Although remote learning is widely used for delivering and capturing knowledge, it has limitations in teaching hands-on skills that require nuanced instructions and demonstrations of precise actions, such as massage. Furthermore, scheduling conflicts between instructors and learners often limit the availability of real-time feedback, reducing learning efficiency. To address these challenges, we developed a synthesis tool utilizing an LLM-powered Virtual Teaching Assistant (VTA). This tool integrates multimodal instructions that convey precise data, such as stroke patterns and pressure control, while providing real-time feedback for learners and summarizing their performance for instructors. Our case study with instructors and learners demonstrated the effectiveness of these multimodal instructions and the VTA in enhancing massage teaching and learning. We then discuss the tools' use in other hands-on skills instruction and cognitive process differences in various courses.

Charts are crucial in conveying information across various fields but are inaccessible to blind and low vision (BLV) people without assistive technology. Chart comprehension tools leveraging haptic feedback have been used widely but are often bulky, expensive, and static, rendering them inefficient for conveying chart data. To increase device portability, enable multitasking, and provide efficient assistance in chart comprehension, we introduce a novel system that delivers unobtrusive modulated electrotactile feedback directly to the fingertip edge. Our three-part study with twelve participants confirmed the effectiveness of this system, demonstrating that electrotactile feedback, when applied for 0.5 seconds with a 0.12-second interval, provides the most accurate position and direction recognition. Furthermore, our electrotactile device has proven valuable in assisting BLV participants in comprehending four commonly used charts: line charts, scatterplots, bar charts, and pie charts. We also delve into the implications of our findings on recognition enhancement, presentation modes, and function synergy.

We present SEMAGE (SEmantically MAtching imaGEs), a new image-based CAPTCHA that capitalizes on the human ability to define and comprehend image content and to establish semantic relationships between them. A SEMAGE challenge asks a user to select semantically related images from a given image set. SEMAGE has a two-factor design where in order to pass a challenge the user is required to figure out the content of each image and then understand and identify semantic relationship between a subset of them. Most of the current state-of-the-art image-based systems like Assira [20] only require the user to solve the first level, i.e., image recognition. Utilizing the semantic correlation between images to create more secure and user-friendly challenges makes SEMAGE novel. SEMAGE does not suffer from limitations of traditional image-based approaches such as lacking customization and adaptability. SEMAGE unlike the current text-based systems is also very user-friendly with a high fun factor. These features make it very attractive to web service providers. In addition, SEMAGE is language independent and highly flexible for customizations (both in terms of security and usability levels). SEMAGE is also mobile devices friendly as it does not require the user to type anything. We conduct a first-of-its-kind large-scale user study involving 174 users to gauge and compare accuracy and usability of SEMAGE with existing state-of-the-art CAPTCHA systems like reCAPTCHA (text-based) [6] and Asirra (image-based) [20]. The user study further reinstates our points and shows that users achieve high accuracy using our system and consider our system to be fun and easy.