GPS and smartphones enable users to place location-based annotations, capturing rich environmental context. Previous research demonstrates that blind and low vision (BLV) people can use annotations to explore unfamiliar areas. However, current commercial systems allowing BLV users to create annotations have never been evaluated, and current GPS-based systems can deviate several meters. Motivated by high-accuracy visual positioning technology, we first conducted a formative study with 24 BLV participants to envision a more accurate and inclusive annotation system. Surprisingly, many participants viewed the high-accuracy technology not just as an annotation system but also as a tool for precise last-few-meters navigation. Guided by participant feedback, we developed NaviNote, which combines vision-based high-precision localization with an agentic architecture to enable voice-based annotation authoring and navigation. Evaluating NaviNote with 18 BLV participants showed that it significantly improved navigation performance and supported users in understanding and annotating their surroundings. Based on these findings, we discuss design considerations for future accessible annotation authoring systems.

Authoring site-specific outdoor augmented reality (AR) experiences requires a nuanced understanding of real-world context to create immersive and relevant content. Existing ex-situ authoring tools typically rely on static 3D models to represent spatial information. However, in our formative study (n=25), we identified key limitations of this approach: models are often outdated, incomplete, or insufficient for capturing critical factors such as safety considerations, user flow, and dynamic environmental changes. These issues necessitate frequent on-site visits and additional iterations, making the authoring process more time-consuming and resource-intensive. To mitigate these challenges, we introduce CoCreatAR, an asymmetric collaborative mixed reality authoring system that integrates the flexibility of ex-situ workflows with the immediate contextual awareness of in-situ authoring. We conducted an exploratory study (n=32) comparing CoCreatAR to an asynchronous workflow baseline, finding that it enhances engagement, creativity, and confidence in the authored output while also providing preliminary insights into its impact on task load. We conclude by discussing the implications of our findings for integrating real-world context into site-specific AR authoring systems.

While augmented reality (AR) enables new ways to play, tell stories, and explore ideas rooted in the physical world, authoring personalized AR content remains difficult for non-experts, often requiring professional tools and time. Prior systems have explored AI-driven XR design but typically rely on manually defined VR environments and fixed asset libraries, limiting creative flexibility and real-world relevance. We introduce ImaginateAR, the first mobile tool for AI-assisted AR authoring to combine offline scene understanding, fast 3D asset generation, and LLMs -- enabling users to create outdoor scenes through natural language interaction. For example, saying "a dragon enjoying a campfire" (P7) prompts the system to generate and arrange relevant assets, which can then be refined manually. Our technical evaluation shows that our custom pipelines produce more accurate outdoor scene graphs and generate 3D meshes faster than prior methods. A three-part user study (N=20) revealed preferred roles for AI, how users create in freeform use, and design implications for future AR authoring tools. ImaginateAR takes a step toward empowering anyone to create AR experiences anywhere -- simply by speaking their imagination.

People are increasingly using their smartphones to 3D scan objects and landmarks. On one hand, users have intrinsic motivations to scan well, i.e. keeping the object in-frame while walking around it to achieve coverage. On the other, users can lose interest when filming inanimate objects, and feel rushed and uncertain of their progress when watching their step in public, seeking to avoid attention.

Conversational agents are rapidly becoming commonplace. However, since these systems are typically blackboxed, users—including vulnerable populations, like children—often do not understand them deeply. For example, they might assume agents are overly intelligent, leading to frustration and distrust. Users may also overtrust agents, and thus overshare personal information or rely heavily on agents' advice. Despite this, little research investigates users' perceptions of conversational agents in-depth, and even less investigates how education might change these perceptions to be more healthy. We present workshops with associated educational conversational AI concepts to encourage healthier understanding of agents. Through studies with the curriculum with children and parents from various countries, we found participants' perceptions of agents—specifically their partner models and trust—changed. When participants discussed changes in trust of agents, we found they most often mentioned learning something. For example, they frequently mentioned learning where agents obtained information, what agents do with this information and how agents are programmed. Based on the results, we developed recommendations for teaching conversational agent concepts, including emphasizing the concepts students found most challenging, like training, turn-taking and terminology; supplementing agent development activities with related learning activities; fostering appropriate levels of trust towards agents; and fostering accurate partner models of agents. Through such pedagogy, students can learn to better understand conversational AI and what it means to have it in the world.

