences, 14(6), Article 127. https://doi.org/10.3390/admsci14060127
Bellini, V., Russo, M., Domenichetti, T., Panizzi, M., Allai, S., & Bignami, E. G. (2024). Artificial intelligence in operating room management. Journal of Medical Systems, 48(1), Article 19. https://doi.org/10.1007/s10916-024-02038-2
Biagini, G. (2024). Assessing the assessments: Toward a multidimensional approach to AI literacy. Media Education, 15(1), 91–101. https://doi.org/10.36253/me-15831
Bisdas, S., Topriceanu, C. C., Zakrzewska, Z., Irimia, A. V., Shakallis, L., Subhash, J., Casapu, M. M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., Zeicu, C., Bouhuwaish, A. M., Lestari, A. N., Abu-Ismail, L., Sadiq, A. S., Khamees, A., Mohammed, K. M. G., Williams, E., Omran, A. I., & Ebrahim, E. H. (2021). Artificial intelligence in medicine: A multinational multi-center survey on the medical and dental students’ perception. Frontiers in Public Health, 9, Article 795284. https://doi.org/10.3389/fpubh.2021.795284
Black, N. B., George, S., Eguchi, A., Dempsey, J. C., Langran, E., Fraga, L., Brunvand, S., & Howard, N. (2023). A framework for approaching AI education in educator preparation programs. Proceedings of the Association for Computing Machinery. https://doi.org/10.1609/aaai.v38i21.30351
Casal-Otero, L., Català, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10(1), Article 18. https://doi.org/10.1186/s40594-023-00418-7
Chaaban, Y., Qadhi, S., Chen, J., & Du, X. (2024). Understanding researchers’ AI readiness in a higher education context: Q methodology research. Education Sciences, 14(7), Article 709. https://doi.org/10.3390/educsci14070709
Cheah, J., Nitzl, C., Roldán, J. L., Cepeda-Carrión, G., & Gudergan, S. P. (2021). A primer on the conditional mediation analysis in PLS-SEM. SIGMIS Database: The Database for Advances in Information Systems, 52(3), 1–16. https://doi.org/10.1145/3505639.3505645
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent, and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 345–372. https://doi.org/10.1007/s10490-023-09871-y
Choi, J. I., Yang, E., & Goo, E. H. (2024). The effects of an ethics education program on artificial intelligence among middle school students: Analysis of perception and attitude changes. Applied Sciences, 14(4), Article 1588. https://doi.org/10.3390/app14041588
Du, H., Sun, Y., Jiang, H., Islam, A. Y. M. A., & Gu, X. (2024). Exploring the effects of AI literacy in teacher learning: An empirical study. Humanities and Social Sciences Communications, 11(1), Article 3101. https://doi.org/10.1057/s41599-024-03101-6
Dyer, T., & Kim, E. (2021). Anonymous equity research. Journal of Accounting Research, 59(2), 575–611. https://doi.org/10.1111/1475-679X.12359
Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, Article 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of formative measurement models. In Partial least squares structural equation modeling (PLS-SEM) using R (pp. 117–146). Springer. https://doi.org/10.1007/978-3-030-80519-7_5
Hsu, T. C., Chang, Y. S., Chen, M. S., Tsai, I. F., & Yu, C. Y. (2023). A validity and reliability study of the formative model for the indicators of STEAM education creations. Education and Information Technologies, 28(7), 8855–8878. https://doi.org/10.1007/s10639-022-11412-x
Huang, C. H. (2021). Using PLS-SEM model to explore the influencing factors of learning satisfaction in blended learning. Education Sciences, 11(5), Article 249. https://doi.org/10.3390/educsci11050249
Ibrahim, A. B. (2024). Assessing the knowledge and perception of artificial intelligence for teaching and research among lecturers in the faculties of arts in Nigeria. Journal of Global Research in Education and Social Science, 18(2), 25–33. https://doi.org/10.56557/jogress/2024/v18i28671
Jebb, A. T., Ng, V., & Tay, L. (2021). A review of key Likert scale development advances: 1995–2019. Frontiers in Psychology, 12, Article 637547. https://doi.org/10.3389/fpsyg.2021.637547
