Introduction:

The integration of artificial intelligence (AI) into education has sparked significant interest, particularly in enhancing teaching practices and supporting professional development. In-service teachers, who are already in the field, often face challenges in continuously updating their pedagogical strategies, especially when dealing with diverse student needs. Traditional professional development programs, while valuable, often fail to provide the personalized, ongoing support that teachers need to adapt and improve their teaching methods. This study explores the potential of AI-driven teaching programs to address these gaps by providing in-service teachers with data-driven insights to refine their instructional practices.

AI technologies, such as machine learning and natural language processing, offer personalized learning experiences by analyzing vast amounts of data. In education, AI has primarily been used to enhance student learning outcomes, for example, through adaptive learning platforms that adjust content based on individual student needs (Luckin et al., 2016). However, AI’s role in supporting teachers’ professional development has been less explored. Some studies have indicated that AI can offer valuable real-time feedback to teachers, allowing for more reflective practices and personalized professional growth (Siang & Zhou, 2020). The key advantage of AI in teacher development is its ability to provide tailored recommendations based on real-time classroom data, something that traditional professional development methods cannot achieve.

The primary aim of this study is to evaluate the effectiveness of an AI-driven teaching program in improving the teaching methods of in-service teachers in mainstream schools. Specifically, this research seeks to assess whether AI can help teachers enhance their ability to differentiate instruction, engage students more effectively, and incorporate technology into their teaching practices. Given that in-service teachers often have limited time for professional development, this study also explores the feasibility of AI tools as an accessible, scalable solution for continuous improvement.

To address these objectives, the study is guided by the following research questions:

1. How does an AI-driven teaching program impact the instructional methods of in-service teachers?
2. To what extent does AI improve teachers' ability to differentiate instruction and engage students?
3. What are the perceived benefits and challenges of using AI in teacher professional development?
4. How do students perceive changes in classroom engagement and learning experiences as a result of AI-assisted teaching?

The importance of this study lies in the potential impact of AI-driven programs on the quality of education. Teacher effectiveness is a key determinant of student success, and equipping educators with tools that can enhance their instructional skills is crucial for improving learning outcomes across diverse classroom settings. Additionally, as AI continues to evolve, it could serve as a central component in teacher training programs, offering a scalable model for professional development that can be implemented globally.

Despite the growing interest in AI for educational purposes, there are challenges in its practical application. Resistance to technology adoption, concerns about data privacy, and a lack of sufficient training in AI are common barriers (Spector, 2019). Furthermore, while AI holds promise, its impact on teaching practices and student learning outcomes remains underexplored. Therefore, this study aims to fill this gap by examining how an AI-driven program can specifically improve in-service teachers’ instructional strategies and classroom practices.

Methodology: This study employed a mixed-methods design to evaluate the impact of an AI-driven teaching program on in-service teachers. A purposive sample of 50 teachers from five mainstream schools participated, ensuring diversity in teaching experience and subject specialization. Teachers were divided into an intervention group (AI program) and a control group (traditional PD). The AI program was a cloud-based platform offering personalized feedback on lesson plans, student engagement, and instructional strategies. Teachers in the intervention group used the program for 12 weeks, with access to real-time data on teaching practices and student performance. Data collection involved pre- and post-intervention surveys assessing teaching confidence and practices. Semi-structured interviews with 10 teachers provided qualitative insights. Classroom observations were conducted before and after the intervention to track changes in teaching strategies, such as differentiation and student engagement. Additionally, student surveys evaluated perceived changes in classroom dynamics. Quantitative data from the surveys were analyzed using paired t-tests, while qualitative data from interviews and observations were analyzed thematically. Results: The AI teaching program significantly enhanced teaching practices in the intervention group. Teachers showed a 15% improvement in teaching confidence and a 20% increase in their ability to differentiate instruction. Classroom observations revealed greater use of technology and more personalized lessons. Students reported a 10% increase in engagement, citing more interactive and tailored lessons. Qualitative data from interviews revealed that teachers found AI-driven feedback invaluable for reflecting on their practices. Many highlighted the program’s ability to provide actionable insights, leading to more dynamic and engaging lessons. Teachers also reported increased confidence in adapting their methods to individual student needs. The control group showed minimal improvement, with no significant changes in their teaching methods or use of technology, reinforcing the program's effectiveness in the intervention group. Discussion: The results suggest that AI-driven teaching programs can significantly improve teaching practices by providing personalized, data-driven feedback. Teachers in the intervention group were better able to differentiate lessons and engage students by both quantitative and qualitative data. The AI system's real-time analytics supported reflective teaching, allowing teachers to make targeted adjustments in their classrooms. However, challenges such as initial resistance to technology and concerns about data privacy were noted, indicating the need for adequate training and support. These findings are consistent with previous studies showing the potential of AI to enhance professional development (Luckin et al., 2016; Siang & Zhou, 2020), although there are concerns about its practical implementation.

Conclusion: This study has demonstrated the significant potential of AI-driven teaching programs in enhancing the instructional methods of in-service teachers. The results indicate that AI tools, when integrated into professional development, can provide valuable, personalized feedback that helps teachers refine their teaching strategies, improve differentiation, and engage students more effectively. Teachers in the intervention group reported increased confidence in using AI to adapt their lessons, and classroom observations confirmed a shift towards more dynamic and personalized teaching practices. Students also perceived a positive impact, noting increased engagement and a more interactive learning environment. The findings highlight the promise of AI in revolutionizing teacher professional development. By offering real-time, data-driven insights, AI enables teachers to make adjustments in their teaching methods based on student needs, creating a more responsive and flexible learning environment. These results suggest that AI could serve as a scalable, accessible tool for continuous teacher improvement, particularly in mainstream schools where resources for traditional professional development may be limited. However, while the study shows promising outcomes, it also points to challenges in the implementation of AI in education. Initial resistance from teachers, concerns about data privacy, and the need for adequate training were notable barriers. Future research should focus on overcoming these challenges and exploring the long-term impact of AI on teaching practices and student outcomes. In conclusion, this research provides evidence that AI-driven teaching programs can play a transformative role in enhancing the pedagogical skills of in-service teachers. As AI technologies evolve, they hold the potential to reshape how professional development is delivered, offering personalized, ongoing support that meets the needs of today’s educators and students.

References: Kapp, K. M., & O'Driscoll, T. (2018). AI in Education: Shaping the Future of Learning. Educational Technology Publications. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson. Siang, W. S., & Zhou, L. (2020). The Role of AI in Teacher Professional Development: Benefits and Challenges. Journal of Educational Technology, 34(2), 108-122. Spector, J. M. (2019). Artificial Intelligence and Teacher Training: Overcoming Challenges in Integration. Journal of Educational Research, 112(3), 290-301.