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Sahana Rangasrinivasan

Publications and source records attributed to Sahana Rangasrinivasan.

2 recordsLinked to original sources

Teaching Spell Checkers to Teach: Pedagogical Program Synthesis for Interactive Learning

Spelling taught through memorization often fails many learners, particularly children with language-based learning disorders who struggle with the phonological skills necessary to spell words accurately. Educators such as speech-language pathologists (SLPs) address this instructional gap by using an inquiry-based approach to teach spelling that targets the phonology, morphology, meaning, and etymology of words. Yet, these strategies rarely appear in everyday writing tools, which simply detect and autocorrect errors. We introduce SPIRE (Spelling Inquiry Engine), a spell check system that brings this inquiry-based pedagogy into the act of composition. SPIRE implements Pedagogical Program Synthesis, a novel approach for operationalizing the inherently dynamic pedagogy of spelling instruction. SPIRE represents SLP instructional moves in a domain-specific language, synthesizes tailored programs in real-time from learner errors, and renders them as interactive interfaces for inquiry-based interventions. With SPIRE, spelling errors become opportunities to explore word meanings, word structures, morphological families, word origins, and grapheme-phoneme correspondences, supporting metalinguistic reasoning alongside correction. Evaluation with SLPs and learners shows alignment with professional practice and potential for integration into writing workflows.

cs.HC↗

AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robot

The social robot's open API allows users to customize open-domain interactions. However, it remains inaccessible to those without programming experience. In this work, we introduce AutoMisty, the first multi-agent collaboration framework powered by large language models (LLMs), to enable the seamless generation of executable Misty robot code from natural language instructions. AutoMisty incorporates four specialized agent modules to manage task decomposition, assignment, problem-solving, and result synthesis. Each agent incorporates a two-layer optimization mechanism, with self-reflection for iterative refinement and human-in-the-loop for better alignment with user preferences. AutoMisty ensures a transparent reasoning process, allowing users to iteratively refine tasks through natural language feedback for precise execution. To evaluate AutoMisty's effectiveness, we designed a benchmark task set spanning four levels of complexity and conducted experiments in a real Misty robot environment. Extensive evaluations demonstrate that AutoMisty not only consistently generates high-quality code but also enables precise code control, significantly outperforming direct reasoning with ChatGPT-4o and ChatGPT-o1. All code, optimized APIs, and experimental videos will be publicly released through the webpage: https://wangxiaoshawn.github.io/AutoMisty.html

cs.RO↗