Judijanto, L., Mohammad, W., Purnamasari, E., & Muthmainah, H. N. (2023). Analysis of reliability, transaction speed, and user experience on information system integration in e-commerce business in Indonesia. Journal of Information Systems and Digital Business, 1(2), 80–89. https://doi.org/10.58812/wsist.v1i02.478
Kapp, K., Sivén, M., Laurén, P., Virtanen, S., Katajavuori, N., & Södervik, I. (2022). Design and usability testing of an augmented reality (AR) environment in pharmacy education—Presenting a pilot study on comparison between AR smart glasses and a mobile device in a laboratory course. Education Sciences, 12(12), Article 854. https://doi.org/10.3390/educsci12120854
Katsuyama, E., Miyawaki, Y., Sada, K., Asano, Y., Hayashi, K., Yamamura, Y., Hiramatsu-Asano, S., Morishita, M., Ohashi, K., Watanabe, H., & Katsuyama, T. (2020). Association of explanatory histological findings and urinary protein and serum creatinine levels at renal biopsy in lupus nephritis: A cross-sectional study. BMC Nephrology, 21, Article 123. https://doi.org/10.1186/s12882-020-01868-9
Kim, N., Park, S., Lee, J., Choi, Y., Shin, S. M., Choi, Y.-S., & Kim, Y. (2017). Qualitative correlation between postoperatively increased vertical dimension and mandibular position in skeletal class III using partial least square path modeling. Maxillofacial Plastic and Reconstructive Surgery, 39, Article 14. https://doi.org/10.1186/s40902-017-0114-4
Kim, S., Jang, Y., Kim, W., Choi, S., Jung, H., Kim, S., & Kim, H. (2021). Why and what to teach: AI curriculum for elementary school. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15569–15576. https://doi.org/10.1609/aaai.v35i17.17833
Komasawa, N., & Yokohira, M. (2023). Learner-centered experience-based medical education in an AI-driven society: A literature review. Cureus, 15(9), Article e46883. https://doi.org/10.7759/cureus.46883
Krisnandi, E. M. (2023). Perbandingan jumlah pendonor darah di unit donor darah Palang Merah Indonesia Kota Bandung tahun 2019 dengan 2020. Health Information: Jurnal Penelitian, 15(Supplement), e817.
Lawless, K. A., & Pellegrino, J. W. (2007). Professional development in integrating technology into teaching and learning: Knowns, unknowns, and ways to pursue better questions and answers. Review of Educational Research, 77(4), 575–614. https://doi.org/10.3102/0034654307309921
Liao, Y., Yin, G., & Fan, X. (2020). The positive lymph node ratio predicts survival in T1–4N1–3M0 non-small cell lung cancer: A nomogram using the SEER database. Frontiers in Oncology, 10, Article 1356. https://doi.org/10.3389/fonc.2020.01356
Liu, C., & Su, H. (2024). Prediction of martensite start temperature of steel combined with expert experience and machine learning. Science and Technology of Advanced Materials, 25(1), Article 2354655. https://doi.org/10.1080/14686996.2024.2354655
Marelić, M., Klasnić, K., & Vukušić Rukavina, T. (2024). Measuring e-professional behavior of doctors of medicine and dental medicine on social networking sites: Indexes construction with formative indicators. JMIR Medical Education, 10, Article e50156. https://doi.org/10.2196/50156
Mohd Dzin, N. H., & Lay, Y. F. (2021). Validity and reliability of adapted self-efficacy scales in Malaysian context using PLS-SEM approach. Education Sciences, 11(11), Article 676. https://doi.org/10.3390/educsci11110676
Ng, P. T., Dung, V. V., Nguyệt, L. M., Khánh Linh, V. T., Thuy, H. T., & Tinh, N. T. (2020). Academic anxiety of Vietnamese secondary school students as a reason for applying online learning. Journal of Intellectual Disability – Diagnosis and Treatment, 8(4), 543–552. https://doi.org/10.6000/2292-2598.2020.08.04.16
Nur’aini, N., & Hamzah , H. (2023). Kecerdasan emosional, intelektual, spiritual, moral dan sosial relevansinya dengan pendidikan agama Islam perspektif Al-Qur’an. Educatio, 9(4), 1783–1790. https://doi.org/10.31949/educatio.v9i4.5867
