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Artificial Intelligence as a Pedagogical Tool in Mathematics Courses: A Descriptive Study of Prospective Elementary School Teachers Antara, I Gede Wahyu Suwela; Diputra, Komang Sujendra; Wirawan, Kadek Adrian Surya Indra
Paedagogi: Jurnal Kajian Ilmu Pendidikan (e-journal) Vol. 11 No. 2 (2025): DECEMBER 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/paedagogi.v11i2.69279

Abstract

The rapid development of artificial intelligence has created new opportunities for enhancing mathematics learning, including in the preparation of prospective elementary school teachers. This study describes the use of artificial intelligence as a pedagogical tool in mathematics courses at the Primary School Teacher Education Program of Universitas Pendidikan Ganesha during the even semester of the 2024/2025 academic year. A descriptive quantitative approach was applied to 118 students enrolled in Elementary Algebra and Arithmetic, Elementary Numeracy, Measurement and Geometry, and Elementary Mathematics Teaching. Data were collected through an online questionnaire covering demographic characteristics, frequency of use, perceived usefulness, perceived ease of use, learning engagement, ethical awareness, and attitudes toward artificial intelligence. The results show high mean scores for perceived usefulness, perceived ease of use, learning engagement, and positive attitudes, while frequency of use and ethical awareness remain at moderate levels. ChatGPT emerged as the most frequently used platform, followed by Photomath, Perplexity, Gemini, QuillBot, Canva AI, and GeoGebra AI features, primarily for generating explanations, checking solutions, and designing instructional materials. These findings indicate that prospective elementary school teachers have developed favorable perceptions and a willingness to integrate artificial intelligence into mathematics learning, yet the moderate levels of usage frequency and ethical awareness highlight the need for structured training and explicit guidance to promote consistent and responsible application of artificial intelligence in mathematics education. Strengthening ethical understanding and providing systematic practice are essential to ensure that future teachers can leverage artificial intelligence effectively to improve mathematics learning in elementary schools.
Innovative Deep Learning–Driven Indonesian Language Instruction: Development and Effectiveness in Improving Elementary Reading Literacy Ni Ketut Desia Tristiantari; Dani Gunawan; Decenni Amelia; Ni Wayan Monik Rismadewi; Komang Sujendra Diputra; Gusti Ayu Putu Sukma Trisna
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 12 No. 2 (2026): June (IN PRESS)
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v12i2.20616

Abstract

This study aims to develop an innovative Indonesian language learning design based on a deep learning approach and to evaluate its effectiveness in improving elementary school students' reading literacy. The study employed a Research and Development (R&D) method using the ADDIE model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. The research participants included two content experts, two instructional design experts, and 65 elementary school students. Data were collected through validation questionnaires to assess product feasibility and multiple-choice tests to measure students’ reading literacy skills. The data were analyzed using descriptive quantitative techniques and the normalized gain (N-Gain) test. The results revealed that: (1) students’ initial reading literacy achievement, as measured by the pre-test, was categorized as moderate, with a mean score of 63.61; (2) the developed learning design was rated as highly feasible by expert validators, achieving an average score of 4.63 out of 5.00; (3) students’ post-test performance improved substantially, reaching a mean score of 84.54 and falling within the very high category; and (4) the N-Gain value was 0.58, indicating a moderate level of improvement. These findings indicate that the deep learning-based Indonesian language learning design is effective in enhancing elementary school students’ reading literacy. This study highlights the potential of integrating cognitive and affective dimensions through a deep learning approach to foster a more meaningful learning environment. Such an approach enables students not only to comprehend texts but also to critically analyze, evaluate, and reflect on information. Therefore, the proposed learning design may serve as a valuable reference for educators seeking to transform literacy instruction into a more student-centered and meaningful learning experience at the elementary school level.