Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet's effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025 Copyright held by the owner/author(s).

Optical see-through Head-Mounted Displays (OST HMDs, OHMDs) are known to facilitate situational awareness while accessing secondary information. However, information displayed on OHMDs can cause attention shifts, which distract users from natural social interactions. We hypothesize that information displayed in paracentral and near-peripheral vision can be better perceived while the user is maintaining eye contact during face-to-face conversations. Leveraging this idea, we designed a circular progress bar to provide progress updates in paracentral and near-peripheral vision. We compared it with textual and linear progress bars under two conversation settings: a simulated one with a digital conversation partner and a realistic one with a real partner. Results show that a circular progress bar can effectively reduce notification distractions without losing eye contact and is more preferred by users. Our findings highlight the potential of utilizing the paracentral and near-peripheral vision for secondary information presentation on OHMDs.

We transform traditional experience writing into in-situ voice-based multimedia authoring. Documenting experiences digitally in blogs and journals is a common activity that allows people to socially connect with others by sharing their experiences (e.g. travelogue). However, documenting such experiences can be time-consuming and cognitively demanding as it is typically done OUT-OF-CONTEXT (after the actual experience). We propose in-situ voice-based multimedia authoring (IVA), an alternative workflow to allow IN-CONTEXT experience documentation. Unlike the traditional approach, IVA encourages in-context content creations using voice-based multimedia input and stores them in multi-modal “snippets”. The snippets can be rearranged to form multimedia articles and can be published with light copy-editing. To improve the output quality from impromptu speech, Q&A scaffolding was introduced to guide the content creation. We implement the IVA workflow in an android application, LiveSnippets - and qualitatively evaluate it under three scenarios (travel writing, recipe creation, product review). Results demonstrated that IVA can effectively lower the barrier of writing with acceptable trade-offs in multitasking.

In this paper, we explore the possibility of applying multimodal feedback to improve multitasking performance. For this purpose, we have devised a general multitasking test application, called the MSP-Blocks, which includes many basic elements of multitasking and can be used to carry out a variety of multimodal multitasking experiments. An experiment was run to study the effects of two factors (the number of jobs and types of multimodal feedback) to user task performance, specifically, interaction effort, concurrency, fairness and output quality. The results indicated that multimodal feedback did influence multitasking performance, and moreover, non-redundant multimodal feedback was more effective than no multimodality or redundant multimodality for tasks with reasonable difficulty, e.g. when the number of jobs was more than four.