Historically, researchers have focused on analyzing Western, Educated, Industrialized Rich and Democratic (WEIRD), adult perspectives on technology. This means we may not have technology developed appropriately for children and those from non-WEIRD countries. In this paper, we analyze children and parents from various countries’ perspectives on an emerging technology: conversational agents. We aim to better understand participants’ trust of agents, partner models, and their ideas of “ideal future agents” such that researchers can better design for these users—for instance, by ensuring children do not overtrust agents. Additionally, we empower children and parents to program their own agents through educational workshops, and present changes in perceptions as participants create and learn about agents. Results from the study (n=49) included how children felt agents were significantly more human-like, warm, and dependable than parents did, how overall participants trusted agents more than parents or friends for correct information, how children described their ideal agents as being more artificial than human-like than parents did, and how children tended to focus more on fun features, approachable/friendly features and addressing concerns through agent design than parents did, among other results. We also discuss potential agent design implications of the results, including how designers may be able to best foster appropriate levels of trust towards agents by focusing on designing agents’ competence and predictability indicators, as well as increasing transparency in terms of agents’ information sources.

With children talking to smart-speakers, smart-phones and even smart-microwaves daily, it is increasingly important to educate students on how these agents work—from underlying mechanisms to societal implications. Researchers are developing tools and curriculum to teach K-12 students broadly about artificial intelligence (AI); however, few studies have evaluated these tools with respect to AI-specific learning outcomes, and even fewer have addressed student learning about AI-based conversational agents. We evaluated our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to 8 AI competencies from the literature. Furthermore, we analyze teacher (n=9) and student (n=47) feedback from workshops with the interface and recommend that future work (1) leverages design considerations to optimize engagement, (2) collaborates with teachers, and (3) addresses a range of student abilities through pacing and opportunities for extension. We found evidence for student understanding of all 8 competencies, with the most difficult concepts being AI ethics and machine learning. We recommend emphasizing these topics in future curricula.

Growing up in an artificial intelligence-filled world, with Siri and Amazon Alexa often within arm’s—or speech’s—reach, could have significant impact on children. Conversational agents could influence how students anthropomorphize computer systems or develop a theory of mind. Previous research has explored how conversational agents are used and perceived by children within and outside of learning contexts. This study investigates how middle and high school students’ perceptions of Alexa change through programming their own conversational agents in week-long AI education workshops. Specifically, we investigate the workshops’ influence on student perceptions of Alexa’s intelligence, friendliness, aliveness, safeness, trustworthiness, human-likeness, and feelings of closeness. We found that students felt Alexa was more intelligent and felt closer to Alexa after the workshops. We also found strong correlations between students’ perceptions of Alexa’s friendliness and trustworthiness, and safeness and trustworthiness. We recommend designers carefully consider personification, transparency, playfulness and utility when designing conversational agents for learning contexts.

Artificial Intelligence (AI) education is an increasingly popular topic area for K-12 teachers. However, little research has investigated how AI curriculum and tools can be designed to be more accessible to all teachers and learners. In this study, we take a Value-Sensitive Design approach to understanding the role of teacher values in the design of AI curriculum and tools, and identifying opportunities to integrate AI into core curriculum to leverage learners’ interests. We organized co-design workshops with 15 K-12 teachers, where teachers and researchers co-created lesson plans using AI tools and embedding AI concepts into various core subjects. We found that K-12 teachers need additional scaffolding in AI tools and curriculum to facilitate ethics and data discussions, and value supports for learner evaluation and engagement, peer-to-peer collaboration, and critical reflection. We present an exemplar lesson plan that shows entry points for teaching AI in non-computing subjects and reflect on co-designing with K-12 teachers in a remote setting.

