Recent Advances in Generative AI and Their Impact on Education: Exploring Self-Efficacy in Learning Environments
DOI:
https://doi.org/10.56294/ai2025430Keywords:
Learning outcomes, Generative AI, Education, Survey analysis, Student engagement, Self-efficacy, Technological integrationAbstract
Abstract:
The study analyzes recent advances in generative AI and examines how these technologies interact with student self-efficacy within educational settings. It discusses how innovative AI tools can enrich learning experiences and aims to outline potential roles for these tools in boosting learners’ confidence and motivation.This study emphasis on the comprehensive understanding of how GenAI technology will increase confidence levels of a student into academic work.
Method:
A quantitative approach was employed with a sample of 161 students from diverse disciplines. Participants completed a self-efficacy scale and answered items related to their experience with generative AI tools. Statistical analysis were conducted to explore relationships between AI engagement and self-efficacy levels.
Results:
The analysis's findings show a positive relationship between the application of generative AI and improved self-efficacy in pupils. The outcomes include increased confidence in learning activities and a stronger willingness to take on difficult tasks.The mean score on the Self-Efficacy (SE) scale was 3.12 , SD = 0.58, indicating that on average, the participants reported a relatively high level of self-efficacy. This suggests a sample of students who, in general, feel confident in their ability to overcome challenges and succeed academically.
Regarding generative AI usage, the data showed a wide range of engagement. The mean frequency of use was 3.45, SD = 1.15, with a significant portion of students reporting that they "often" or "very often" use GenAI tools for academic purposes. The most commonly reported uses were for brainstorming ideas (78% of users), followed by drafting outlines (65%) and revising text (55%). A smaller percentage reported using AI for complex tasks such as generating code (28%) or scientific summaries (22%).
Conclusions:
The findings suggest that introducing generative artificial intelligence into educational programs might be beneficial to the growth of students; nevertheless, additional research is necessary. The findings of this study highlight the importance of self-efficacy as a potential mediator in learning advances offered by artificial intelligence. Future research should investigate specific uses of artificial intelligence and the ways in which these applications affect the teaching and learning processes.
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