The Use of Gemini AI as a Learning Tool for Poster Creation in the Topic of Technological and Scientific Development for Grade XII Students at SMA Al-Hikmah Surabaya

Authors

  • Muhammad Yazid Al Busthomi Universitas Sebelas Maret
  • Ajitama Putra Nugraha Universitas Negeri Surabaya
  • Muhibbul Anwar Universitas Negeri Surabaya

DOI:

https://doi.org/10.59944/postaxial.v4i3.1595

Abstract

This study analyzes how to Grade XII students used Gemini AI to create posters on the development of science and technology. A descriptive qualitative design was employed with 25 students at Al-Hikmah Senior High School, Surabaya. The data comprised 25 prompts submitted to Gemini and students’ responses to ten open-ended questions after the activity. The data were examined through reflexive thematic analysis, complemented by a descriptive reading of prompt length and components. The findings show that students used Gemini as an integrated production environment to locate information, formulate titles and copy, generate visual ideas, and revise designs. Their practices nevertheless formed a spectrum ranging from near-complete delegation to collaboration in which students acted as directors and editors. All students reported encountering limitations in AI outputs, particularly inaccuracies, overly general answers, visual mismatches, and typographical errors. They verified outputs through books, teacher-provided materials, articles, and websites, although the depth of verification varied. This study contributes the concept of dual learning: students learned the history of technological development while developing AI literacy through prompting, curation, verification, and creative decision-making. Gemini was most educationally productive when positioned as a dialogic partner subject to human judgment rather than as a substitute for student thinking.

References

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7

Ahmad, S. Z. (2019). Digital posters to engage EFL students and develop their reading comprehension. Journal of Education and Learning, 8(4), 169–184. https://doi.org/10.5539/jel.v8n4p169

Biagini, G. (2025). Towards an AI-literate future: A systematic literature review exploring education, ethics, and applications. International Journal of Artificial Intelligence in Education, 35, 2616–2666. https://doi.org/10.1007/s40593-025-00466-w

Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., . . . Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Braun, V., & Clarke, V. (2022). Conceptual and design thinking for thematic analysis. Qualitative Psychology, 9(1), 3–26. https://doi.org/10.1037/qup0000196

Breakstone, J., Smith, M., Wineburg, S., Rapaport, A., Carle, J., Garland, M., . . . Saavedra, A. (2021). Students’ civic online reasoning: A national portrait. Educational Researcher, 50(8), 505–515. https://doi.org/10.3102/0013189X211017495

Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56(3), 1391–1412. https://doi.org/10.1007/s11135-021-01182-y

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(1), 43. https://doi.org/10.1186/s41239-023-00411-8

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

Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846

Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 292, 114523. https://doi.org/10.1016/j.socscimed.2021.114523

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., . . . Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., . . . Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730

Jiang, L. (2018). Digital multimodal composing and investment change in learners’ writing in English as a foreign language. Journal of Second Language Writing, 40, 60–72. https://doi.org/10.1016/j.jslw.2018.03.002

Jiang, L., Yu, S., & Zhao, Y. (2022). Incorporating digital multimodal composing through collaborative action research: Challenges and coping strategies. Technology, Pedagogy and Education, 31(1), 45–61. https://doi.org/10.1080/1475939X.2021.1978534

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., . . . Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854. https://doi.org/10.1080/0142159X.2020.1755030

Lee, S. J., & Kwon, K. (2024). A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 6, 100211. https://doi.org/10.1016/j.caeai.2024.100211

Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790

McGrew, S. (2020). Learning to evaluate: An intervention in civic online reasoning. Computers & Education, 145, 103711. https://doi.org/10.1016/j.compedu.2019.103711

McGrew, S., & Breakstone, J. (2023). Civic online reasoning across the curriculum: Developing and testing the efficacy of digital literacy lessons. AERA Open, 9, 1–14. https://doi.org/10.1177/23328584231176451

McGrew, S., Breakstone, J., Ortega, T., Smith, M., & Wineburg, S. (2018). Can students evaluate online sources? Learning from assessments of civic online reasoning. Theory & Research in Social Education, 46(2), 165–193. https://doi.org/10.1080/00933104.2017.1416320

Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22, 1–18. https://doi.org/10.1177/16094069231205789

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, 1–13. https://doi.org/10.1177/1609406919899220

Popa, N. (2022). Operationalizing historical consciousness: A review and synthesis of the literature on meaning making in historical learning. Review of Educational Research, 92(2), 171–208. https://doi.org/10.3102/00346543211052333

Roberts, K., Dowell, A., & Nie, J.-B. (2019). Attempting rigour and replicability in thematic analysis of qualitative research data: A case study of codebook development. BMC Medical Research Methodology, 19(1), 66. https://doi.org/10.1186/s12874-019-0707-y

Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423

Sumantri, P., Nababan, S. A., Sumanti, S. T., Tanjung, Y., Mulyani, F. F., & Jali, J. M. (2024). Effectiveness of use of interactive e-poster history learning media in increasing historical awareness. Jurnal Ilmu Pendidikan, 30(1), 41–48. https://doi.org/10.17977/um048v30i1p40-47

Tang, K.-S., Cooper, G., Rappa, N., Cooper, M., Sims, C., & Nonis, K. (2024). A dialogic approach to transform teaching, learning & assessment with generative AI in secondary education: A proof of concept. Pedagogies: An International Journal, 19(3), 493–503. https://doi.org/10.1080/1554480X.2024.2379774

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x

Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and justifying sample size sufficiency in interview-based studies: Systematic analysis of qualitative health research over a 15-year period. BMC Medical Research Methodology, 18(1), 148. https://doi.org/10.1186/s12874-018-0594-7

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(1), 15. https://doi.org/10.1186/s41239-024-00448-3

Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768

Wisanti, W., Indah, N. K., & Putri, E. K. (2024). Scientific digital poster assignments: Strengthen concepts, train creativity, and communication skills. International Journal of Evaluation and Research in Education, 13(2), 1035–1044. https://doi.org/10.11591/ijere.v13i2.25909

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., . . . 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

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Zhang, M., Akoto, M., & Li, M. (2023). Digital multimodal composing in post-secondary L2 settings: A review of the empirical landscape. Computer Assisted Language Learning, 36(4), 694–721. https://doi.org/10.1080/09588221.2021.1942068

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Published

2026-09-04