• Abhiraj Mohan Kadam
• Nirbhay Singh

The rapid integration of Artificial Intelligence (AI) into education has transformed the way learners access information, receive feedback, and engage with instructional content. AI-driven Teaching Assistants (AI-TAs) have emerged as scalable tools capable of answering student queries, supporting personalized learning, automating grading, and facilitating interactive learning experiences. Although recent studies suggest that AI-TAs can match or even surpass human teaching assistants in response speed, availability, and consistency, several critical challenges remain unresolved. These include the risk of hallucinated content, lack of accountability, algorithmic bias, pedagogical misalignment, and the absence of internal quality assurance mechanisms. Most existing AI-TAs are designed to optimize linguistic fluency and task completion rather than epistemic reliability and ethical compliance. This paper proposes a novel framework for a Self-Validating and Self-Regulating Artificial Intelligence Teaching Assistant (SVSR-AI-TA) that embeds reflective and governance capabilities directly into the system. The framework introduces two core layers: a self-validation layer that verifies factual accuracy, confidence, and consistency, and a self-regulation layer that enforces ethical, and institutional constraints. Over-reliance on AI-generated solutions may reduce learners’ critical thinking and problem-solving skills [1]. Drawing upon a synthesis of fifteen peer-reviewed studies in Artificial Intelligence in Education (AIEd), this work identifies structural limitations in current AI-TAs and demonstrates how internal governance mechanisms can address these gaps. A multi-layered system architecture is presented in which generative modules operate alongside validation engines, regulatory controls, and adaptive feedback loops. The paper argues that future educational AI systems must transition from reactive tools into reflective learning partners capable of evaluating and regulating their own behavior. The proposed framework aims to enhance trust, fairness, learner engagement, and institutional scalability in higher education.

Artificial Intelligence in Education, AI Teaching Assistant, Self-Validation, Self-Regulation, Generative AI, Retrieval-Augmented Generation, Ethics, Adaptive Learning.

"A SELF-VALIDATING AND SELF-REGULATING ARTIFICIAL INTELLIGENCE TEACHING ASSISTANT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.b747-b750, February-2026, Available :http://www.jetir.org/papers/JETIR2602199.pdf

"A SELF-VALIDATING AND SELF-REGULATING ARTIFICIAL INTELLIGENCE TEACHING ASSISTANT", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. ppb747-b750, February-2026, Available at : http://www.jetir.org/papers/JETIR2602199.pdf