Oh, V. Y. S. (2023). Direct versus indirect measures of mixed emotions in predictive models: A comparison of predictive validity, multicollinearity, and the influence of confounding variables. Frontiers in Psychology, 14, Article 1231845. https://doi.org/10.3389/fpsyg.2023.1231845
Pala, F. (2023). Metaphorical perceptions of teachers regarding technology-enhanced social studies instruction. International Journal of Education and Literacy Studies, 11(4), 240–252. https://doi.org/10.7575/aiac.ijels.v.11n.4p.240
Reyna-Castillo, M., Pulgarín-Rodríguez, M. A., Ríos-Serna, A. H., & Santiago, A. (2022). PLS-SEM validation for burnout measures in Latino college students: A socially sustainable educational return. Sustainability, 14(21), Article 14635. https://doi.org/10.3390/su142114635
Smith, F., Sadek, M., & Mougenot, C. (2023). Empowering end-users in co-designing AI: An AI literacy card-based toolkit for non-technical audiences. In Proceedings of the British HCI Conference 2023. https://doi.org/10.14236/ewic/BCSHCI2023.2
Tabet, S. M., Lambie, G. W., Jahani, S., & Rasoolimanesh, S. M. (2019). The factor structure of Outcome Questionnaire–45.2 scores using confirmatory tetrad analysis–partial least squares. Journal of Psychoeducational Assessment, 37(8), 1007–1021. https://doi.org/10.1177/0734282919842035
Teng, M., Singla, R., Yau, O., Lamoureux, D., Gupta, A., Hu, Z., Hu, R., Aissiou, A., Eaton, S., Hamm, C., Hu, S., Kelly, D., MacMillan, K. M., Malik, S., Mazzoli, V., Teng, Y. W., Laricheva, M., Jarus, T., & Field, T. S. (2022). Health care students’ perspectives on artificial intelligence: Countrywide survey in Canada. JMIR Medical Education, 8(1), Article e33390. https://doi.org/10.2196/33390
Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024). Curriculum frameworks and educational programs in AI for medical students, residents, and practicing physicians: Scoping review. JMIR Medical Education, 10, Article e54793. https://doi.org/10.2196/54793
Vimalanathan, V., Kunasegaran, V., Alagenthran, K., Balamurugan, R. N. D., & Sandrasaigaran, P. (2022). Integration of casual video games during online learning to relieve stress in students. Malaysian Journal of Science and Advanced Technology, 2(1), 36–41. https://doi.org/10.56532/mjsat.v2i1.38
Wang, Y., & Wang, H. (2024). Mediating effects of artificial intelligence on the relationship between academic engagement and mental health among Chinese college students. Frontiers in Psychology, 15, Article 1477470. https://doi.org/10.3389/fpsyg.2024.1477470
Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2023). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 33(2), 290–324. https://doi.org/10.1007/s40593-022-00293-3
Zhang, R., Gao, L., Long, C., & Ren, P. (2022). Latent profile analysis of children’s moral character and the classing effect on bullying in rural China. International Journal of Environmental Research and Public Health, 19(18), Article 11285. https://doi.org/10.3390/ijerph191811285
Zhao, L., Wu, X., & Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), Article 14549. https://doi.org/10.3390/su142114549
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Devi Miftahul Jannah, Syamsul Huda, Muhamad Yasin, M Miftach Fakhri, Syahid Nur Wahid, Andi Baso Kaswar, Soeharto Soeharto, Stephen Amukune
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Online Learning in Educational Research is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Online Learning in Educational Research agree to the following terms:
Copyright Retention: Authors retain the copyright of their work without any restrictions.
Publishing Rights: Authors retain the right to publish and distribute their work without any restrictions.
License Agreement: By publishing with Online Learning in Educational Research, authors agree that their work will be licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA). This license allows others to share and adapt the work, provided that appropriate credit is given, any changes are indicated, and the new creations are licensed under the same terms.
