BEYOND THE SCREEN: HIGH SCHOOL STUDENTS’ PERCEPTIONS OF CANVA AI-POWERED LEARNING MEDIA IN HISTORY EDUCATION

Authors

  • Novri Sulyintho Benu Department of Educational Technology, Faculty of Education, Universitas Negeri Malang
  • I Nyoman S. Degeng Department of Educational Technology, Faculty of Education, Universitas Negeri Malang
  • Fikri Aulia Department of Educational Technology, Faculty of Education, Universitas Negeri Malang
  • Made Duananda Kartika Degeng Department of Educational Technology, Faculty of Education, Universitas Negeri Malang

DOI:

https://doi.org/10.61677/satmata.v4i1.1045

Keywords:

Canva AI, history learning, student perceptions, secondary school, Technology Acceptance Model.

Abstract

The advancement of artificial intelligence (AI) in education has created new opportunities for designing and delivering learning materials, particularly in history that a subject traditionally dominated by text and chronological narratives. This study aims to explore secondary school students' perceptions regarding the use of Canva AI-based learning media in history lessons, specifically focusing on the local history topic of the Sonba'i III Rebellion. Employing a qualitative descriptive approach, the study involved 14 students selected through purposive sampling. Data were collected via semi-structured interviews following a lesson that utilized Canva AI media, which incorporated visual content, infographics, and interactive practice exercises. The findings indicate that students perceived Canva AI as user-friendly, visually engaging, and beneficial for comprehending historical material through visualization and information summarization. The quiz feature, offering immediate feedback, was the most appreciated element, as it enabled students to independently assess their understanding. However, students also identified several limitations, including reliance on internet connectivity, text size issues, potential inaccuracies in historical information, and the risk of over-reliance on AI-generated answers. The study's main conclusion is that high school students perceive Canva AI as usable, visually engaging, and pedagogically helpful for history learning, particularly through its quiz feedback and multimodal presentation, while also recognizing limitations related to connectivity, readability, content accuracy, and AI dependency. Broader pedagogical implications suggest that AI-assisted history education requires teachers to act as facilitators, validators, and guides for critical thinking, and that media development should prioritize collaboration, personalization, source verification, and accessibility.

References

Adi, N. H., Nelmira, W., Novrita, S. Z., Gusnita, W., Lubis, A. L., & Riyanda, A. R. (2025). Augmented reality as an educational tool: A meta-analysis of its impact on student performance. TEM Journal, 14(3), 2271-2284. https://doi.org/10.18421/TEM143-32

Ahmad, M., & Wilkins, S. (2025). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity, 59(2), 1461–1479. https://doi.org/10.1007/s11135-024-02022-5

Babu, C. S., William, M. P., & Kowsika, S. (2025). Generative AI tools and education: Enabling collaborative intelligence through ChatGPT, Canva, InVideo AI, and InShot. In Humans and Generative AI Tools for Collaborative Intelligence (pp. 441–460). IGI Global Scientific Publishing. https://www.igi-global.com/chapter/generative-ai-tools-and-education/382780

Berg, M. (2021). Combining time and space: An organizing concept for narratives in history teaching. Acta Didactica Norden, 15(1), 1–23. https://dx.doi.org/10.5617/adno.7860

Catubig, M. J. A., Kilat, R. V. B., Laurito, M. E., Patoc, T. M. T., & Valle, L. C. (2024). Visual Literacy in the Lived Experiences of BSED Students in Utilizing Canva. Journal of Educational and Social Research, 14(4), 117-131. https://doi.org/10.36941/jesr-2024-0090

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008

Farkish, A., Bosaghzadeh, A., Amiri, S. H., & Ebrahimpour, R. (2023). Evaluating the Effects of Educational Multimedia Design Principles on Cognitive Load Using EEG Signal Analysis. Education and Information Technologies, 28(3), 2827–2843. https://doi.org/10.1007/s10639-022-11283-2

Hamid, F. A., & Ahmad, N. J. (2026). Co-Teaching with AI: Shift Pedagogy, Opportunities, and Challenges in Mathematics Education. Asia Pacific Journal of Educators and Education, 41(1), 65–84. https://doi.org/10.21315/apjee2026.41.1.4

Hayati, U., Johar, R., & Marwan. (2020). The integration of local context through problem-based learning (PBL) to improve junior high school students problem-solving skills. Journal of Physics: Conference Series, 1460(1), 1-9. https://doi.org/10.1088/1742-6596/1460/1/012036

Huda, A., Sari, L. M., Effendi, H., Giatman, M., Firdaus, -, & Sukmawati, M. (2025). Canva-based Animation Comic Video Media in Informatics Learning at SMP Negeri 14 Padang. JOIV : International Journal on Informatics Visualization, 9(1), 120–127. https://doi.org/10.62527/joiv.9.1.3177

Huijgen, T., Van De Grift, W., Van Boxtel, C., & Holthuis, P. (2018). Promoting historical contextualization: The development and testing of a pedagogy. Journal of Curriculum Studies, 50(3), 410–434. https://doi.org/10.1080/00220272.2018.1435724

Ibarra-Sáiz, M. S., González-Elorza, A., & Rodríguez-Gómez, G. (2023). Methodological contributions to the use of semi-structured interview in educational research based on a multiple case study. Rev. Investig. Educ, 41(2), 501–522. https://doi.org/10.6018/rie.546401

Kallio, H., Pietilä, A., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi‐structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031

Lee, C.-F., Lai, T.-H., Eunike, I.-J., & Tran, K.-A. (2026). Trust, Usefulness, and Satisfaction in ChatGPT Adoption: An ELM–TAM Study among University Students. International Journal of Information and Education Technology, 16(3), 625–639. https://doi.org/10.18178/ijiet.2026.16.3.2535

