Search for: All records

Generative AI, particularly Large Language Models (LLMs), has revolutionized human-computer interaction by enabling the generation of nuanced, human-like text. This presents new opportunities, especially in enhancing explainability for AI systems like recommender systems, a crucial factor for fostering user trust and engagement. LLM-powered AI-Chatbots can be leveraged to provide personalized explanations for recommendations. Although users often find these chatbot explanations helpful, they may not fully comprehend the content. Our research focuses on assessing how well users comprehend these explanations and identifying gaps in understanding. We also explore the key behavioral differences between users who effectively understand AI-generated explanations and those who do not. We designed a three-phase user study with 17 participants to explore these dynamics. The findings indicate that the clarity and usefulness of the explanations are contingent on the user asking relevant follow-up questions and having a motivation to learn. Comprehension also varies significantly based on users’ educational backgrounds. 

Medical financial literacy is essential to make smart decisions in healthcare settings and prevent unanticipated financial hardships. Existing literature has shown that young adults often struggle to understand information associated with health insurance and the financial planning necessary for health-related costs. AI-driven chatbots are emerging as educational tools that have the potential to address this issue. This exploratory study examined an AI chatbot aimed at enhancing medical financial literacy among high school students. Participants engaged with the chatbot’s responses to medical financial questions while also rating the clarity, ease of use, trustworthiness, and educational value of the chatbot engagement. Our experiment results supported that the chatbot increased students’ understanding of the financial aspect of healthcare - 76.9 percent of students reported a high degree of understanding, 80.8 percent rated the chatbot’s responses as clear, and 73.1 percent reported they would recommend it to a peer. The responses indicated that students found the chatbot helpful, but suggested that interactive features be added and/or real-world finance features be incorporated into the chatbot. 

We report a Tuning Fork Scanning Electrochemical Cell Microscopy (TF-SECCM) technique for providing morphological and electrochemical information of single redox-active entities. This new operation configuration of SECCM utilizes an electrolyte-filled nanopipette tip mounted onto a tuning fork force sensor to obtain a precise tip-sample distance control and surface morphological mapping. Redox activities of regions of interest can be investigated by scanning electrode potential by moving the nanopipette to any target regions while maintaining the constant force engagement of the tip with the sample. Using silver nanowires (Ag NWs) as a model system due to their extensive utilization in energy and sensing devices, TF-SECCM provides not only the topography of single Ag NWs but also their distinctive redox activities and catalytic hydrogen evolution reaction (HER) activities and electrolyte anion adsorption/desorption features in contrast to NW bundles and supporting substrate (e.g., indium tin oxide). 

CO 2 reduction reaction (CO 2 RR) is a promising technique for mitigating global warming and storing renewable energy if it can be obtained with a highly selective, efficient, and durable electrocatalyst. Here, we report CO 2 RR catalyzed by Au nanoparticles (NPs) stabilized by pyridines and pyrimidines (e.g., 2-mercaptopyridine (2Mpy), 4-mercaptopyridine (4Mpy), and 2-mercaptopyrimidine (2Mpym)) on a nanostructured carbon-doped TiO 2 nanowire (NanoCOT) electrode, which has been previously reported by our team for electrocatalytic water oxidation. An online gas chromatography (GC) set-up with improved gaseous product sensitivity with real-time pressure monitoring is used to quantify CO and hydrogen products from the Au NP-modified NanoCOT electrode. High CO selectivity is observed at Au-2Mpy coated NanoCOT electrode. CO 2 reduction products are not observed at bare NanoCOT suggesting CO 2 is reduced at the Au nanoparticle sites of the electrode. Moreover, CH 3 OH is not detected at the Au-Mpy/Mpym NPs during rotating ring disk electrode (RRDE) analysis which implies pyridine attached to the Au NPs has no catalytic effects on CO 2 RR as claimed by others in the literature. A durable complete H-cell using a NanoCOT anode and Au NP-NanoCOT cathode electrodes is assembled for complete water splitting, CO 2 RR, and stability test.