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All Journal Sistemasi: Jurnal Sistem Informasi EduImpact: Jurnal Pengabdian dan Inovasi Masyarakat
Mei Ratnasari
STMIK Kalirejo
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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INTEGRASI PROMPT AI DALAM PEMBELAJARAN BAHASA INGGRIS UNTUK MENINGKATKAN LITERASI DIGITAL SISWA SMP Widi Andewi; Diyah Trinovita; Amalyanda Azhari; Mei Ratnasari
EduImpact: Jurnal Pengabdian dan Inovasi Masyarakat Vol. 3 No. 1 (2026): EduImpact: Jurnal Pengabdian dan Inovasi Masyarakat
Publisher : Cipta Pustaka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63324/eipm.3v.1i.172

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengintegrasikan prompt Artificial Intelligence (AI) dalam pembelajaran Bahasa Inggris guna meningkatkan literasi digital siswa SMP. Kegiatan ini dilatarbelakangi oleh pentingnya literasi digital di era teknologi serta masih terbatasnya keterampilan siswa dalam aspek writing dan speaking. Metode yang digunakan adalah pendekatan partisipatif berbasis praktik (learning by doing) melalui empat tahap, yaitu persiapan, pelaksanaan, evaluasi, dan tindak lanjut. Kegiatan dilaksanakan pada 30 siswa kelas VII SMP Al-Hikam Sendang Agung. Hasil kegiatan menunjukkan bahwa penggunaan prompt AI memberikan dampak positif terhadap kemampuan siswa dalam menyusun teks dan dialog Bahasa Inggris, memperkaya kosakata, serta meningkatkan kepercayaan diri dalam berbicara. Selain itu, literasi digital siswa juga mulai berkembang, terutama dalam kemampuan menyusun instruksi, berpikir kritis, dan mengevaluasi informasi yang dihasilkan oleh AI. Lebih dari 80% siswa memberikan respons positif terhadap pembelajaran berbasis AI karena dinilai lebih menarik dan interaktif. Namun, kegiatan ini masih memiliki kendala, seperti keterbatasan siswa dalam menyusun prompt yang lebih kompleks dan keterbatasan akses teknologi. Dengan demikian, integrasi prompt AI menunjukkan potensi efektif sebagai strategi pembelajaran inovatif untuk mendukung peningkatan keterampilan Bahasa Inggris dan literasi digital siswa SMP.
Structured Generative AI Co-Tutoring for Learning Outcomes and Motivation in Information Systems Mei Ratnasari; Amalyanda Azhari; Diyah Trinovita; Widi Andewi; Eva Nurchurifiani
SISTEMASI Vol 15, No 9 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i9.6875

Abstract

Generative AI (GenAI) offers interactive explanations, feedback, and personalized support in higher education, yet its educational value depends on how such support is structured. Evidence remains limited regarding whether structured GenAI co-tutoring can simultaneously improve independent learning outcomes and learning motivation in concept-oriented Information Systems courses. This study examined the effectiveness of structured GenAI co-tutoring compared with conventional instruction in an undergraduate Information Systems Concepts course in Indonesia. A quasi-experimental nonequivalent control group pretest–posttest design involved 60 students from two intact classes, with 30 students in each condition. Learning outcomes were measured using parallel forms of the Information Systems Concepts Achievement Test, while learning motivation was assessed using the EVC Light scale. Data collection comprised pre-intervention assessment, the instructional intervention, and post-intervention assessment; achievement tests were completed without GenAI access to capture independently demonstrated learning. ANCOVA was used to compare posttest outcomes while controlling for corresponding pretest scores. The experimental group achieved a significantly higher adjusted ISCAT-B score than the control group, with an adjusted mean difference of 7.89 points, F(1, 57) = 27.65, p < .001, partial η² = .327. Learning motivation was also significantly higher in the experimental group, with an adjusted EVC Index difference of 0.053, F(1, 57) = 13.53, p < .001, partial η² = .192. The study extends evidence on structured GenAI support by examining independently assessed conceptual learning alongside expectancy–value–cost motivation in Information Systems education. In practice, lecturers can adopt a sequence of independent attempts, guided AI feedback, verification, and revision, followed by assessment without AI assistance.
Co-Authors Amalyanda Azhari Diyah Trinovita Eva Nurchurifiani Widi Andewi