Enhancing EFL Students’ Poetry Comprehension through AI-Assisted Learning: A Mixed-Methods Study Using HyperWrite - Sukini, Sukini; Setiyadi, Dwi Bambang Putut; Herawati, Nanik (Eralingua: Jurnal Pendidikan Bahasa Asing dan Sastra, 2026)
The conventional, text-based literary learning model that does not utilize digital technology has an impact on students' lack of ability to understand the meaning of poetry. It is urgent to analyse technology-based learning innovations that can improve student understanding, motivation, and engagement. Therefore, the purpose of this study is to evaluate the effectiveness of HyperWrite Technology as a teaching aid in improving students' ability to understand the meaning of poetry in Indonesian language learning. The research approach uses a mixed method sequential explanation design. The number of respondents in this study was 170 students, with a sampling technique using purposive sampling. Data collection techniques in this study used pre-test and post-test, observation interviews, and documentation. Data analysis techniques used statistics for paired sample t-tests, effect sizes, correlations, and regressions, while qualitative data were analysed thematically. The results of the study showed a significant increase in poetry comprehension ability, with an average of 59.40 (pre-test) to 82.15 (post-test), t value = 18.462, p < 0.05 and an effect size of 0.81. These findings indicate that HyperWrite's automatic feature analysis provides a dominant contribution in improving students' understanding of the meaning of poetry. In conclusion, HyperWrite technology is effective in enhancing literary understanding, particularly in interpreting poetry interactively and constructively in the future. The global implication is that HyperWrite-based literature learning makes it easier for students to interpret the meaning of poetry