APPLICATION AND IMPACT ANALYSIS OF AI-BASED TEACHING ASSISTANT SYSTEMS IN HIGHER EDUCATION

• Zarovshan Babayeva

  Nakhchivan State University

  Author

• Erdal Bay

  Gaziantep University

  Author

• Aynur Babayeva

  Shusha International Intellectuals Institute

  Author

In recent years, Artificial Intelligence (AI) technologies have been increasingly integrated into higher education to enhance the quality of teaching and personalize students’ learning experiences. In particular, AI-based Teaching Assistant (TA) systems have emerged as innovative tools that provide real-time academic support, automate question–answer interactions, guide students through laboratory and practical tasks, and deliver personalized feedback. These systems also contribute to reducing instructors’ administrative and assessment workload, thereby improving the overall efficiency and effectiveness of the teaching process. The primary aim of this study is to evaluate the impact of AI-based TA systems on undergraduate students’ academic performance, motivation, and classroom engagement. The research was conducted using an experimental design involving a total of 150 students, who were divided into two groups: a control group receiving traditional instruction and an experimental group supported by AI-based TA systems. Data were collected through multiple instruments, including academic performance records, motivation and engagement questionnaires, and user experience surveys completed by both students and instructors. The findings of the study indicate that the integration of AI-based TA systems significantly improves students’ academic outcomes, enhances their motivation and active participation in learning activities, and fosters a more interactive and student-centered learning environment. Furthermore, the results reveal that these systems effectively reduce instructors’ workload by automating routine tasks such as grading, feedback provision, and student support, allowing educators to focus more on pedagogical strategies and individualized instruction.

In addition, the study highlights that AI-supported learning environments outperform traditional teaching approaches in terms of adaptability, responsiveness, and personalization. Students in the experimental group demonstrated higher engagement levels, better knowledge retention, and increased satisfaction with the learning process. From the instructors’ perspective, AI TA systems were perceived as valuable tools that enhance teaching efficiency and instructional quality. In conclusion, the integration of AI-based Teaching Assistant systems represents a transformative approach in higher education, contributing to improved learning outcomes, increased student motivation, and optimized teaching practices. The results of this research provide practical implications for the effective implementation of AI in educational settings and offer recommendations for developing future AI-driven teaching models that are adaptive, scalable, and learner-centered.