The Influence of Artificial Intelligence (AI) Adoption on the Academic Performance of the Pre-Service Teachers
- DOI
- 10.2991/978-94-6463-750-2_77How to use a DOI?
- Keywords
- Artificial Intelligence (AI); education; academic performance; pre-service teachers
- Abstract
This study examined the influence of AI adoption on the academic performance of pre-service teachers, focusing on factors like perspective on using AI, self-confidence, user-friendliness, apprehension, and purposeful behavior. A descriptive-correlational research design was used to explore these connections. Findings show that while pre-service teachers adopt AI at a high level, their AI-related competencies and attitudes do not significantly affect their academic performance. This suggests that factors beyond AI adoption, such as study habits, learning strategies, and curriculum design, influence academic success. The study recommends integrating AI-based pedagogical training into curricula to prepare future educators better. It also calls for a holistic approach to evaluating AI’s role in education and highlights the need for policies that effectively incorporate AI while considering other academic influences.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ariel U. Cubillas AU - Lynzee L. Lusica AU - Twila N. Sabanal AU - Genroh A. Yula PY - 2025 DA - 2025/06/15 TI - The Influence of Artificial Intelligence (AI) Adoption on the Academic Performance of the Pre-Service Teachers BT - Proceedings of the 2025 4th International Conference on Educational Innovation and Multimedia Technology (EIMT 2025) PB - Atlantis Press SP - 766 EP - 773 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-750-2_77 DO - 10.2991/978-94-6463-750-2_77 ID - Cubillas2025 ER -