Li, J., & Xue, E. (2023). Dynamic Interaction between Student Learning Behaviour and Learning Environment: Meta-Analysis of Student Engagement and Its Influencing Factors. Behavioral Sciences, 13(1), 1-15. https://doi.org/10.3390/bs13010059

McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, 1-11. https://doi.org/10.1016/j.chbah.2025.100121

Midtsundstad, J. H., & Langfeldt, G. (2020). The School Programme: A Key Link between Contextual Influence and School Development. Scandinavian Journal of Educational Research, 64(1), 87–97. https://doi.org/10.1080/00313831.2018.1495261

Miguel-Revilla, D., Calle-Carracedo, M., & Sánchez-Agustí, M. (2021). Fostering engagement and historical understanding with a digital learning environment in secondary education. E-Learning and Digital Media, 18(4), 344–360. https://doi.org/10.1177/2042753020957452

Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis. sage. https://books.google.com/books?hl=id&lr=&id=3CNrUbTu6CsC&oi=fnd&pg=PR1&dq=qualitative+data+analysis+miles&ots=Lj81kjVM5e&sig=MlubC6QTZa7h0Yw5Dr7Co6FVHXI

Ngoc, L. P. T., & Nguyen, L. T. (2025). Students’ perspectives on Canva’s level of utilization and impact in academic learning: A case study of a private university in Vietnam. 2025 10th International STEM Education Conference (iSTEM-Ed), 1–6. https://ieeexplore.ieee.org/abstract/document/11129264/

Rabaglia, C. D., & Schneider, B. A. (2016). Age-related inhibitory deficit, or lack of familiarity benefit? Evidence from letter identification among visual distractors. Attention, Perception, & Psychophysics, 78(2), 542–565. https://doi.org/10.3758/s13414-015-1009-z

Ramos, S. M. (2024). Canva como herramienta para promover el aprendizaje significativo en la enseñanza del inglés como lengua extranjera. European Public & Social Innovation Review, 9, 1–19. https://doi.org/10.31637/epsir-2024-869

Shirley, P. Y. C. (2025). Evaluating the Impact of Chatgpt on Academic Performance and Technology Acceptance: A Quasi-Experimental Study in Malaysian Chinese Independent School History Education. International Journal of Academic Research in Business and Social Sciences, 15(8), 1060–1081. https://dx.doi.org/10.6007/IJARBSS/v15-i8/26115

Singh, S., & Singh, V. (2026). Impact of artificial intelligence on academic engagement and performance of social sciences college students in Delhi NCR. Discover Education, 5(1), 319-340. https://doi.org/10.1007/s44217-026-01297-0

Skau, S., Karlsson, J., Åsberg Johnels, J., & Helenius, O. (2026). Visualizations during arithmetic tasks hamper performance and increase cognitive load in 9-year-olds: An fNIRS study. Frontiers in Psychology, 17, 1-17. https://doi.org/10.3389/fpsyg.2026.1722948

Slimi, Z., Benayoune, A., & Alemu, A. E. (2025). Students’ perceptions of artificial intelligence integration in higher education. European Journal of Educational Research, 14(2), 471–484. https://doi.org/doi.org/10.12973/eu-jer.14.2.471

Song, F., & Rabu, S. N. A. (2026). Beyond Toolness: A Qualitative Content Analysis of Generative AI Platforms for Design Creation. In Cultural Considerations for Effective Digital Transformation in Education (1–26). IGI Global Scientific Publishing. https://www.igi-global.com/chapter/beyond-toolness/392585

Suiçmez, İ., Altinay, F., Dağlı, G., Zeng, H., Shadiev, R., İşlek, D., Danju, İ., & Altinay, Z. (2025). Artificial intelligence application for museum to experiential transformation of cultural heritage and learning. Smart Learning Environments, 12(1), 1-19. https://doi.org/10.1186/s40561-025-00404-2

Sunarso, A., & Herdianto, F. (2024). Canva-assisted Modelling the Way strategy and students’ Islamic education learning outcomes. Jurnal Cakrawala Pendidikan, 43(2), 430–438. https://doi.org/10.21831/cp.v43i2.60691

SzHuts, Z., Lengyelné Molnár, T., Racskó, R., Vaughan, G., Ceglédi, S., & Dominek, D. L. (2025). Examining the flow dynamics of artificial intelligence in real-time classroom applications. Computers, 14(7), 1-17. https://doi.org/10.3390/computers14070275

Villamin, P., Lopez, V., Thapa, D. K., & Cleary, M. (2025). A Worked Example of Qualitative Descriptive Design: A Step‐by‐Step Guide for Novice and Early Career Researchers. Journal of Advanced Nursing, 81(8), 5181–5195. https://doi.org/10.1111/jan.16481

Xiao, F., & Amiri, M. (2026). Extending the technology acceptance model with ethics, trust, and subjective norms: A PLS-SEM analysis of students’ AI adoption in higher education. AIP Advances, 16(5), 1-14. https://doi.org/10.1063/5.0332804

Published

2026-09-30

How to Cite

Novri Sulyintho Benu, I Nyoman S. Degeng, Fikri Aulia, & Made Duananda Kartika Degeng. (2026). BEYOND THE SCREEN: HIGH SCHOOL STUDENTS’ PERCEPTIONS OF CANVA AI-POWERED LEARNING MEDIA IN HISTORY EDUCATION. Satmata: Journal of Historical Education Studies, 4(1), 78 ~ 92. https://doi.org/10.61677/satmata.v4i1.1045