Adaptasi Pembelajaran Jurnalistik Multimedia Berbasis AI pada Mahasiswa Pendidikan Tinggi Vokasi - Sisca T. Gurning; Suradi Suradi; Safudiningsih Safrudiningsih (UNIVED PRESS, Universitas Dehasen Bengkulu, 2026)
Artificial Intelligence (AI) technology is developing rapidly and is changing multimedia journalism practice substantially; however, AI technology in higher vocational education curricula encounters different challenges. The purpose of this study is to investigate the student experience through utilising AI in multimedia journalism at an a vocational college. Applying the qualitative approach and adopting a case study methodology, data were generated via open questionnaires with focus group questions, besides direct observations over 48 students that produced news packages from AI – like applications: ChatGPT, Humva, Elevenlabs. Thematic analysis identified five main themes: technical constraints, ethical dilemmas, teamwork patterns, efficiency benefits, and work coordination. The research findings show that although AI improves research and visual production efficiency, students experience significant obstacles in the audio-visual synchronization stage and limited application accessibility. Based on the Cognitive Theory of Multimedia Learning perspective, these obstacles create extraneous cognitive load that diverts students' focus from the substance of journalism to solving technical problems. In addition, a collective ethical awareness emerges, whereby students continue to prioritize manual verification to maintain the integrity of the news. This study concludes that the adaptation of AI in vocational education requires consistent infrastructure support so that this technology can boost productivity without adding to students' mental load.