The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.

Virtual Reality (VR) applications provide an immersive experience when using panoramic images that contain a 360-degree view of the scene. Currently, the equirectangular image format is the widely used pattern to represent these panoramic images. The development of a virtual reality viewer of panoramic images should consider several parameters that define the quality of the rendered image. Such parameters include resolution configurations, texture-to-objects mappings and deciding from different rendering approach, but to select the optimal value of these parameters, visual quality analysis is required. In this work, we propose a tool integrated within Unity editor to automate this quality assessment using different settings for the visualization of equirectangular images. We compare the texture mapping of a skybox with a procedural sphere and a cubemap using full-reference objective metrics for Image Quality Analysis (IQA). Based on the assessment results, the tool decides how the final image will be rendered at the target device to produce a visually pleasing and high-quality image.

This paper presents a method for extracting and visualizing learning analytics from Lightbot using computer vision. Lightbot is a serious educational game that foments computational thinking and teaches introductory programming concepts. We collect information from recorded game sessions, such as the number of commands used, the effectiveness of the player’s solution, and the number of times the player submitted their solution for assessment. We then use this data to create graphs that help education professionals understand the challenges and successes students find when using Lightbot as a teaching tool.