arXiv subjects

Prathamesh Muzumdar

Publications and source records attributed to Prathamesh Muzumdar.

14 recordsLinked to original sources

arXiv2025

Exploring Student Interactions with AI-Powered Learning Tools: A Qualitative Study Connecting Interaction Patterns to Educational Learning Theories

With the growing use of artificial intelligence in classrooms and online learning, it has become important to understand how students actually interact with AI tools and how such interactions match with traditional ways of learning. In this study, we focused on how students engage with tools like ChatGPT, Grammarly, and Khan Academy, and tried to connect their usage patterns with well known learning theories. A small experiment was carried out where undergraduate students completed different learning tasks using these tools, and later shared their thoughts through semi structured interviews. We looked at four types of interaction directive, assistive, dialogic, and empathetic and compared them with learning approaches like behaviorism, cognitivism, constructivism, and humanism. After analyzing the interviews, we found five main themes Feedback and Reinforcement, Cognitive Scaffolding, Dialogic Engagement, Personalization and Empathy, and Learning Agency. Our findings show that how useful an AI tool feels is not just about its features, but also about how students personally connect with it. By relating these experiences to existing educational theories, we have tried to build a framework that can help design better AI based learning environments. This work aims to support teachers, EdTech designers, and education researchers by giving practical suggestions grounded in real student experiences.

stat.AP↗

arXiv2025

Ethical AI in the Healthcare Sector: Investigating Key Drivers of Adoption through the Multi-Dimensional Ethical AI Adoption Model (MEAAM)

The adoption of Artificial Intelligence (AI) in the healthcare service industry presents numerous ethical challenges, yet current frameworks often fail to offer a comprehensive, empirical understanding of the multidimensional factors influencing ethical AI integration. Addressing this critical research gap, this study introduces the Multi-Dimensional Ethical AI Adoption Model (MEAAM), a novel theoretical framework that categorizes 13 critical ethical variables across four foundational dimensions of Ethical AI Fair AI, Responsible AI, Explainable AI, and Sustainable AI. These dimensions are further analyzed through three core ethical lenses: epistemic concerns (related to knowledge, transparency, and system trustworthiness), normative concerns (focused on justice, autonomy, dignity, and moral obligations), and overarching concerns (highlighting global, systemic, and long-term ethical implications). This study adopts a quantitative, cross-sectional research design using survey data collected from healthcare professionals and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM). Employing PLS-SEM, this study empirically investigates the influence of these ethical constructs on two outcomes Operational AI Adoption and Systemic AI Adoption. Results indicate that normative concerns most significantly drive operational adoption decisions, while overarching concerns predominantly shape systemic adoption strategies and governance frameworks. Epistemic concerns play a facilitative role, enhancing the impact of ethical design principles on trust and transparency in AI systems. By validating the MEAAM framework, this research advances a holistic, actionable approach to ethical AI adoption in healthcare and provides critical insights for policymakers, technologists, and healthcare administrators striving to implement ethically grounded AI solutions.

cs.AI↗

arXiv2025

Construal Level and Cognitive Reflection in Newsvendor Games: Unveiling the Influence of Individual Heterogeneity on Decision-Making

During the last decade, scholars have studied the behavior of decision-making in newsvendor settings and have identified numerous behavior patterns for deviating from normative behavior. However, there is a dearth of research which have examined the influence of individual heterogeneity on decision-making in newsvendor settings. This study examines the level of construal (Abstract and concrete) using construal level theory (CLT) on performance in newsvendor games. In addition, this study measures the cognitive reflection of individuals using cognitive reflection test (CRT) ex-ante to analyze the true impact of how people construe a problem and its impact on their decision-making.

physics.soc-ph↗

arXiv2025

Determinants of Human Development Index (HDI): A Regression Analysis of Economic and Social Indicators

This study aims to investigate the factors influencing the Human Development Index (HDI). Five variables-GDP per capita, health expenditure, education expenditure, infant mortality rate (per 1,000 live births), and average years of schooling-were analyzed to develop a regression model assessing their impact on HDI. The results indicate that GDP per capita, infant mortality rate, and average years of schooling are significant predictors of HDI. Specifically, the study finds a positive relationship between GDP per capita and average years of schooling with HDI, while infant mortality rate is negatively associated with HDI.

cs.CY↗

arXiv2025

The Dead Internet Theory: A Survey on Artificial Interactions and the Future of Social Media

