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Large Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high‐level programming languages. Like many other university educators, those teaching programming courses would like to detect if students submit assignments generated by an LLM. To investigate grade performance and the likelihood of instructors identifying code generated by artificial intelligence (AI) tools, we compare code generated by students and ChatGPT for introductory Python homework assignments. Our research reveals mixed results on both counts, with ChatGPT performing like a mid‐range student on assignments and seasoned instructors struggling to detect AI‐generated code. This indicates that although AI‐generated results may not always be identifiable, they do not currently yield results approaching those of diligent students. We describe our methodology for selecting and evaluating the code examples, the results of our comparison, and the implications for future classes. We conclude with recommendations for how instructors of programming courses can mitigate student use of LLM tools as well as articulate the inherent value of preserving students' individual creativity in producing programming languages. [ABSTRACT FROM AUTHOR] more...

Internet technology has influenced banking systems because of its capability to enhance the performance of financial operations. Several factors influence the adoption of new technology within the financial industry, namely the customers’ Perception of Benefit, Quality, Ease of Use, Security, Self-Efficacy, and Trust. However, various cultures and geopolitical issues influence technology adoption and are worth further study. This paper focuses on the factors that influence the behaviors of Saudi Arabians and their adoption of e-payment systems. It reports new research that distinguishes and investigates the key issues that impact the population in Saudi Arabia regarding the use of electronic payment. Results show that perceived benefit, ease of use, security, trust, and self-efficacy are associated with e-payment adoption. Quality is relatively not crucial to Saudi Arabians. Furthermore, according to the stepwise regression, employment status and self-efficacy are the best predictors for using e-payment systems. [ABSTRACT FROM AUTHOR] more...

Many people find technology helpful, but seniors tend to use technology less than others. We apply constructs from the Theory of Planned Behavior to investigate the factors that drive technology use in Taiwan's rapidly growing senior population. A structural equation model was built using the attitudinal, behavioral, and use variables collected. Affect, the influence of friends, and comfort with technology were found to affect intentions and use significantly. We discuss the implications and limitations of our findings, plus offer suggestions for future research. [ABSTRACT FROM AUTHOR] more...

The idea of a "digital divide" is a common issue in less developed countries but is also found in developed countries. We surveyed 151 Japanese to examine the effect of digital divide characteristics such as regional variations, age, and gender on the perception and behavior surrounding e-payments. We find that regional difference affects Japanese perceptions, especially ease of use and trust, but not use. Also, older people are more like to use e-payment in terms of weekly frequency and amount of money spent than younger consumers. Japanese males, in general, have higher average scores in ease of use, importance of incentives, and usage than females. Moreover, males are more likely to purchase electronics, books, groceries, and services using e-payments. [ABSTRACT FROM AUTHOR] more...

This research investigates the performance of a proportion-based approach to meta-analytic moderator estimation through a series of Monte Carlo simulations. This approach is most useful when the moderating potential of a categorical variable has not been recognized in primary research and thus heterogeneous groups have been pooled together as a single sample. Alternative scenarios representing different distributions of group proportions are examined along with varying numbers of studies, subjects per study, and correlation combinations. Our results suggest that the approach is largely unbiased in its estimation of the magnitude of between-group differences and performs well with regard to statistical power and type I error. In particular, the average percentage bias of the estimated correlation for the reference group is positive and largely negligible, in the 0.5-1.8% range; the average percentage bias of the difference between correlations is also minimal, in the −0.1-1.2% range. Further analysis also suggests both biases decrease as the magnitude of the underlying difference increases, as the number of subjects in each simulated primary study increases, and as the number of simulated studies in each meta-analysis increases. The bias was most evident when the number of subjects and the number of studies were the smallest (80 and 36, respectively). A sensitivity analysis that examines its performance in scenarios down to 12 studies and 40 primary subjects is also included. This research is the first that thoroughly examines the adequacy of the proportion-based approach. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] more...

Presents reactions to the medical issues of the 'British Medical Journal' in Great Britain. Restrictions on blood donors; Safety of Acyclovir in pregnancy; Treatment of analgesics.