arXiv subjects

Nimra Ishfaq

Publications and source records attributed to Nimra Ishfaq.

4 recordsLinked to original sources

arXiv2026

"I Talked an AI Chatbot, So What's Next?" How U.S. Young Adults Imagine Responsible AI for Emotion Coping

Emotion coping is inherently relational, unfolding through interactions with friends, family, professionals, and communities. Yet AI chatbots are largely designed around a user--AI dyad. Learning from the ethics of care, we examine how AI chatbots shape the relational conditions of emotion coping. We conducted a scenario-based study with 17 U.S. young adults across four emotion coping scenarios. Participants identified eight roles through which AI could support relational conditions, alongside three challenges: flattening distinct relational conditions, discouraging reciprocity, and shifting relational labor onto users. This study contributed a relational perspective of responsible AI in emotion coping. We argue that responsible AI should respond to individuals' situated relational conditions rather than provide general-purpose support. We further identify two design principles: fostering reciprocity by supplying materials to engage with others, and strengthening emotional self-efficacy. Together, we position responsible AI as AI in the loop of human relationships.

cs.HC↗

arXiv2026

CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI

AI is increasingly used for mental health and well-being support, creating an urgent need for safer engagement, while design, evaluation, and governance take time to develop. We explore a complementary approach: helping users critically reflect on their own AI conversations. We introduce CounselReflect, a tool that translates literature-grounded counseling quality metrics into a user-facing reflection framework. Using CounselReflect as a study probe, we interviewed 21 users of AI for mental health and well-being support. Although most participants did not routinely reflect on their conversations, they articulated concrete questions they would want reflection to address. Tool-assisted reflection also revealed challenges: participants selectively sought evidence confirming existing perceptions of AI and prioritized dimensions they already valued. We argue that reflection tools should surface blind spots and scaffold more holistic examination of AI interactions. Finally, overcoming emotional barriers to revisiting tense conversations remains a major design challenge and warrants input from future work.

cs.CL↗

arXiv2026

CliniCIRCA: A Modular LLM Framework for Constructing Longitudinal Mental Health Patient Journeys from Raw EHR Narratives

In mental health care, reasoning over patient journeys is a key task for clinicians. Yet these journeys, encompassing a longitudinal progression of biological, psychological, and social events, are often spread across disparate unstructured text narratives, making temporal recovery challenging. We present CliniCIRCA, a multi-stage LLM framework for Calendar-anchored, Imprecision-aware Reconstruction of Clinical Annals. To our knowledge, CliniCIRCA is the first to temporally classify clinical events across unstructured discharge summaries without event-level timestamps. From 14,882 MIMIC-III mental health admissions, we first construct a benchmark of 52 discharge summaries on which CliniCIRCA produces 15,891 temporally tagged events. After correcting 629 errors based on a clinician-in-the-loop evaluation, we produce verified gold-standard labels. Finally, the corrected timelines drive a temporally grounded summarization stage that compresses each source 1.52 times into a date-grouped chronological record. We then scale the framework to generate 1,000 silver-standard timelines and evaluate them as training data. Compared with zero- and few-shot prompting, instruction tuning generally improves five open-weight models on event extraction, temporal tagging, and summarization across silver and clinician-verified evaluations.

cs.CL↗

arXiv2026

Context-Aware Detection and Victim-Centered Response Generation for Online Harassment in Private Messaging

Online harassment is a widespread social and public health concern, yet most computational approaches for detecting and addressing harassment focus on publicly visible social media content rather than private messaging environments. Private conversations present unique challenges because harmful interactions often unfold through context-dependent, multi-turn exchanges, while victims may lack timely support during moments of harassment. In this study, we investigate how large language models (LLMs) can support both the detection of and response to online harassment in private messaging. Using a dataset of 80,053 Instagram direct messages donated by 26 adolescents aged 12-18, including youth with suicide risk factors, we first construct a human-labeled dataset of online harassment in private conversations and develop a context-aware cascading LLM classification pipeline. The proposed pipeline outperforms baseline toxicity classifiers trained primarily on public social media data. We then develop a victim-centered response framework that produces context-sensitive and psychologically-grounded AI-generated responses to online harassment messages. Human evaluators perceived the AI-generated responses as significantly more helpful than the original participant responses (95% CI: 0.767--0.815, p < .001), particularly in terms of emotional support and de-escalation. Our findings highlight the potential of context-aware and victim-centered AI systems to provide just-in-time support during harassment in private messaging environments.

cs.SI↗