It has been well documented that generative AI tools are capable of solving traditional CS1-level problems as effectively as beginner students. With the addition of this technology as part of the technological landscape, it is necessary to reconsider expectations for CS1 content as well as the pedagogical approaches we choose to adopt. This poster documents the experience teaching a recent course that encouraged students to use a generative AI tool while revisiting four pedagogical tools: Problem-Centric Instruction, Flipped Instruction, Competency Demonstrations and In-Person Code reviews. Not only can AI serve as a catalyst to rethink what it means for students to engage with introductory programming, it also provides an opportunity to revisit some of the pedagogical advancements that we should have been paying attention to all along.

The rapid development of touchless systems has introduced many innovations in social interaction scenarios in recent years. People now can interact with touchless systems in social applications that are aimed to be used in everyday situations in the future. This accelerated development makes us ask, what will the next generation of touchless systems be like? How can we responsibly develop new touchless technologies in the future? To answer the first question, we brought together 20 experts to ideate, speculate, and evaluate possible touchless applications for social interactions. A total of 48 ideas were generated from two consecutive workshops. Then, to answer the second question, we critically analyzed those ideas through a thematic analysis using a responsible innovation (RI) framework, and identified key ethical considerations to guide developers, practitioners when designing future touchless systems. We argue that the social scenarios described, and the RI framework proposed in this paper are a useful starting point for responsibly designing the next generation of touchless systems.

We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm.

Stane is a hand-held interaction device controlled by tactile input: scratching or rubbing textured surfaces and tapping. The system has a range of sensors, including contact microphones, capacitive sensing and inertial sensing, and provides audio and vibrotactile feedback. The surface textures vary around the device, providing perceivably different textures to the user. We demonstrate that the vibration signals generated by stroking and scratching these surfaces can be reliably classified, and can be used as a very cheaply manufacturable way to control different aspects of interaction. The system is demonstrated as a control for a music player.

Shoogle is a novel, intuitive interface for sensing data withina mobile device, such as presence and properties of textmessages or remaining resources. It is based around activeexploration: devices are shaken, revealing the contents rattlingaround "inside". Vibrotactile display and realistic impactsonification create a compelling system. Inertial sensingis used for completely eyes-free, single-handed interactionthat is entirely natural. Prototypes are described runningboth on a PDA and on a mobile phone with a wireless sensorpack. Scenarios of use are explored where active sensing ismore appropriate than the dominant alert paradigm.

We combine the functionality of a mobile Global Positioning System (GPS) with that of an MP3 player, implemented on a PocketPC, to produce a handheld system capable of guiding a user to their desired target location via continuously adapted music feedback. We illustrate how the approach to presentation of the audio display can benefit from insights from control theory, such as predictive 'browsing' elements to the display, and the appropriate representation of uncertainty or ambiguity in the display. The probabilistic interpretation of the navigation task can be generalised to other context-dependent mobile applications. This is the first example of a completely handheld location- aware music player. We discuss scenarios for use of such systems.