Emporia State University (Emporia State or ESU) is a public university in Emporia, Kansas, United States. Established in March 1863 as the Kansas State Normal School, Emporia State is the third-oldest public university in the state of Kansas. Emporia State is one of six public universities governed by the Kansas Board of Regents.The university offers degrees in more than 80 courses of study through four colleges and schools: the School of Business, College of Liberal Arts and Sciences, School of Library and Information Management, and The Teachers College.Emporia State's men's intercollegiate athletic teams are known as the Hornets and the women's teams are the Lady Hornets. Emporia State competes in NCAA Division II and has been a member of the Mid-America Intercollegiate Athletics Association (MIAA) since 1991. Since joining the NCAA Division II in 1991, the Lady Hornets basketball team is the only team to win an NCAA championship.
A significant gap exists in the current body of knowledge regarding the acceptance and utilization of AI tools, especially ChatGPT among students to adopt this technology for higher education. This study examines student involvement in higher education using ChatGPT in a developed and developing countries. The study examines how system quality, information quality, and facilitating conditions affect students’ ChatGPT engagement and intention to use. Quantitative data from US and Bangladeshi university students was used. Structural equation modelling (SEM) was used to evaluate the associations between student interest, behavioral intention, and privacy concerns in 452 online questionnaire responses. All construct elements have VIF values between 1.6333 and 3.085, indicating no multicollinearity with explanatory variables. The KMO and Bartlett score of .965 with the significance of .000 suggests good statistical dependability for data analysis. The results show that information, system, and supporting conditions greatly affect student ChatGPT involvement. The developed and developing environments differed in technology infrastructure and privacy issues, with Bangladeshi students expressing greater data security concerns. The report helps educators and policymakers in developed and emerging nations improve student engagement with AI technologies in higher education. To maximize benefits, institutions must handle privacy and security concerns and provide strong technological support. This research provides comparative insights into how emerging AI technologies are shaping student engagement in higher education across diverse economic settings through SmartPLS, SPSS, and Python software to offer the contextual factors of ChatGPT adoption in education.
Generative artificial intelligence (AI) tools like ChatGPT hold the capacity to tremendously impact patterns of information seeking behavior in higher education, as students rely on AI tools for a variety of tasks in their daily life. This study examines the current state of how U.S. university students perceive and use AI chatbots versus traditional online search engines and academic library resources for academic information seeking and retrieval tasks. Based on an understanding of information seeking concepts drawn from existing information behavior research and theory, an electronic survey was distributed to 236 students from diverse demographic backgrounds, measuring information source use, preference, perceived relevance, and satisfaction across AI tools, search engines, and library databases. The results of the survey suggest that, while search engines like Google remain dominant for information retrieval in higher education, generative AI tools are an increasingly significant component of students' information worlds. Younger students and international students are especially likely to use AI for academic tasks. Students who are frequent AI users also report higher satisfaction in the information supplied by AI models. These findings are indicative of a shifting ecology of information behavior where artificial intelligence serves both as a complement and a competitor to traditional information sources like search engines and university libraries, presenting important implications for information literacy instruction, academic library services, and educators navigating AI integration in higher education.
Data literacy has gained significant momentum as an essential lifelong learning competency to address challenges arising from growth in the availability and accessibility of data. Much work has been done to address the data literacy gap, including both conceptual and empirical pieces from a wide range of disciplines. This paper critically reviews the literature on data literacy to provide a deeper understanding of how it is conceptualized, framed, and assessed across different contexts. Through a systematic search of the literature from various academic fields published from 2000 to 2025, relevant works in this area were identified and evaluated. By adopting a critical review approach, we conducted a conceptual analysis of data literacy by tracing its evolution, examining current interpretations, and exploring the theoretical frameworks that underpin it, including competency‐based models, critical theory approaches, and learning‐centered perspectives. The review also discusses emerging perspectives and gaps in the conceptual understanding of data literacy, highlighting areas for future research and scholarly inquiry.
Background Large language models (LLMs) such as GPT-4 are increasingly used in scientific writing, yet little is known about how AI-generated scientific titles are perceived by researchers in terms of quality. Objective To compare the perceived alignment with the abstract content (as a surrogate for perceived accuracy), appeal, and overall preference for AI-generated versus human-written scientific titles. Methods We conducted a blinded comparative study with 21 researchers from diverse academic backgrounds. A random sample of 50 original titles was selected from 10 high-impact general internal medicine journals. For each title, an alternative version was generated using GPT-4.0. Each rater evaluated 50 pairs of titles, each pair consisting of one original and one AI-generated version, without knowing the source of the titles or the purpose of the study. For each pair, raters independently assessed both titles on perceived alignment with the abstract content and appeal, and indicated their overall preference. We analyzed alignment and appeal using Wilcoxon signed-rank tests and mixed-effects ordinal logistic regressions, preferences using McNemar’s test and mixed-effects logistic regression, and inter-rater agreement with Gwet’s AC. Results AI-generated titles received significantly higher ratings for both perceived alignment with the abstract content (mean 7.9 vs. 6.7, p-value <0.001) and appeal (mean 7.1 vs. 6.7, p-value <0.001) than human-written titles. The odds of preferring an AI-generated title were 1.7 times higher (p-value =0.001), with 61.8% of 1,049 paired judgments favoring the AI version. Inter-rater agreement was moderate to substantial (Gwet’s AC: 0.54–0.70). Conclusions AI-generated titles were rated more favorably than human-written titles within the context of this study in terms of perceived alignment with the abstract content, appeal, and preference, suggesting that LLMs may enhance the effectiveness of scientific communication. These findings support the responsible integration of AI tools in research.
