BharatGen Yojaka: AI-assisted formative assessment for spoken language learning

BharatGen Yojaka is an AI-driven educational tool designed to support large-scale assessment of spoken language skills in school education systems. In many classrooms, especially in government-run schools with large student populations, evaluating students’ spoken language proficiency can be challenging. Teachers often rely on manual assessment methods that require recording, listening, and grading each student's spoken responses individually. This process is time-consuming and limits how frequently educators can conduct oral assessments. As a result, students receive limited feedback on their speaking abilities, which slows down language development and reduces opportunities for improvement.

The BharatGen Yojaka platform was developed to address this challenge by combining artificial intelligence with human oversight to create a scalable assessment framework. The system enables students to submit audio recordings of spoken responses through classroom devices. These recordings are processed using AI models that perform speech-to-text transcription and analyze the responses against predefined scoring rubrics. Based on these rubrics, the platform generates preliminary scores and diagnostic feedback that highlight areas where students need improvement.

A key design feature of BharatGen Yojaka is its human-in-the-loop approach. While the AI system performs initial analysis and scoring, teachers remain responsible for reviewing and approving the results before they are finalized. This approach ensures that automated evaluations do not fully replace human judgment, which is essential in language assessment where nuance and context are important. Teachers can modify or override AI-generated scores, maintaining both accuracy and trust in the evaluation process.

The platform also addresses broader educational challenges faced by teachers in low-resource schools. Many educators spend significant time preparing teaching materials and grading assignments, which leaves limited capacity for individualized instruction. BharatGen Yojaka includes tools that assist teachers in generating lesson plans, quizzes, worksheets, and storytelling exercises in multiple Indian languages. By automating some aspects of instructional preparation and evaluation, the system allows teachers to focus more on student engagement and learning outcomes.

Pilot implementations of BharatGen Yojaka in semi-urban schools have demonstrated promising results. In early trials, teachers reported that grading time decreased by approximately 30 percent compared to traditional manual evaluation methods. At the same time, agreement between AI-generated scores and teacher evaluations remained high, indicating that the automated system could reliably support assessment tasks. This reduction in grading workload enabled teachers to conduct speaking assessments more frequently, giving students more opportunities to practice and receive feedback.

The platform also incorporates strong data governance and privacy protections. Audio recordings are stored using anonymized identifiers, and recordings can be deleted after grading is completed. The system maintains audit logs for automated decisions and follows data protection frameworks aligned with emerging digital governance regulations. These safeguards ensure that student data is handled responsibly while still allowing educators to benefit from AI-powered analytics.

Overall, BharatGen Yojaka illustrates how artificial intelligence can support teachers in delivering more effective language education. By automating routine aspects of assessment while maintaining human oversight, the platform enables scalable spoken-language evaluation without sacrificing reliability or pedagogical integrity. As education systems increasingly explore digital learning tools, solutions like BharatGen Yojaka demonstrate how AI can enhance classroom assessment practices while reducing teacher workload and improving student learning outcomes.
For additional context and detailed documentation of this use case, please refer to pages 9-10 in the attached Casebook.