arXiv subjects

Tawfiq Ammari

Publications and source records attributed to Tawfiq Ammari.

At least 19 recordsLinked to original sources

arXiv2026

How Students (Really) Use ChatGPT: Uncovering Experiences Among Undergraduate Students

We examine how undergraduate students integrate ChatGPT into everyday self-directed learning, analyzing 10,536 naturalistic messages donated by 36 students over a year. A sequential mixed-methods pipeline pairs iterative qualitative coding with zero-shot language-model annotation validated against human labels (kappa = 0.75-0.91). It yields a five-category taxonomy: Information Seeking, Content Generation, Language Use, Student-ChatGPT Interaction, and ChatGPT Response Behavior. Time-lagged linear regression and Cox proportional-hazards models link these categories to sustained engagement. Three findings stand out. First, structured tasks (theory application, code writing, job-application content, multiple-choice questions) predict continued use; ChatGPT becomes incorporated into academic rhythms when gratifications are reliably fulfilled. Second, system-issued "apologies" are the strongest positive predictor of increased engagement, outweighing every task-completion predictor. We name this mechanism "repair gratification": the reward of a breakdown acknowledged and repaired rather than a task simply completed. Third, interactional strain--prompt revision, frustration, follow-up clarification--predicts disengagement. When managing the system falls on the user without system accountability, students abandon the tool. We interpret these results through Self-Directed Learning, Uses and Gratifications Theory, and Expectancy Violations Theory, mapping predictors onto positive/negative violations and confirmations. We close with design recommendations for graduated repair patterns, mode-aware interaction, and verification affordances, and outline a participatory AI-literacy agenda for higher education.

cs.HC↗

arXiv2026

From Retrieval to Synthesis: Repair Literacy and the Domestication of Generative AI

How do students develop AI literacy through everyday practice rather than formal instruction? While normative AI literacy frameworks proliferate, empirical understanding of how students actually learn to work with generative AI remains limited. This study analyzes 10,536 ChatGPT messages from 36 undergraduates over one academic year, revealing five use genres -- academic workhorse, emotional companion, metacognitive partner, repair and negotiation, and trust calibration -- that constitute distinct configurations of student-AI learning. Drawing on domestication theory and emerging frameworks for AI literacy, we demonstrate that functional AI competence emerges through ongoing relational negotiation rather than one-time adoption. Students develop sophisticated genre portfolios, strategically matching interaction patterns to learning needs while exercising critical judgment about AI limitations. Notably, repair work during AI breakdowns produces substantial learning about AI capabilities, developing what we term "repair literacy" -- a crucial but underexplored dimension of AI competence. Our findings offer educators empirically grounded insights into how students actually learn to work with generative AI, with implications for AI literacy pedagogy, responsible AI integration, and the design of AI-enabled learning environments that support student agency.

cs.HC↗

arXiv2026

Beyond the Hype: Mapping Uncertainty and Gratification in AI Assistant Use

A new generation of AI personal assistants reached consumers in 2023-2024 amid sweeping claims about anticipatory, agentic intelligence. Wearables such as the Rabbit R1 and Humane AI Pin, and subscription services such as Ohai and Docus, promised to learn users' routines and complete tasks across digital platforms. Drawing on semi-structured interviews with nine early adopters, this article asks how users make sense of these systems when the imaginary of an autonomous "second self" meets the recalcitrance of actual devices. Extending uncertainty reduction theory, we specify three forms of uncertainty in initial encounters: functional (what can it do?), relational (how do I get it to do it?), and metaphysical (what is it to me, and what should it remember?). We find that hype continues the pre-domestication of voice assistants; that the most satisfying uses are user-curated constellations of narrow tools rather than standalone "second selves."

cs.HC↗

arXiv2026

When Sounds Hurt and Voices Aren't Heard: An Experience Report on Misophonia, Sensory Trauma, and Trauma-Informed Design

This experience report reflects on researching misophonia as someone who lives with it. Misophonia is an aversive response to everyday sounds (chewing, sniffling, pen clicking) and, for many of us, to associated visual cues (misokinesia). It is poorly recognized clinically and socially. People with misophonia are routinely disbelieved, and they live inside platform surfaces (auto-playing audio, algorithmic ASMR, normalized eating on camera) that turn the sensory environment itself into recurring distress. This report is a re-reading of a prior qualitative study of 16 semi-structured interviews with misophones, conducted in dialogue with my lived experience and my role in the soQuiet Misophonia Research Network. I extend the trauma-informed design (TID) conversation in two ways. First, TID must treat embodied, contested conditions as sources of both sensory and epistemic harm: ongoing trauma produced by the audiovisual surface and by repeated dismissal of users' accounts of their bodies. Second, the closed groups and moderated subreddits participants relied on can reproduce that dismissal when a few moderators decide whose experiences count. I close with implications for ASSETS.

