Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers’ flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI co-writing tasks. With ABScribe, users can swiftly modify variations using LLM prompts, which are auto-converted into reusable buttons. Variations are stored adjacently within text fields for rapid in-place comparisons using mouse-over interactions on a popup toolbar. Our user study with 12 writers shows that ABScribe significantly reduces task workload (d = 1.20, p < 0.001), enhances user perceptions of the revision process (d = 2.41, p < 0.001) compared to a popular baseline workflow, and provides insights into how writers explore variations using LLMs.

In resource-constrained environments, the adoption of digital services faces formidable barriers. To address these challenges, we introduce BlendNet, an innovative platform designed to foster intermediated adoption of digital services in remote and rural areas. BlendNet harnesses satellite and intelligent edge technologies and creates a reliable and cost-effective channel for intermediaries to distribute digital content and services in remote areas. The key technology component of the system is the ‘Blendnet Hub’, a small satellite connected hardware device placed in the local kirana stores, facilitating seamless access to users without incurring data expenses, thereby addressing access, awareness, and affordability issues. We conducted a pilot study in Bihar, signing up 258 retailers who reached over 68,000 users in three months, demonstrating the crucial role of intermediaries in digital service adoption. This paper offers detailed insights into BlendNet’s design and the pilot study’s outcomes, emphasizing user and retailer engagement and adoption of services. It explores the platform’s potential as a scalable and financially viable solution.

In this paper, we investigate the potential of Large Language Models (LLMs) to improve English speaking skills. This is particularly relevant in countries like India, where English is crucial for academic, professional, and personal communication but remains a non-native language for many. Traditional methods for enhancing speaking skills often rely on human experts, which can be limited in terms of scalability, accessibility, and affordability. Recent advancements in Artificial Intelligence (AI) offer promising solutions to overcome these limitations.