Analisis Faktor Penerimaan dan Pengembangan Mockup Antarmuka Dosen pada Sistem Rekomendasi Pembelajaran Vokasi Berbasis AI

Intan Wulan Sari, Hilma Erliana, Ade Dwinta, Very Ilhadi, Desy Sary Ayunda

Abstract


This study identifies the factors influencing vocational lecturers’ acceptance of an Artificial Intelligence (AI)-based recommendation system as a basis for designing a lecturer interface mockup for a two-way recommendation system implementing the concept of Human–AI Collaboration. The study employed a quantitative approach using a survey method based on an adapted version of the Technology Acceptance Model (TAM), incorporating Human–AI Collaboration and Technology Readiness as additional variables in the model. Data were collected through Likert-scale questionnaires administered to 100 vocational lecturers from various institutions across Indonesia using purposive sampling, with the criteria of having at least one year of teaching experience and prior experience in using digital learning technologies. Multiple linear regression was used to analyze the data. The results indicate that only three of the five variables significantly influence system acceptance: Expected Benefits (B = 0.288; p = 0.000), Lecturer Feature Mapping (B = 0.113; p = 0.007), and Technology Readiness (B = 0.462; p = 0.000). Technology Readiness has the strongest influence, whereas Lecturer Needs and Human–AI Collaboration do not have statistically significant effects on system acceptance. These findings provide a foundation for designing a lecturer interface mockup that incorporates priority features, including a real-time monitoring dashboard, student performance data visualization, intervention controls, two-way communication, and integration with a Learning Management System (LMS). This study is expected to serve as a foundation for developing an adaptive recommendation system prototype for vocational education in Indonesia.

Keywords


Adaptive Learning; Human-AI Collaboration; Recommendation System

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References


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DOI: https://doi.org/10.35308/jopt.v12i2.15828

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