The Effect of Artificial Intelligence on the Academic Development of Iranian EFL Students
Year: 2025
Type: article
Abstract: The incorporation of Artificial Intelligence (AI) with teaching and education has changed academic learning and created new opportunities and challenges for students’ academic development. The current research examines the effect of AI on EFL students’ academic performance and learning processes, focusing on their opportunities and challenges related to AI. The participants of the study consisted of ninety-five EFL students of Tehran University who used AI to learn courses better in the academic year of 2024-2025. These students were chosen using purposive sampling to ensure relevance. This research used a mixed methods design, including both quantitative and qualitative data collection techniques. A self-structured questionnaire consisted of eleven items: seven closed-ended questions measuring the perception, usage, and effectiveness of AI tools, and four open-ended questions that examined experiences, expectations, and concerns. Using SPSS version 24 software, quantitative data were analyzed by considering frequency and percentage, while qualitative answers underwent thematic analysis. The results show that 96.6 percent of participants apply AI in their academic tasks; 89.2 percent of virtual assistants are the most widely employed AI applications, supporting real-time feedback, task management, and information retrieval. Moreover, 43.4 percent of participants apply AI-powered learning platforms, indicating a shift towards interactive and personalized learning. This research also emphasizes the necessity of a structured framework for integrating AI with support from ethical guidelines. Consequently, while AI has enormous potential to improve academic performance and increase learning efficiency, its successful implementation is contingent on addressing concerns about accuracy, cognitive disengagement, and ethical implications.
Author: Safari, Ibrahim
Institution: Imam Hossein University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7081692906
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