Investigating How AI Tools Can Assist Teachers in Refining Their Pedagogy and Enhancing Student Learning Experiences
The application of Artificial Intelligence (AI) in the education sector is actually reshaping pedagogical practices, assessment procedures, and teacher professional development. The present study aimed to examine the impact of AI tools on teaching efficacy, student motivation, and correctness in assessment among Pakistani school teachers. A quantitative research design was utilized, and data were collected from 215 randomly selected school teachers through a self-administered Likert scale-based questionnaire. The analyzed data were processed with the help of the regression, multiple regression, and ANOVA techniques. The results revealed that AI-based grading significantly reduces the workload of teachers and provides timely feedback, but its impact on teaching practices and student motivation is statistically non-significant. In addition to this, the study also highlighted data privacy, algorithmic bias, and equitable distribution of AI-driven educational resources as areas of concern. Although these concerns are present, AI has the potential to enhance differentiated learning experiences, automate administrative tasks, and support the professional development of teachers. Future research should focus on enhancing AI algorithms to ensure unbiased decision-making and wider availability of AI-driven educational packages across various socioeconomic settings.
Authors
- Nida Shahzad (ORCID: https://orcid.org/0000-0001-7124-3809)
- Imtiaz Hussain Khan (ORCID: https://orcid.org/0000-0003-3653-009X)
- Muhammad Tufail (ORCID: https://orcid.org/0000-0002-4113-8183)
- Muhammad Naseer
Institutions
- Bahauddin Zakariya University (PK)
- University of Sargodha (PK)
- National University of Modern Languages (PK)
- Northern University (PK)
Publication Details
- Journal
- The critical review of social sciences studies
- Published
- 2025-04-12
- DOI
- https://doi.org/10.59075/758a2610
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00