BoilerTAI: A Platform for Enhancing Instruction Using Generative AI in Educational Forums

Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). Background: This study is anchored in Vygotsky's socio- cultural theory, with a particular focus on the concept of the More Knowledgeable Other (MKO). It examines how GenAI can augment the instructional capabilities of course staff in educational environments, acting as an auxiliary MKO to facilitate an enriched educational dialogue between students and instructors. This theoretical backdrop is important for understanding the integration of AI within educational contexts, suggesting a balanced collaboration between human expertise and artificial intelligence to enhance the learning and teaching experience. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Employing a mixed-methods approach, our study concentrated on select first and second-year computer programming courses with significant enrollments. The investigation involved the use of an AI -assisted platform by designated (human) Teaching Assistants (AI- TAs) to pre-answer student queries on educational forums. Our analysis includes a qualitative examination of feedback and interactions, focusing on the AI-TAs' experiences and perceptions. While we primarily analyzed efficiency indicators such as the frequency of modifications required to AI generated responses, we also explored broader qualitative aspects to understand the impact and reception of AI -generated responses within the educational context. This approach allowed us to gather insights into both the quantitative engagement with AI -assisted posts and the qualitative sentiments expressed by the instructional staff, laying the groundwork for further in-depth analysis. Findings: The findings indicate no significant difference in student reception to responses generated by AI - TAs compared to those provided by human instructors. This suggests that GenAl can effectively meet educational needs when adequately managed. Moreover, AI - TAs experienced a reduction in the cognitive load required for responding to queries, pointing to GenAI's potential to enhance instructional efficiency without compromising the quality of education.