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

TL;DR

This study introduces BoilerTAI, a platform that integrates Generative AI with educational forums to augment instructional staff while maintaining a human-in-the-loop for quality control. Using a mixed-methods design in four large CS courses, the authors show that AI-assisted responses can improve efficiency and quality, with most student interactions remaining positive and personalized. However, approximately half of AI-suggested replies required staff corrections, underscoring the ongoing need for human oversight and ethical safeguards. The work demonstrates a practical, scalable approach to leveraging GenAI for instruction in high-enrollment courses, with clear implications for workload management and instructional quality in online discussion environments.

Abstract

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). This contribution enhances the efficiency and effectiveness of instructional support and significantly improves the quality and speed of responses provided to students, thereby enriching the overall learning experience. Background: Grounded in Vygotsky's socio-cultural theory and the concept of the More Knowledgeable Other (MKO), the study examines how GenAI can act as an auxiliary MKO to enrich educational dialogue between students and instructors. 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: Using a mixed-methods approach in large introductory programming courses, human Teaching Assistants (AI-TAs) employed an AI-assisted platform to pre-answer student queries. We analyzed efficiency indicators like the frequency of modifications to AI-generated responses and gathered qualitative feedback from AI-TAs. 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 GenAI 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.

Figures (1)

• Figure 1: The generation of a response in BoilerTAI: The process begins with a student posting a question. The AI generates a response based on prompts and course documents, which is then reviewed and approved by AI-TAs before being posted. There are options for customized reprompting and manual editing to ensure accuracy and relevance.