cs.HC↗

arXiv2026

Interdependent Navigation and Pragmatic Disengagement: How Older Korean Immigrants Selectively Engage with Digital Technologies

Older immigrant adults face unique barriers to digital participation, often framed as skill deficits. Through a community-based study with 22 older Korean immigrants in the greater New York area, we reframe these behaviors as active strategies. We identify pragmatic disengagement, where users selectively reject emotionally taxing or linguistically risky technologies, and interdependent navigation, where digital literacy operates as a distributed, relational resource rather than an individual skill. These practices reveal that non-use is often a culturally grounded form of "data refusal" shaped by values of dignity and family obligation. We contribute to CSCW by expanding theories of non-use beyond accessibility, offering design recommendations for "relational infrastructure" that supports dignity-preserving, collaborative engagement for aging immigrant populations.

cs.CY↗

arXiv2026

Patient-Made Knowledge Networks: Long COVID Discourse, Epistemic Injustice, and Online Community Formation

Long COVID represents an unprecedented case of patient-led illness definition, emerging through Twitter in May 2020 when patients began collectively naming, documenting, and legitimizing their condition before medical institutions recognized it. This study examines 2.8 million tweets containing #LongCOVID to understand how contested illness communities construct knowledge networks and respond to epistemic injustice. Through topic modeling, reflexive thematic analysis, and exponential random graph modeling (ERGM), we identify seven discourse themes spanning symptom documentation, medical dismissal, cross-illness solidarity, and policy advocacy. Our analysis reveals a differentiated ecosystem of user roles -- including patient advocates, research coordinators, and citizen scientists -- who collectively challenge medical gatekeeping while building connections to established ME/CFS advocacy networks. ERGM results demonstrate that tie formation centers on epistemic practices: users discussing knowledge sharing and community building formed significantly more network connections than those focused on policy debates, supporting characterization of this space as an epistemic community. Long COVID patients experienced medical gaslighting patterns documented across contested illnesses, yet achieved WHO recognition within months -- contrasting sharply with decades-long struggles of similar conditions. These findings illuminate how social media affordances enable marginalized patient populations to rapidly construct alternative knowledge systems, form cross-illness coalitions, and contest traditional medical authority structures.

cs.HC↗

arXiv2026

Normalized Surveillance in the Datafied Car: How Autonomous Vehicle Users Rationalize Privacy Trade-offs

Autonomous vehicles (AVs) are characterized by pervasive datafication and surveillance through sensors like in-cabin cameras, LIDAR, and GPS. Drawing on 16 semi-structured interviews with AV drivers analyzed using constructivist grounded theory, this study examines how users make sense of vehicular surveillance within everyday datafication. Findings reveal drivers demonstrate few AV-specific privacy concerns, instead normalizing monitoring through comparisons with established digital platforms. We theorize this indifference by situating AV surveillance within the `surveillance ecology' of platform environments, arguing the datafied car functions as a mobile extension of the `leaky home' -- private spaces rendered permeable through connected technologies continuously transmitting behavioral data. The study contributes to scholarship on surveillance beliefs, datafication, and platform governance by demonstrating how users who have accepted comprehensive smartphone and smart home monitoring encounter AV datafication as just another node in normalized data extraction. We highlight how geographic restrictions on data access -- currently limiting driver log access to California -- create asymmetries that impede informed privacy deliberation, exemplifying `tertiary digital divides.' Finally, we examine how machine learning's reliance on data-intensive approaches creates structural pressure for surveillance that transcends individual manufacturer choices. We propose governance interventions to democratize social learning, including universal data access rights, binding transparency requirements, and data minimization standards to prevent race-to-the-bottom dynamics in automotive datafication.

cs.HC↗

arXiv2026

Navigating Algorithmic Opacity: Folk Theories and User Agency in Semi-Autonomous Vehicles

As semi-autonomous vehicles (AVs) become prevalent, drivers must collaborate with AI systems whose decision-making processes remain opaque. This study examines how drivers of AVs develop folk theories to interpret algorithmic behavior that contradicts their expectations. Through 16 semi-structured interviews with drivers in the United States, we investigate the explanatory frameworks drivers construct to make sense of AI decisions, the strategies they employ when systems behave unexpectedly, and their experiences with control handoffs and feedback mechanisms. Our findings reveal that drivers develop sophisticated folk theories -- often using anthropomorphic metaphors describing systems that ``see,'' ``hesitate,'' or become ``overwhelmed'' -- yet lack informational resources to validate these theories or meaningfully participate in algorithmic governance. We identify contexts where algorithmic opacity manifests acutely, including complex intersections, adverse weather, and rural environments. Current AV designs position drivers as passive data sources rather than epistemic agents, creating accountability gaps that undermine trust and safety. Drawing on critical data studies and algorithmic accountability literature, we propose a framework for participatory algorithmic governance that would provide drivers with transparency into AI decision-making and meaningful channels for contributing to system improvement. This research contributes to understanding how users navigate datafied sociotechnical systems in safety-critical contexts.

