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

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.

Harnessing GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbot's Impact on Human Learning

The advent of generative artificial intelligence (GenAI) and large language models (LLMs) has opened new avenues for enhancing human learning. This study introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval Augmented Generation (RAG) chatbot designed to enhance educational support for undergraduate engineering students. 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 uncover the impact of Professor Leodar on student 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 study of learning outcomes offers a benchmark for GenAI educational tools and serves as stepping stone for redefining the interplay between AI and human learning.

Local stochastic computing using memristor-enabled stochastic logics

Stochastic computing offers a probabilistic approach to address challenges posed by problems with uncertainty and noise in various fields, particularly machine learning. The realization of stochastic computing, however, faces the limitation of developing reliable stochastic logics. Here, we present stochastic logics development using memristors. Specifically, we integrate memristors into logic circuits to design the stochastic logics, wherein the inherent stochasticity in memristor switching is harnessed to enable stochastic number encoding and processing with well-regulated probabilities and correlations. As a practical application of the stochastic logics, we design a compact stochastic Roberts cross operator for edge detection. Remarkably, the operator demonstrates exceptional contour and texture extractions, even in the presence of 50% noise, and owning to the probabilistic nature and compact design, the operator can consume 95% less computational costs required by conventional binary computing. The results underscore the great potential of our stochastic computing approach as a lightweight local solution to machine learning challenges in autonomous driving, virtual reality, medical diagnosis, industrial automation, and beyond.