Integrating Artificial Intelligence into Reading Comprehension Instruction: Opportunities and Pedagogical Strategies
This paper explores the use of Artificial Intelligence (AI) in reading comprehension training and discloses the extent of teachers' awareness, actions, perceptions of opportunities, and challenges. The study employed quantitative and descriptive survey research methods to gather data from 200 secondary school teachers using a structured questionnaire. The findings reveal that the teachers are highly aware of the potential of AI in education. The AI tools are used by the educators, along with the AI, to personalize reading tasks, provide instant feedback, and monitor student's progress. They consider AI tools as effective for comprehension, engagement, and critical thinking. The most outstanding opportunities mentioned are the advancement of inclusive practices, preparing students for digital literacy, and reducing the load of teachers. However, some barriers still dominate the scene, such as a lack of professional training, poor institutional support, limited resources, and privacy concerns in terms of ethics. The article goes on to say that although AI is capable of revolutionizing reading pedagogy, the successful and fair-going integration of it requires a stable, comprehensive teacher-training program, sound ethical principles, and infrastructural support to close the gap between ideals and reality.
Authors
- Zaheer Abbas (ORCID: https://orcid.org/0000-0002-1300-7098)
- Nisar Ahmad (ORCID: https://orcid.org/0000-0002-5955-0504)
- Nusrat Fatima
- Ashraf Hussain
- Syed Zaheer Abbas
- Fatima Khan (ORCID: https://orcid.org/0000-0002-7629-0935)
Institutions
- Quaid-i-Azam University (PK)
- Allama Iqbal Open University (PK)
- Department of Education (GB)
- Institute of Inorganic Chemistry of the Slovak Academy of Sciences (SK)
- Institute of Educational Policy (GR)
- University of Education (PK)
Publication Details
- Journal
- Research Journal for Social Affairs
- Published
- 2025-10-02
- DOI
- https://doi.org/10.71317/rjsa.003.06.0432
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00