Teacher-AI Collaboration: How Educators can Harness Artificial Intelligence without Losing Pedagogical Control
It examines if there is a link between teachers’ autonomy, how often AI is used and pedagogical power in Pakistan, to know how AI is practiced in classes. Using a survey design that gathers data from a wide range of schools, I asked 270 public and private school teachers to complete a questionnaire. Summarizing demographics was done using descriptive statistics and associations and differences among main variables were examined with correlation, regression and independent sample t-tests. The study showed that teachers who enjoy greater autonomy are often more likely to use AI technology in their teaching. According to regression analysis, how often teachers use AI significantly relates to better pedagogical control which shows that more interaction with AI tools supports their ability to teach. Also, t-test findings suggest that public school teachers enjoy more control over teaching and learning, possibly owing to differences in support and resources provided by each type of school. The results agree with existing ideas like the Technology Acceptance Model and what has been studied recently, highlighting teacher freedom, equal access to AI technology and ethical approaches. The research shows that education systems should encourage autonomy, offer targeted training and put balanced ethical advice in place for AI to be integrated safely and successfully. The results of these studies are useful for policymakers, school administrators and teacher trainers hoping to use AI boost instruction while supporting the importance of teaching people as individuals.
Authors
- Aamir Hashim
- Jawaad Bhatti (ORCID: https://orcid.org/0000-0002-6591-7560)
- Fahiza Fauz
- Tayiba Rasheed
Institutions
- National University of Modern Languages (PK)
- Riphah International University (PK)
- Fatima Jinnah Women University (PK)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- Review of Applied Management and Social Sciences
- Published
- 2025-05-26
- DOI
- https://doi.org/10.47067/ramss.v8i2.509
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00