The Dead Internet Theory (DIT) suggests that much of today's internet, particularly social media, is dominated by non-human activity, AI-generated content, and corporate agendas, leading to a decline in authentic human interaction. This study explores the origins, core claims, and implications of DIT, emphasizing its relevance in the context of social media platforms. The theory emerged as a response to the perceived homogenization of online spaces, highlighting issues like the proliferation of bots, algorithmically generated content, and the prioritization of engagement metrics over genuine user interaction. AI technologies play a central role in this phenomenon, as social media platforms increasingly use algorithms and machine learning to curate content, drive engagement, and maximize advertising revenue. While these tools enhance scalability and personalization, they also prioritize virality and consumption over authentic communication, contributing to the erosion of trust, the loss of content diversity, and a dehumanized internet experience. This study redefines DIT in the context of social media, proposing that the commodification of content consumption for revenue has taken precedence over meaningful human connectivity. By focusing on engagement metrics, platforms foster a sense of artificiality and disconnection, underscoring the need for human-centric approaches to revive authentic online interaction and community building.

cs.CY↗

arXiv2024

A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to Explore Topical Features in Charity Crowdfunding Campaigns

Crowdfunding in the realm of the Social Web has received substantial attention, with prior research examining various aspects of campaigns, including project objectives, durations, and influential project categories for successful fundraising. These factors are crucial for entrepreneurs seeking donor support. However, the terrain of charity crowdfunding within the Social Web remains relatively unexplored, lacking comprehension of the motivations driving donations that often lack concrete reciprocation. Distinct from conventional crowdfunding that offers tangible returns, charity crowdfunding relies on intangible rewards like tax advantages, recognition posts, or advisory roles. Such details are often embedded within campaign narratives, yet, the analysis of textual content in charity crowdfunding is limited. This study introduces an inventive text analytics framework, utilizing Latent Dirichlet Allocation (LDA) to extract latent themes from textual descriptions of charity campaigns. The study has explored four different themes, two each in campaign and incentive descriptions. Campaign description themes are focused on child and elderly health mainly the ones who are diagnosed with terminal diseases. Incentive description themes are based on tax benefits, certificates, and appreciation posts. These themes, combined with numerical parameters, predict campaign success. The study was successful in using Random Forest Classifier to predict success of the campaign using both thematic and numerical parameters. The study distinguishes thematic categories, particularly medical need-based charity and general causes, based on project and incentive descriptions. In conclusion, this research bridges the gap by showcasing topic modelling utility in uncharted charity crowdfunding domains.

cs.CL↗

arXiv2024

Navigating the Docker Ecosystem: A Comprehensive Taxonomy and Survey

The cloud computing landscape is rapidly expanding and growing in complexity. It has witnessed the emergence of Cloud Computing as a widely adopted model for efficiently processing large volumes of data by harnessing clusters of commodity computers. This evolution enables the handling of massive data through on-demand services, relying on numerous microservices with diverse dependencies. The technology of containers ensures secure storage, allowing for largescale data processing with high scalability and portability. Container technology, particularly exemplified by Docker in the last decade, plays a pivotal role in this scenario. It empowers microservices to process data swiftly, enabling developers to dynamically scale these services in real-time. This paper initiates by establishing a comprehensive taxonomy for delineating container architecture. Focusing specifically on Docker containers, we scrutinize various existing container related literature. Through this taxonomy and survey, we not only discern similarities and disparities in the architectural approaches of Docker container technology but also pinpoint areas necessitating further research.

cs.DC↗

arXiv2023

Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework

This study conducts a thorough examination of the research stream focusing on AI risks in healthcare, aiming to explore the distinct genres within this domain. A selection criterion was employed to carefully analyze 39 articles to identify three primary genres of AI risks prevalent in healthcare: clinical data risks, technical risks, and socio-ethical risks. Selection criteria was based on journal ranking and impact factor. The research seeks to provide a valuable resource for future healthcare researchers, furnishing them with a comprehensive understanding of the complex challenges posed by AI implementation in healthcare settings. By categorizing and elucidating these genres, the study aims to facilitate the development of empirical qualitative and quantitative research, fostering evidence-based approaches to address AI-related risks in healthcare effectively. This endeavor contributes to building a robust knowledge base that can inform the formulation of risk mitigation strategies, ensuring safe and efficient integration of AI technologies in healthcare practices. Thus, it is important to study AI risks in healthcare to build better and efficient AI systems and mitigate risks.

cs.CY↗

arXiv2023

Econometrics Modelling Approach to Examine the Effect of STEM Policy Changes on Asian Students Enrollment Decision in USA

Academic research has shown significant interest in international student mobility, with previous literature primarily focusing on the migration industry from a political and public policy perspective. For many countries, international student mobility plays a crucial role in bolstering their economies through financial gains and attracting skilled immigrants. While previous studies have explored the determinants of mobility and country economic policies, only a few have examined the impact of policy changes on mobility trends. In this study, the researchers investigate the influence of immigration policy changes, particularly the optional practical training (OPT) extension on STEM programs, on Asian students' preference for enrolling in STEM majors at universities. The study utilizes observational data and employs a quasi-experimental design, analysing the information using the difference-in-difference technique. The findings of the research indicate that the implementation of the STEM extension policy in 2008 has a significant effect on Asian students' decisions to enroll in a STEM major. Additionally, the study highlights the noteworthy role of individual factors such as the specific STEM major, terminal degree pursued, and gender in influencing Asian students' enrollment decisions.

