arXiv subjects

Nikitha Donekal Chandrashekar

Publications and source records attributed to Nikitha Donekal Chandrashekar.

5 recordsLinked to original sources

arXiv2026

Understanding Student Use of Large Language Models Across Computer Science Subfields

This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aware instruction. This paper presents a cross-subfield analysis of LLM usage among 211 undergraduate students in a problem-solving course intentionally designed to support responsible and effective LLM use through structured instruction and reflection. Using post-assignment reflection data collected across seven instructional modules spanning multiple computer science subfields, we examine prompt counts, LLM role conceptualization, and verification behavior. Results show that LLM adoption varies substantially by assignment, with higher usage in algorithms and web development and lower usage in software engineering. Students predominantly treat LLMs as assistive tools rather than authoritative sources, and verification is common across all subfields, with most students using multiple strategies. Verification behavior also varies by assignment context, with testing more common in structured tasks and web search more common in open-ended tasks. These findings suggest that assignment characteristics play a central role in shaping how students interact with and evaluate LLM outputs, even under a single, consistently applied instructional design. This work contributes empirical evidence on how LLM adoption, role conceptualization, and verification behavior vary across computer science subfields, extending our prior work on structured, reflective LLM instruction to show how its effects differ by task rather than only in aggregate.

cs.CY↗

arXiv2026

EmoSay: Artificial Intelligence-Driven Text-to-Emotional-Speech System for Affective Communication in Extended Reality

While contemporary neural text-to-speech (TTS) systems have achieved high levels of intelligibility, they frequently lack the emotional nuance required for authentic affective communication. This limitation is particularly critical in Extended Reality (XR), where the absence of emotionally expressive audio can diminish user presence and spatial immersion. We present EmoSay, an Artificial Intelligence-driven Text-to-Emotional-Speech (TTES) system designed to bridge the semantic-affective gap in immersive environments. EmoSay modulates a neural synthesis pipeline using discrete emotional prompts, delivering the output through a Unity-based interface featuring high-fidelity spatialized audio. The system was evaluated through a comprehensive user study focusing on perception, engagement, and the subjective sense of empathy. Our results demonstrate that EmoSay significantly enhances the immersive experience, achieving a System Usability Scale (SUS) score of 74.76, indicating strong usability and seamless integration within the XR workflow. Subjective assessments reveal a high degree of perceived naturalness and a strong positive correlation between emotional expressiveness and user engagement. Regression analysis identifies vocal naturalness as the strongest of the tested predictors of user satisfaction, suggesting that EmoSay's affective prosody helps meet the heightened expectations for realism in immersive settings. This work contributes a scalable, affect-aware framework for inclusive XR design and demonstrates the role synthetic emotion can play in fostering human-computer rapport through voice-first interaction.

cs.HC↗

arXiv2026

Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2

Student access to Large Language Models (LLMs) is reshaping learning behaviors; at the same time students are entering the workforce where effective LLM use is becoming an expected skill. In this Experience Report we share our DURA framework (Demystify-Use-Reflect-Assess) and materials we used to restructure our CS2 course to allow the use of LLMs. We first demystified LLMs, then provided guidance on use with required attribution. We also added reflections related to LLM use at three points throughout the semester to encourage student meta-cognition around LLM use. We increased the value of proctored assessments in tandem with allowing retakes and including questions that explicitly assess skills from programming assignments. Students reported using LLMs for clarifying course concepts, debugging, understanding assignment guidelines, and determining test cases, but also still sought assistance via office hours and TAs, monitored Piazza, and reviewing course content. Students articulated thoughtful and strategic approaches to LLM use and also valued the instructional content and guidance from course staff. Student use of office hours increased slightly this semester and student perceptions that the instructor cares about them and their learning improved.

cs.CY↗

arXiv2026

Exploring Expert Perspectives on Wearable-Triggered LLM Conversational Support for Daily Stress Management

Wearable devices increasingly support stress detection, while LLMs enable conversational mental health support. However, designing systems that meaningfully connect wearable-triggered stress events with generative dialogue remains underexplored, particularly from a design perspective. We present EmBot, a functional mobile application that combines wearable-triggered stress detection with LLM-based conversational support for daily stress management. We used EmBot as a design probe in semi-structured interviews with 15 mental health experts to examine their perspectives and surface early design tensions and considerations that arise from wearable-triggered conversational support, informing the future design of such systems for daily stress management and mental health support.

cs.HC↗

arXiv2025

Demystify, Use, Reflect: Preparing students to be informed LLM-users

We transitioned our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs) in a structured, critical, and practical manner. It aims to help students develop the skills needed to engage meaningfully and responsibly with AI. The course now includes explicit instruction on how LLMs work, exposure to current tools, ethical issues, and activities that encourage student reflection on personal use of LLMs as well as the larger evolving landscape of AI-assisted programming. In class, we demonstrate the use and verification of LLM outputs, guide students in the use of LLMs as an ingredient in a larger problem-solving loop, and require students to disclose and acknowledge the nature and extent of LLM assistance. Throughout the course, we discuss risks and benefits of LLMs across CS subfields. In our first iteration of the course, we collected and analyzed data from students pre and post surveys. Student understanding of how LLMs work became more technical, and their verification and use of LLMs shifted to be more discerning and collaborative. These strategies can be used in other courses to prepare students for the AI-integrated future.

cs.CY↗