cs.HC↗

arXiv2026

Spatiotemporal Change-Points in Development Discourse: Insights from Social Media in Low-Resource Contexts

This study investigates the spatiotemporal evolution of development discourse in low-resource settings. Analyzing more than two years of geotagged X data from Zambia, we introduce a mixed-methods pipeline utilizing topic modeling, change-point detection, and qualitative coding to identify critical shifts in public debate. We identify seven recurring themes, including public health challenges and frustration with government policy, shaped by regional events and national interventions. Notably, we detect discourse changepoints linked to the COVID19 pandemic and a geothermal project, illustrating how online conversations mirror policy flashpoints. Our analysis distinguishes between the ephemeral nature of acute crises like COVID19 and the persistent, structural reorientations driven by long-term infrastructure projects. We conceptualize "durable discourse" as sustained narrative engagement with development issues. Contributing to HCI and ICTD, we examine technology's socioeconomic impact, providing practical implications and future work for direct local engagement.

cs.HC↗

arXiv2026

Remote Triggers: Misophonia, Technology Non-Use, and Design for Inclusive Digital Spaces

Misophonia, characterized by intense negative reactions to specific sounds or related visual cues, remains poorly recognized in clinical settings yet profoundly affects daily life. This study examines how individuals with misophonia experience and sometimes avoid technology that amplifies their triggers. Drawing on 16 semi-structured interviews with U.S. adults recruited from online communities, we explore how social media platforms such as TikTok and Instagram, along with remote communication tools like Zoom and Discord, shape coping strategies and patterns of non-use. Participants described frequent distress from uncontrollable audiovisual content and food-related behaviors during virtual gatherings. We propose design interventions -- including channel-specific audio-visual controls, real-time trigger detection, and shared preference tools -- to better support misophonic users and reduce exclusion in increasingly mediated social and professional contexts.

cs.HC↗

arXiv2026

Beyond the Silence: How Men Navigate Infertility Through Digital Communities and Data Sharing

Men experiencing infertility face unique challenges navigating Traditional Masculinity Ideologies that discourage emotional expression and help-seeking. This study examines how Reddit's r/maleinfertility community helps overcome these barriers through digital support networks. Using topic modeling (115 topics), network analysis (11 micro-communities), and time-lagged regression on 11,095 posts and 79,503 comments from 8,644 users, we found the community functions as a hybrid space: informal diagnostic hub, therapeutic commons, and governed institution. Medical advice dominates discourse (63.3\%), while emotional support (7.4\%) and moderation (29.2\%) create essential infrastructure. Sustained engagement correlates with actionable guidance and affiliation language, not emotional processing. Network analysis revealed structurally cohesive but topically diverse clusters without echo chamber characteristics. Cross-posters (20\% of users) who bridge r/maleinfertility and the gender-mixed r/infertility community serve as navigators and mentors, transferring knowledge between spaces. These findings inform trauma-informed design for stigmatized health communities, highlighting role-aware systems and navigation support.

cs.HC↗

arXiv2025

Crisis Messaging Journeys: Epistemic Struggles over CDC Guidance During COVID-19

This study investigates how the U.S. Centers for Disease Control and Prevention (CDC) communicated COVID-19 guidance on Twitter and how publics responded over two years of the pandemic. Drawing on 275,124 tweets mentioning or addressing @CDCgov, I combine BERTopic modeling, sentiment analysis (VADER), credibility checks (Iffy Index), change point detection (PELT), and survival analysis to trace three phases of discourse: (1) early hoax claims and testing debates, (2) lockdown and mask controversies, and (3) post-vaccine variant concerns. I introduce the concept of crisis messaging journeys to explain how archived "receipts" of prior CDC statements fueled epistemic struggles, political polarization, and sustained engagement. Findings show that skeptical, cognitively complex discourse particularly questioning institutional trust prolonged participation, while positive affirmation predicted faster disengagement. I conclude with design recommendations for annotated, cautious, and flashpoint-responsive communication strategies to bolster public trust and resilience during protracted health crises.

cs.HC↗

arXiv2025

Governance and Technological Challenge in Digital Solidarity Economies: A Case Study of a Collaborative Transportation Platform in South Korea

