OPTIMALISASI PEMBELAJARAN TEKNOLOGI PENDIDIKAN DENGAN BANTUAN GPT AI

Authors

  • MUH. HUSEIN BAYSHA Universitas Pendidikan Mandalika, Mataram, Indonesia
  • ENDAH RESNANDARI PUJI ASTUTI Universitas Pendidikan Mandalika, Mataram, Indonesia

DOI:

https://doi.org/10.51878/edutech.v4i3.3143

Keywords:

GPT, AI, Teknologi Pendidikan

Abstract

This research aims to evaluate student perceptions of the use of Generative Pre-trained Transformer (GPT) AI in learning educational technology in the Educational Technology Study Program, Mandalika Education University. The research method used is a quantitative survey with a descriptive approach. The research participants consisted of 118 students who were selected as respondents. The research instrument is a closed questionnaire with a 5-point Likert scale designed to measure various aspects of GPT AI use, including understanding of the material, learning motivation, learning feedback, accessibility, interaction with learning material, relevance to learning objectives, and quality of education received. The data collected was analyzed using descriptive statistics to provide a general overview, normality test to test data distribution, and Pearson correlation analysis to explore the relationship between variables. The research results show that students generally have a positive perception of the use of GPT AI in learning. The average rating for various aspects of using GPT AI is above the middle value (3.0), indicating that students feel helped in understanding the course material, are more motivated to learn, and receive useful feedback. However, the normality test showed that the data were not normally distributed, indicating significant variation in student perceptions. Pearson correlation analysis identified a significant relationship between learning motivation and interaction with learning materials (r = 0.104), as well as between recommendations for using GPT AI and increased interaction with learning materials (r = 0.166). This research concludes that GPT AI has great potential to improve students' understanding of material and learning motivation, but its implementation requires adequate technical and pedagogical support to maximize its benefits. These findings provide important insights for the development of more effective AI implementation strategies in educational technology and underscore the need for approaches tailored to individual student needs.

ABSTRAK
Penelitian ini bertujuan untuk mengevaluasi persepsi mahasiswa terhadap penggunaan Generative Pre-trained Transformer (GPT) AI dalam pembelajaran teknologi pendidikan di Program Studi Teknologi Pendidikan, Universitas Pendidikan Mandalika. Metode penelitian yang digunakan adalah survei kuantitatif dengan pendekatan deskriptif. Partisipan penelitian terdiri dari 118 mahasiswa yang dipilih sebagai responden. Instrumen penelitian berupa kuesioner tertutup dengan skala Likert 5 poin yang dirancang untuk mengukur berbagai aspek penggunaan GPT AI, termasuk pemahaman materi, motivasi belajar, umpan balik pembelajaran, aksesibilitas, interaksi dengan materi belajar, relevansi dengan tujuan pembelajaran, dan kualitas pendidikan yang diterima. Data yang dikumpulkan dianalisis menggunakan statistik deskriptif untuk memberikan gambaran umum, uji normalitas untuk menguji distribusi data, serta analisis korelasi Pearson untuk mengeksplorasi hubungan antar variabel. Hasil penelitian menunjukkan bahwa mahasiswa secara umum memiliki persepsi positif terhadap penggunaan GPT AI dalam pembelajaran. Rata-rata penilaian terhadap berbagai aspek penggunaan GPT AI berada di atas nilai tengah (3,0), menunjukkan bahwa mahasiswa merasa terbantu dalam memahami materi kursus, lebih termotivasi untuk belajar, dan mendapatkan umpan balik yang bermanfaat. Meskipun demikian, uji normalitas menunjukkan bahwa data tidak terdistribusi normal, mengindikasikan adanya variasi signifikan dalam persepsi mahasiswa. Analisis korelasi Pearson mengidentifikasi hubungan signifikan antara motivasi belajar dan interaksi dengan materi belajar (r = 0,104), serta antara rekomendasi penggunaan GPT AI dan peningkatan interaksi dengan materi belajar (r = 0,166). Penelitian ini menyimpulkan bahwa GPT AI memiliki potensi besar untuk meningkatkan pemahaman materi dan motivasi belajar mahasiswa, namun implementasinya memerlukan dukungan teknis dan pedagogis yang memadai untuk memaksimalkan manfaatnya. Temuan ini memberikan wawasan penting bagi pengembangan strategi implementasi AI yang lebih efektif dalam teknologi pendidikan dan menggarisbawahi perlunya pendekatan yang disesuaikan dengan kebutuhan individu mahasiswa.

Downloads

Download data is not yet available.

References

Al-Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A., & Iranmanesh, M. (2024). Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence From PLS-SEM and Fuzzy Sets (fsQCA). IEEE Transactions on Engineering Management, 71, 4985–4999. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2023.3237789

Archibald, M. M., & Clark, A. M. (2023). ChatGTP: What is it and how can nursing and health science education use it? Journal of Advanced Nursing, 79(10), 3648–3651. https://doi.org/10.1111/jan.15643

Chen, B., Zhu, X., & Díaz del Castillo H., F. (2023). Integrating generative AI in knowledge building. Computers and Education: Artificial Intelligence, 5, 100184. https://doi.org/10.1016/j.caeai.2023.100184

Dalka, R. P., Sachmpazidi, D., Henderson, C., & Zwolak, J. P. (2022). Network analysis approach to Likert-style surveys. Physical Review Physics Education Research, 18(2), 020113. https://doi.org/10.1103/PhysRevPhysEducRes.18.020113

