Teaching Assistant Based on a Brazilian Large Language Model

Context: The use of AI Teaching Assistants (AI-TAs) in educational contexts aims to support student learning in complex subjects, where there are challenges in assimilating interdisciplinary concepts and a need for quick and accurate responses. Problem: Students face barriers when studying subjects that require prior knowledge in multiple areas, leading to comprehension difficulties that negatively affect their academic progress. Proposed Solution: An AI-TA based on the Brazilian language model Sabiá 2.0 is proposed, incorporating mechanisms for Quick Verification, Expansion, and Error Correction to enhance the question-and-answer system. IS Theory: This work is grounded in Social Learning Theory, which explores how individuals acquire knowledge in technology-mediated interaction contexts. This theory is applicable to the use of AI-TAs, which act as learning mediators, promoting knowledge acquisition and facilitating understanding of complex concepts. Method: The methodology is Design Science Research, with a descriptive approach and quantitative analysis, including proof of concept and practical evaluation with users. The system was implemented in an educational environment and tested for performance and student satisfaction. Summary of Results: The system showed a high satisfaction rate regarding response quality; however, it revealed limitations in response time for model-based queries, suggesting improvements in API optimization. Contributions and Impact in IS Field: This study contributes to the IS field by demonstrating how AI models can enhance teaching and student support, proposing a robust and adaptable solution for learning environments.