econ.GN↗

arXiv2022

An empirical comparison of machine learning models for student's mental health illness assessment

Student's mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student's mental health illness.

stat.AP↗

arXiv2021

Impact of review valence and perceived uncertainty on purchase of time-constrained and discounted search goods

Increasing online shoppers have generated enormous amount of data in form of reviews (text) and sales data. Aggregate reviews in form of rating (stars) have become noticeable indicators of product quality and vendor performance to prospective consumers at first sight. Consumers subjected to product discount deadlines search for ways in which they could evaluate product and vendor service using a comprehensible benchmark. Considering the effect of time pressure on consumers, aggregate reviews, known as review valence, become a viable indicator of product quality. This study investigates how purchase decisions for new products are affected by past customer aggregate ratings when a soon-to-expire discount is being offered. We examine the role that a consumer's attitude towards review valence (RV) plays as an antecedent to that consumer's reliance on RV in a purchase decision for time-discounted search goods. Considering review credibility, diagnosticity, and effectiveness as determinants of consumer attitude in a time-constrained search and purchase environment, we follow the approach-avoidance conflict theory to examine the role of review valence and perceived uncertainty in a time-constrained environment. The data was collected through an online survey and analyzed using structural equation modelling. This study provides significant implications for practitioners as they can better understand how review valence can influence a purchase decision. Empirical analysis includes two contributions: 1. It helps to understand how consumer attitude toward review valence, when positively influenced by the determinants, can lead to reliance on review valence, further influencing purchase decision; 2. Time constrained purchase-related perceived uncertainty negatively moderates the relationship between consumer attitude and reliance on review valence.

cs.CY↗

arXiv2021

Empirical study to explore the influence of salesperson's customer orientation on customer loyalty

This study tries to examine the influence of salesperson's customer orientation on customer loyalty. Customer orientation is the approach taken by a salesperson to improve customer relationship and increase sales. Many organizations prefer sales orientation as a strategic approach towards increasing sales. Though successful in its objective, sales orientation fails to attract repetitive purchase. It has become a necessity to train frontline employees to better understand the customer needs, keeping in mind the firm's ultimate objective. This study examines the improvements customer orientation can bring to increase repurchases thus leading to customer loyalty. The findings suggest that product assortment, long lines of customers, customers' annual income, and the listening skills of salesperson were the significant antecedents of customer loyalty.

stat.AP↗

arXiv2021

Moderating effects of retail operations and hard-sell sales techniques on salesperson's interpersonal skills and customer repurchase intention

Salesperson's interpersonal skills have always played an important role in influencing various stages of customer's purchase decision. With the increase in retail outlets and merchandisers, retail operations have taken a pivotal role in influencing the salesperson's sales practices and customer's purchase decisions.This study tries to examine the influence of retail operations and hard-selling startegies on the relationship between salesperson's interpersonal skills and customer repurchase intention. Salesperson's interpersonal skills are the trained and tacit competencies that a salesperson employs to improve customer relationship and sales performance. Many organizations prefer skill training fails to attract repetitive purchases due to unavoidable extraneous factors. It has become a necessity to understand the role of extraneous factors like retail operations on the relationship between salesperson's interpersonal skills and customer repurchase intention. The findings suggest that retail operations significantly moderate the relationship between salesperson's interpersonal skills and customer repurchase intention. We also find that hard-sell sales techniques play a significant moderating role in negatively infleuncing customer repurchase intention . This study has important implications for retailers and sales managers.

stat.AP↗

arXiv2013

Effects Of Zoning On Housing Option Value

The research explores the subject of zoning effect on price value of a house in a certain designated zone. Zoning is defined as a land under planned use. This results in price value of the land. As the area of the land decreases an increase in price occurs. This research does not specify the magnitude of a certain unit by which a housing price will increase or decrease but it estimates the housing option value. This research helps the policy makers to improve long term policy decisions. This research will showcase zoning effects on housing option value for the specific zones in the city of Normal. The observations are taken for different parcels of total neighborhoods in the city of Normal. This research will use hedonic model for conducting a regression analysis on the subject. The hypothesis taken into consideration before starting the research is to prove that more 50 percent variation in the housing price is affected by the zoning characteristics. This research will try to prove the hypothesis by comparing the assessed values with the predicted values from the model.

stat.AP↗