Fidan, M., & Gencel, N. (2022). Supporting the Instructional Videos With Chatbot and Peer Feedback Mechanisms in Online Learning: The Effects on Learning Performance and Intrinsic Motivation. Journal of Educational Computing Research, 60(7), 1716–1741. https://doi.org/10.1177/07356331221077901

Flores-Vivar, J.-M., & García-Peñalvo, F.-J. (2023). Reflexiones sobre la ética, potencialidades y retos de la Inteligencia Artificial en el marco de la Educación de Calidad (ODS4). Comunicar: Revista Científica de Comunicación y Educación, 31(74), 37–47. https://doi.org/10.3916/C74-2023-03

Gomede, E., Gaffo, F. H., Briganó, G. U., De Barros, R. M., & Mendes, L. D. S. (2018). Application of Computational Intelligence to Improve Education in Smart Cities. Sensors, 18(1), Article 1. https://doi.org/10.3390/s18010267

Ifenthaler, D., & Schumacher, C. (2023). Reciprocal issues of artificial and human intelligence in education. Journal of Research on Technology in Education, 55(1), 1–6. https://doi.org/10.1080/15391523.2022.2154511

Lai, T., Xie, C., Ruan, M., Wang, Z., Lu, H., & Fu, S. (2023). Influence of artificial intelligence in education on adolescents’ social adaptability: The mediatory role of social support. PLOS ONE, 18(3), e0283170. https://doi.org/10.1371/journal.pone.0283170

Lee, H. (2024). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Sciences Education, 17(5), 926–931. https://doi.org/10.1002/ase.2270

Lin, Y., & Yu, Z. (2023). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education, 21(2), 189–213. https://doi.org/10.1108/ITSE-12-2022-0165

Mohd Rahim, N. I., A. Iahad, N., Yusof, A. F., & A. Al-Sharafi, M. (2022). AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach. Sustainability, 14(19), Article 19. https://doi.org/10.3390/su141912726

Moldt, J.-A., Festl-Wietek, T., Mamlouk, A. M., & Herrmann-Werner, A. (2022). Assessing medical students’ perceived stress levels by comparing a chatbot-based approach to the Perceived Stress Questionnaire (PSQ20) in a mixed-methods study. DIGITAL HEALTH, 8, 20552076221139092. https://doi.org/10.1177/20552076221139092

Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. https://doi.org/10.1186/s41239-022-00372-4

Paek, S., & Kim, N. (2021). Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education. Sustainability, 13(14), Article 14. https://doi.org/10.3390/su13147941

Rad, H. S., Alipour, R., & Jafarpour, A. (2023). Using artificial intelligence to foster students’ writing feedback literacy, engagement, and outcome: A case of Wordtune application. Interactive Learning Environments, 32(1), 1–21. https://doi.org/10.1080/10494820.2023.2208170

Romero-Rodríguez, J.-M., Ramírez-Montoya, M.-S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at University as a Tool for Complex Thinking: Students’ Perceived Usefulness. Journal of New Approaches in Educational Research, 12(2), Article 2. https://doi.org/10.7821/naer.2023.7.1458

Saha, G. C., Kumar, S., Kumar, A., Saha, H., Lakshmi, TR. K., & Bhat, N. (2023). Human-AI Collaboration: Exploring interfaces for interactive Machine Learning. Tuijin Jishu/Journal of Propulsion Technology, 44(2), Article 2. https://doi.org/10.52783/tjjpt.v44.i2.148

Sahu, P. K., Benjamin, L. A., Singh Aswal, G., & Williams-Persad, A. (2024). ChatGPT in research and health professions education: Challenges, opportunities, and future directions. Postgraduate Medical Journal, 100(1179), 50–55. https://doi.org/10.1093/postmj/qgad090

Sanabria-Navarro, J.-R., Silveira-Pérez, Y., Pérez-Bravo, D.-D., & de-Jesús-Cortina-Núñez, M. (2023). Incidences of artificial intelligence in contemporary education. Comunicar: Revista Científica de Comunicación y Educación, 31(77), 97–107. https://doi.org/10.3916/C77-2023-08

Schiff, D. (2022). Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. International Journal of Artificial Intelligence in Education, 32(3), 527–563. https://doi.org/10.1007/s40593-021-00270-2

Tanveer, M., Hassan, S., & Bhaumik, A. (2020). Academic Policy Regarding Sustainability and Artificial Intelligence (AI). Sustainability, 12(22), Article 22. https://doi.org/10.3390/su12229435

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x

Wu, R., & Yu, Z. (2024). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55(1), 10–33. https://doi.org/10.1111/bjet.13334

Yang, L., Zou, H., Shang, C., Ye, X., & Rani, P. (2023). Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs). Technological Forecasting and Social Change, 188, 122308. https://doi.org/10.1016/j.techfore.2022.122308

Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147

Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M. (2022). Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review. IEEE Access, 10, 61905–61921. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3179356

Downloads

Published

2024-08-09

How to Cite

BAYSHA, M. H., & ASTUTI, E. R. P. . (2024). OPTIMALISASI PEMBELAJARAN TEKNOLOGI PENDIDIKAN DENGAN BANTUAN GPT AI . EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi, 4(3), 137-149. https://doi.org/10.51878/edutech.v4i3.3143

Issue

Section

Articles