Integrating AI and Machine Learning in Teaching Aptitude and Educational Evaluation: A Pedagogical Shift

The present conceptual paper investigates how Artificial Intelligence (AI) and Machine Learning (ML) can transform teaching ability and educational assessment. With the transformation of digital technologies in the field of education, the use of AI and ML has the potential to become an effective technology in the implementation of personalization in learning, automated assessment, and optimization of teaching. The paper explores how the introduction of AI-based systems like adaptive learning systems, intelligent tutoring or student tracking technologies helps move past the traditional pedagogy of the teacher-centered model to the learner-centric model. Such a pedagogical transformation creates real-time feedback, individualized teaching, and information-based assessments. Nonetheless, the paper also highlights other serious problems, such as privacy of data, bias in algorithms, poor training of teachers, and absence of ethical policies. Nevertheless, AI and ML can enhance the quality of teaching and increase its level of interaction with students and the diversity of the study process. The paper presents a conceptual framework, that connects pedagogical transformation and AI/ML technologies together, still insisting on the direct association of ethical implementation and policy support. By highlighting the gap in existing studies, especially those involving issues of teacher readiness and AI-based review efficacy, the paper makes a request concerning future studies and targeted use and implementation to guarantee fair and solid inclusion. Finally, this study raises not only AI and ML as the tools of technology, but also as the cause of a major paradigm shift in education.