Sep 2026· EDUTECH Jurnal Inovasi Pendidikan Berbantuan Teknologi· 0 citations
Abstract
Artificial intelligence (AI) technology is mostly used in the education sector to help students complete academic tasks, but some teachers and future teachers are still unsure how to integrate AI into their lessons, while AI integration has become a necessity to address digital transformation challenges. The aim of this research is to analyze the influence of institutional support and AI literacy on students' intention to apply AI-based learning, with technology acceptance as a mediating variable, based on Self-Determination Theory. This study uses a quantitative method and distributes questionnaires with a 1-4 Likert scale to 405 students from the Faculty of Teacher Training and Education at Sebelas Maret University. After the questionnaires were collected, the data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) provided by SmartPLS 4.. The research results show that institutional support, AI literacy, and technology acceptance have a positive and significant effect on the intention to apply AI-based learning, with an R Square value of 0.584, which means these three variables can explain 58.4% of students' intentions. This study provides empirical contributions by expanding the application of Self-Determination Theory to students and empowers universities to create a conducive learning environment, provide access to AI technology, and prepare training to enhance students' understanding and intention to use AI-based learning.
ABSTRAK
Teknologi artificial intelligence (AI) sebagian besar digunakan di sektor pendidikan untuk membantu mahasiswa menyelesaikan tugas akademik, beberapa guru dan mahasiswa calon guru masih belum yakin bagaimana cara mengintegrasikan AI ke dalam proses pelajaran mereka, sedangkan integrasi AI menjadi kebutuhan untuk mengatasi tantangan trasnformasi digital. Tujuan penelitian ini adalah menganalisis pengaruh dukungan institusi dan literasi AI terhadap intensi mahasiswa untuk menerapkan pembelajaran berbasis AI dengan penerimaan teknologi sebagai variabel mediasi berdasarkan Self-Determination Theory. Penelitian ini menerapkan metode kuantitatif dan penyebaran angket dengan skala likert 1-4 terhadap 405 mahasiswa pada Fakultas Keguruan dan Ilmu Pendidikan Universitas Sebelas Maret. Setelah kuesioner dikumpulkan, analisis data dilakukan menggunakan Partial Least Squares-Structural Equation Modeling (PLS-SEM) yang disediakan oleh SmartPLS 4. Hasil penelitian menunjukkan bahwa dukungan institusi, literasi AI, dan penerimaan teknologi berpengaruh positif dan signifikan terhadap intensi menerapkan pembelajaran berbasis AI dengan nilai R Square 0,584, yang artinya ketiga variabel mampu menjelaskan 58,4% intensi mahasiswa. Penelitian ini memberikan kontribusi empiris dengan memperluas penerapan Self-Determination Theory pada mahasiswa serta memberikan pemberdayaan bagi universitas dalam menciptakan lingkungan belajar yang kondusif, menyediakan akses ke teknologi AI, dan menyiapkan pelatihan untuk meningkatkan pemahaman dan intensi mahasiswa dalam menggunakan pembelajaran berbasis AI.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.