Educational-Stage Profiles of Learners’ Trust in Artificial Intelligence Across Secondary and Higher Education Contexts

Fredi Ganda Putra; Khoirunnisa  Imama

doi:10.58524/aidie.v2i1.155

Authors

Fredi Ganda Putra Universitas Islam Negeri Raden Intan Lampung Author
Khoirunnisa Imama UIN Raden Intan Lampung Author

DOI:

https://doi.org/10.58524/aidie.v2i1.155

Keywords:

AI trust, AI literacy, educational technology, perceived transparency

Abstract

Trust in artificial intelligence (AI) is increasingly relevant to educational technology adoption, yet evidence remains limited on how learners at different educational stages evaluate AI systems. This cross-sectional survey examined AI trust among 412 students from secondary schools and universities in Lampung Province, Indonesia. The study assessed AI trust, AI literacy, prior AI experience, perceived AI transparency, and perceived institutional AI integration policy. Descriptive analyses, independent-samples t-tests, hierarchical multiple regression, bootstrapped mediation, and moderated mediation were used to estimate educational-stage differences and conditional indirect associations. Higher education students reported higher composite AI trust than secondary school students (M = 70.63 vs. M = 57.04, p < .001, d = 1.21). AI literacy and perceived transparency were positively associated with AI trust after controlling for gender, educational stage, and prior AI experience. Perceived transparency partially accounted for the association between AI literacy and trust (indirect effect = 0.17, 95% CI [0.11, 0.24]), and institutional AI integration policy strengthened the AI literacy-transparency pathway. Because the design was cross-sectional and based on self-report data, the findings should be interpreted as associational rather than causal or developmental evidence. The study suggests that AI literacy curricula should explicitly develop learners’ ability to evaluate transparency, uncertainty, and appropriate reliance when using AI-supported educational tools.

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References

Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00444-7

Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., ... Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14, Article 1983. https://doi.org/10.1038/s41598-024-52549-8

Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), Article 100014. https://doi.org/10.1016/j.chbah.2023.100014

Casal-Otero, L., Catala, 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, Article 29. https://doi.org/10.1186/s40594-023-00418-7

Çelik, I. (2023). Exploring the determinants of artificial intelligence (AI) literacy: Digital divide, computational thinking, cognitive absorption. Telematics and Informatics, 83, Article 102026. https://doi.org/10.1016/j.tele.2023.102026

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, Article 38. https://doi.org/10.1186/s41239-023-00408-3

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8

Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10, Article 60. https://doi.org/10.1186/s40561-023-00269-3

Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10, Article 64. https://doi.org/10.1186/s40561-023-00284-4

Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, Article 100197. https://doi.org/10.1016/j.caeai.2023.100197

Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8

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

Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., & Spirgi, L. (2024). First-year students’ AI competence as a predictor for intended and de facto use of AI tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education, 21, Article 18. https://doi.org/10.1186/s41239-024-00452-7

Ghotbi, N., Ho, M. T., & Mantello, P. (2022). Attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI & Society, 37(1), 283-290. https://doi.org/10.1007/s00146-021-01168-2

Goretzko, D., Siemund, K., & Sterner, P. (2023). Evaluating model fit of measurement models in confirmatory factor analysis. Educational and Psychological Measurement, 84(1), 123-144. https://doi.org/10.1177/00131644231163813

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

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Buckingham Shum, S., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide agenda. Journal of Learning Analytics, 9(1), 1-24. https://doi.org/10.18608/jla.2022.7227

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, Article 100074. https://doi.org/10.1016/j.caeai.2022.100074

Kizilcec, R. F. (2023). To advance AI use in education, focus on understanding educators. International Journal of Artificial Intelligence in Education, 33, 12-19. https://doi.org/10.1007/s40593-023-00351-4

Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). Meta AI literacy scale: Further validation and development of a short version. Heliyon, 10(21), Article e39686. https://doi.org/10.1016/j.heliyon.2024.e39686

Kong, S. C., Cheung, M.-Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence, 6, Article 100214. https://doi.org/10.1016/j.caeai.2024.100214

Lai, C. Y., Cheung, K. Y., Chan, C. S., & Law, K. K. (2024). Integrating the adapted UTAUT model with moral obligation, trust and perceived risk to predict ChatGPT adoption for assessment support: A survey with students. Computers and Education: Artificial Intelligence, 6, Article 100246. https://doi.org/10.1016/j.caeai.2024.100246

Laupichler, M. C., Aster, A., & Raupach, T. (2023). Delphi study for the development and preliminary validation of an item set for the assessment of non-experts’ AI literacy. Computers and Education: Artificial Intelligence, 4, Article 100126. https://doi.org/10.1016/j.caeai.2023.100126

Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “Scale for the assessment of non-experts’ AI literacy” - An exploratory factor analysis. Computers in Human Behavior Reports, 12, Article 100338. https://doi.org/10.1016/j.chbr.2023.100338

Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical students’ AI literacy and attitudes towards AI: A cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24, Article 401. https://doi.org/10.1186/s12909-024-05400-7

Li, H. (2023). Rethinking human excellence in the AI age: The relationship between intellectual humility and attitudes toward ChatGPT. Personality and Individual Differences, 215, Article 112401. https://doi.org/10.1016/j.paid.2023.112401

Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, Article 102362. https://doi.org/10.1016/j.techsoc.2023.102362

Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 1(2), Article 100022. https://doi.org/10.1016/j.chbah.2023.100022

Mustofa, R. H., Kuncoro, T. G., Atmono, D., Hermawan, H. D., & Sukirman. (2025). Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students. Computers and Education: Artificial Intelligence, 8, Article 100379. https://doi.org/10.1016/j.caeai.2025.100379

Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914-931. https://doi.org/10.1111/bjet.13232

Nazaretsky, T., Mejia-Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025). The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions. Computers and Education: Artificial Intelligence, 8, Article 100368. https://doi.org/10.1016/j.caeai.2025.100368

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241. https://doi.org/10.1007/s10639-022-11316-w

Polyportis, A., & Pahos, N. (2025). Understanding students’ adoption of the ChatGPT chatbot in higher education: The role of anthropomorphism, trust, design novelty and institutional policy. Behaviour & Information Technology, 44(2), 315-336. https://doi.org/10.1080/0144929X.2024.2317364

Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724-2741. https://doi.org/10.1080/10447318.2022.2085400

Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21, Article 46. https://doi.org/10.1186/s41239-024-00478-x

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 15. https://doi.org/10.1186/s41239-024-00448-3

Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025). In GenAI we trust: An investigation of university students’ reliance on and resistance to generative AI in language learning. International Journal of Educational Technology in Higher Education, 22, Article 59. https://doi.org/10.1186/s41239-025-00547-9

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7

Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2025). Could AI ethical anxiety, perceived ethical risks and ethical awareness about AI influence university students’ use of generative AI products? An ethical perspective. International Journal of Human–Computer Interaction, 41(1), 742-764. https://doi.org/10.1080/10447318.2024.2323277