South Korea's City P illustrates how lofty goals of digital solidarity can falter when challenged by local governance realities. Drawing on Hansmann's ownership theory, collaborative governance concepts, and platform cooperativism, we conducted a qualitative case study involving policy documents, independent assessments, and 11 in-depth interviews with residents, officials, and technology developers. Findings reveal a marked disconnect between the initiative's stated emphasis on community co-ownership and the actual power dynamics that largely favored government agencies and external firms. Although blockchain and integrated digital tools were meant to enhance transparency and inclusivity, stakeholders--especially elderly residents--experienced confusion and mistrust. We argue that genuine collaboration in digital solidarity economies requires not only robust technical designs but also culturally resonant ownership structures, substantive inclusion of local voices, and transparent governance mechanisms. The City P case underscores the necessity of addressing heterogeneous digital capacities, aligning funding and incentives with grassroots empowerment, and mitigating performative participation to ensure meaningful and sustainable outcomes in community-based digital innovation.

cs.CY↗

arXiv2025

Intergenerational AI Literacy in Korean Immigrant Families: Interpretive Gatekeeping Meets Convenient Critical Deferment

As artificial intelligence (AI) becomes deeply integrated into family life, immigrant families must navigate unique intergenerational, linguistic, and cultural challenges. This study examines how Korean immigrant families in the United States negotiate the use of AI tools such as ChatGPT and smart assistants in their homes. Through 20 semi-structured interviews with parents and teens, we identify two key practices that shape their engagement: interpretive gatekeeping, where parents mediate their children's AI use through a lens of cultural and ethical values, and convenient critical deferment, where teens strategically postpone critical evaluation of AI for immediate academic and social utility. These intertwined practices challenge conventional, skills-based models of AI literacy, revealing it instead as a dynamic and relational practice co-constructed through ongoing family negotiation. We contribute to information science and HCI by offering a new conceptual extension for intergenerational AI literacy and providing design implications for more equitable, culturally attuned, and family-centered AI systems.

cs.HC↗

arXiv2025

From the CDC to emerging infectious disease publics: The long-now of polarizing and complex health crises

This study examines how public discourse around COVID-19 unfolded on Twitter through the lens of crisis communication and digital publics. Analyzing over 275,000 tweets involving the CDC, we identify 16 distinct discourse clusters shaped by framing, sentiment, credibility, and network dynamics. We find that CDC messaging became a flashpoint for affective and ideological polarization, with users aligning along competing frames of science vs. freedom, and public health vs. political overreach. Most clusters formed echo chambers, while a few enabled cross cutting dialogue. Publics emerged not only around ideology but also around topical and emotional stakes, reflecting shifting concerns across different stages of the pandemic. While marginalized communities raised consistent equity concerns, these narratives struggled to reshape broader discourse. Our findings highlight the importance of long-term, adaptive engagement with diverse publics and propose design interventions such as multi-agent AI assistants, to support more inclusive communication throughout extended public health crises.

cs.HC↗

arXiv2025

Retweets, Receipts, and Resistance: Discourse, Sentiment, and Credibility in Public Health Crisis Twitter

As the COVID-19 pandemic evolved, the Centers for Disease Control and Prevention (CDC) used Twitter to disseminate safety guidance and updates, reaching millions of users. This study analyzes two years of tweets from, to, and about the CDC using a mixed methods approach to examine discourse characteristics, credibility, and user engagement. We found that the CDCs communication remained largely one directional and did not foster reciprocal interaction, while discussions around COVID19 were deeply shaped by political and ideological polarization. Users frequently cited earlier CDC messages to critique new and sometimes contradictory guidance. Our findings highlight the role of sentiment, media richness, and source credibility in shaping the spread of public health messages. We propose design strategies to help the CDC tailor communications to diverse user groups and manage misinformation more effectively during high-stakes health crises.

cs.SI↗

arXiv2025

Racism, Resistance, and Reddit: How Popular Culture Sparks Online Reckonings

This study examines how Reddit users engaged with the racial narratives of Lovecraft Country and Watchmen, two television series that reimagine historical racial trauma. Drawing on narrative persuasion and multistep flow theory, we analyze 3,879 Reddit comments using topic modeling and critical discourse analysis. We identify three dynamic social roles advocates, adversaries, and adaptives and explore how users move between them in response to racial discourse. Findings reveal how Reddits pseudonymous affordances shape role fluidity, opinion leadership, and moral engagement. While adversaries minimized or rejected racism as exaggerated, advocates shared standpoint experiences and historical resources to challenge these claims. Adaptive users shifted perspectives over time, demonstrating how online publics can foster critical racial learning. This research highlights how popular culture and participatory platforms intersect in shaping collective meaning making around race and historical memory.

cs.CY↗

arXiv2025

Kintsugi-Inspired Design: Communicatively Reconstructing Identities Online After Trauma

Trauma can disrupt one's sense of self and mental well-being, leading survivors to reconstruct their identities in online communities. Drawing from 30 in-depth interviews, we present a sociotechnical process model that illustrates the mechanisms of online identity reconstruction and the pathways to integration. We introduce the concept of fractured identities, reflecting the enduring impact of trauma on one's self-concept.

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