In the realm of education, the assessment of exam papers is a pivotal component, involving the formulation of well-structured questions and the subsequent evaluation of student responses. This process is laden with challenges, consuming extensive time, resources, and manpower and expertise in Kannada Language. Moreover, the human-driven evaluation is susceptible to biases influenced by the evaluator's circumstances and context and hold on languageIn light of these challenges, there emerges a transformative solution: a proficiently trained Exam Paper Evaluation Module. This module harnesses the power of Large Language Models for Kannada Language to comprehend questions and responses, thereby revolutionizing the conventional evaluation process. By extracting pertinent features from the answers, this module learns to assess the content and quality of student replies, thus automating and expediting the evaluation process significantly.Here we try to address the lack of good quality teachers of Kannada language using GenAI and LLMS.The advantages are manifold. Not only does this innovation save time and resources, but it also mitigates the biases inherent like Grader, Cultural, Stereotype, Confirmation, Halo Effect, Leniency or Severity, Examiner Fatigue, Recency, Confirmation of Expectations, Subject Knowledge Biass in human grading like. This transformative model is versatile, capable of adapting to diverse Indian languages, subjects, grading systems, and even distinct universities. Its potential impact on education is profound, heralding a new era of efficiency and fairness in assessment procedures.By leveraging Generative AI, this Exam Paper Evaluation Module heralds a future where students' performances are assessed objectively, swiftly, and consistently. As education transcends geographical and linguistic boundaries, this model stands as a beacon of advancement, ensuring that evaluation remains equitable and unbiased across various educational contextsIn our effort to mitigate linguistic biases, we take steps such as diversifying our data, especially by including underrepresented languages like Kannada. We employ bias mitigation strategies and train the model to better comprehend language nuances and sensitivities. Additionally, we work on making the model's decision-making process more transparent and interpret able. We also maintain a system of continuous feedback to enhance the model's learning. Furthermore, the responsibility for ensuring fairness and inclusivity lies not only with the developers but also with the human designers, reviewers, and institutions using these models. Together, we collaborate to construct AI models that prioritize diversity and minimize biases in grading exam applications.