Smart assistants, like Amazon's Alexa or Apple's Siri, have become commonplace in many people's lives, appearing in their phones and homes. Despite their ubiquity, these conversational AI agents still largely remain a mystery to many, in terms of how they work and what they can do. To lower the barrier to entry to understanding and creating these agents for young students, we expanded on Convo, a conversational programming agent that can respond to both voice and text inputs. The previous version of Convo focused on teaching only programming skills, so we created a simple, intuitive user interface for students to use those programming skills to train and create their own conversational AI agents. We also developed a curriculum to teach students about key concepts in AI and conversational AI in particular. We ran a 3-day workshop with 15 participating middle school students. Through the data collected from the pre- and post-workshop surveys as well as a mid-workshop brainstorming session, we found that after the workshop, students tended to think that conversational AI agents were less intelligent than originally perceived, gained confidence in their abilities to build these agents, and learned some key technical concepts about conversational AI as a whole. Based on these results, we are optimistic about CONVO'S ability to teach and empower students to develop conversational AI agents in an intuitive way.

Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.

Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers. While conversational agents have often been used for short goal-oriented dialog, we know little about agents for developing computer programs. To explore the utility of natural language for programming, we conducted a study (n=45) comparing different input methods to a conversational programming system we developed. Participants completed novice and advanced tasks using voice-based, text-based, and voice-or-text-based systems. We found that users appreciated aspects of each system (e.g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users. Our results show that future conversational programming tools should be tailored to users' programming experience and allow users to choose their preferred input mode. To reduce cognitive load, future interfaces can incorporate visualizations and possess custom natural language understanding and speech recognition models for programming.

Artificial Intelligence (AI) technology can be found nearly everywhere. However, developing and controlling this technology is generally limited to large companies or those with extensive training in computer science. It is difficult for many who have application ideas for AI to even know where to start when developing AI technology.

Autonomous vehicle technology has been rapidly expanding through the incorporation of advanced driver assistance systems in many new vehicles. The integration of autonomous vehicle technology to assist and alert drivers is essential to increase driver safety. The main aim of this paper is to (1) compare real driving data from the Next Generation SIMulation I-80 dataset to an "ideal" driving scenario and (2) develop a tool that can be used to prescreen large datasets and filter the data points according to specific study parameters. This proposed tool uses a fuzzy inference system which outputs a warning level based on three inputs including relative velocity between the host and the preceding vehicle, velocity of the host vehicle and time headway. The warning level is used as a measure for initial analysis of real-life driving data to categorize the data and identity "unsafe" driving patterns. The "ideal" driving scenario and real driving data are compared and visualized using a graphical simulation in MATLAB. This visual comparison clearly highlights the importance of the integration of autonomous vehicle technology to increase driver safety.

Collision warning and avoidance is a well-established area of research for the automotive industry. However, there is little research towards vitally important collision warning systems for cyclists, who are increasingly jeopardized by motorists on urban roads, especially as quiet, fast electric vehicles become more popular. This paper describes the hardware and software of a low-cost collision warning system for cyclists. Installed on the back of a bike seat, the system consists of a single-beam laser rangefinder and two ultrasonic sensors that detect oncoming vehicles from behind, two handlebar eccentric mass vibrators that provide left and right haptic feedback to the cyclist, and a taillight that warns oncoming vehicles. Executed by an Arduino microcontroller, its software consists of a fuzzy rule-based inference system (FIS), which computes the collision risk and generates appropriate warning signals in a similar way to how a cyclist would assess collision risk based on the distance, velocity and direction of an approaching vehicle. The device was prototyped and statistically evaluated by a survey taken from a pool of seven participants. The participants tested the system before and after receiving initial training. The experimental results demonstrate the efficacy of the proposed system in warning cyclists in an intuitive manner, without distracting them.