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Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning

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arxiv 2406.07796 v2 pith:CTW7GQKD submitted 2024-06-12 cs.HC cs.AI

Maung Thway , Jose Recatala-Gomez , Fun Siong Lim , Kedar Hippalgaonkar , Leonard W. T. Ng This is my paper · ORCID
classification cs.HCcs.AI
keywords learninggenaibotpoopleodarprofessoraugmentedchatbotchatbots
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Generative artificial intelligence (GenAI) and large language models (LLMs) have simultaneously opened new avenues for enhancing human learning and increased the prevalence of poor-quality information in student response - termed Botpoop. This study introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval Augmented Generation (RAG) chatbot designed to enhance educational while reducing Botpoop. Deployed at Nanyang Technological University, Singapore, Professor Leodar offers a glimpse into the future of AI-assisted learning, offering personalized guidance, 24/7 availability, and contextually relevant information. Through a mixed-methods approach, we examine the impact of Professor Leodar on learning, engagement, and exam preparedness, with 97.1% of participants reporting positive experiences. These findings help define possible roles of AI in education and highlight the potential of custom GenAI chatbots. Our combination of chatbot development, in-class deployment and outcomes study offers a benchmark for GenAI educational tools and is a stepping stone for redefining the interplay between AI and human learning.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting

    cs.ET 2025-06 conditional novelty 6.0 of 10

    In blind pairwise comparisons, educators rated an LLM tutor (MWPTutor) as better than human tutors from MathDial on empathy, scaffolding, and conciseness, with no significant advantage on engagement.

  2. Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval

    cs.IR 2025-06 reject novelty 4.0 of 10

    A RAG-based tutoring and support chatbot with hybrid search and an instructor feedback loop is described, but no quantitative evaluation of its accuracy is provided.

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