Nazmus Sakib

Organizations use asynchronous AI interview systems to efficiently manage large applicant pools, enabling quick and uniform evaluations. However, concerns remain about their impact on user agency and the lack of personalization applicants experience with these systems. Although efforts have been made to humanize the interview process, users’ expectations are often unmet, especially when compared to the promises made by these systems. To examine how applicants perceive and experience these tools, particularly as they become more familiar with widely accessible AI technologies, we conducted a two-phase study. The first phase involved an analysis of 11 subreddit discussions on interview experiences with asynchronous AI interviewers, followed by a semi-structured interview study with 17 participants. Qualitative analysis revealed key issues such as mismatched expectations, amplified by organizational rhetoric and applicant expectations shaped by experiences with Large Language Models (LLMs). These factors shaped participants’ sense of agency and trust, often leading to workarounds and deceptive practices. In the follow-up study, we designed an interface with two features, response variants and feedback variants, and evaluated it across six groups (N = 180, 30 participants each) to assess whether these features support users’ sense of agency, competence, and relatedness. Our analysis suggests that even subtle design changes can enhance user autonomy and that carefully designed feedback can provide meaningful support in high-stakes interview contexts.

This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and application. Commonsense reasoning is reviewed across zero-shot learning, cultural adaptation, structured evaluation, and interactive contexts. Intent detection is examined through open-set models, generative formulations, clustering, and human-centered systems. By bridging insights from NLP and HCI, we highlight emerging trends toward more adaptive, multilingual, and context-aware models, and identify key gaps in grounding, generalization, and benchmark design.

Children go through several developmental milestones at an early age. When a child becomes slow or cannot even reach one or more of these milestones compared to other typically developing children, that condition is called Developmental Delays and Disabilities (DDDs). Children's DDDs create much parental stress, resulting in social isolation and loneliness. YouTubers make videos on DDDs to assist parents and other caregivers. We need to gain more knowledge of how these videos are created and how much they impact the parents of children with DDDs. We followed a mixed-method approach to annotate and analyze 1,532 YouTube videos on children's DDDs. Our analysis found that YouTube videos on DDDs support the community with critical informational content and provide mental and emotional strength through content related to personal experiences. Our analysis of the comments in these videos explored how YouTubers and viewers created a strong community connection through these video content. Most importantly, the interviews with the parents of children with DDDs revealed how parents find these videos relatable and critical for taking necessary actions for their children's DDDs diagnosis and follow-up treatments. We concluded by discussing platform-centric design implications for supporting parents and other caregivers of children with DDDs.

TikTok is one of the most common content-creating social media platforms for youth in the USA. In recent years, its engaging content has significantly influenced people, shaping trends, behaviors, and communication styles among its predominantly young user base. This study evaluates TikTok's impact on college and university students as they invest a lot of time creating content and engaging on TikTok besides their studies. While existing research highlights TikTok's educational benefits and adverse societal and psychological effects, our mixed-method approach provides a focused analysis of student content creators. Survey data quantifies usage patterns and their correlation with academic and mental health indicators, while interviews offer qualitative insights into personal experiences. Findings reveal that TikTok affects students' time management, mental health, academic performance, and self-perception. Although TikTok facilitates creativity and social connections, it also induces stress and distraction. This study aims to fill research gaps and propose new directions, offering practical recommendations for balancing TikTok's benefits and drawbacks for student content creators.

The "Post-Roe" refers to the period following the June 2022 Supreme Court decision to overrule Roe v. Wade, the 1973 abortion right law. Since this overturn of this law, substantial public discourse was noticed across the US and this controversial issue has trended on all news media agencies and social media platforms. Several studies have analyzed public opinion and the impact of this legal change on healthcare, economic challenges, and society. However, little work has been done to identify the shift in discussion over time. Our research analyzes YouTube and Reddit comments to perceive insights into the evolving spectrum of public opinion on abortion legislation by utilizing NLP techniques and opinion mining. By systematically categorizing comments to extract themes and employing a temporal analysis approach, we identify shifts in public sentiment across different phases of time, such as immediate reactions, peak debate, and long-term responses. Our preliminary findings show that different themes prevail at different phases and primary concerns shift over time. This study might help policymakers, activists, and social commentators understand these shifts to effectively address the evolving concerns of the public and take measures accordingly.