Artificial Intelligence

The primary issue of this research was to investigate the relationship between artificial intelligence (AI) and user behavior patterns on social networks, the impact of AI on voting behavior, and the connection between the use of online data analysis and students' decision-making in presidential elections. This question arises while considering the influence of AI on user behavior patterns and decision-making in presidential elections. The objective of this study was to evaluate the impact of AI variables and online data on user behavior and decision-making in various contexts. This re-search examined the study hypothesis to better understand how behavior and decision-making are shaped in the digital age and the role of modern technologies in these processes. To conduct this research, a survey method and available library resources were utilized. The study used existing sources and collected necessary data from the target population, namely students at universities in Tehran, and employed descriptive and inferential statistics for data analysis. The findings of this research indicate that demographic variables and AI play a significant role in user behavior and de-cision-making, and the use of online data analysis can influence students' decision-making in presi-dential elections. Additionally, AI can be effective in detecting user behavior patterns on social networks and influencing voting behavior. These results can be useful for policymakers, election officials, and social activists in designing effective strategies to monitor user behavior and decision-making in the digital age, contributing to a better understanding of AI's role in shaping behavior and decision-making in society.

English for Specific Purposes (ESP) education has gained prominence as a specialized field designed to address the distinct linguistic needs of learners. Although AI-enhanced instruction has been investigated within general English language learning, its implementation in ESP contexts particularly concerning vocabulary recall and retention has remained insufficiently explored. This study sought to evaluate the impact of AI-supported instruction on vocabulary recall and retention among Iranian ESP learners. Based on non-random convenience sampling, a total of 48 undergraduate students in an ESP course from a university in a southeastern province in Iran participated in the present quasi-experimental study. The participants were non-randomly assigned into two different groups: an experimental group (n=24) receiving AI-powered vocabulary instruction through Diffit, and a control group (n=24) receiving traditional print-based vocabulary instruction. Data were collected during a twelve-week intervention through AI-powered instruction of ESP vocabulary. To address the research questions, a univariate repeated measures analysis of covariate (ANCOVA) was employed. Findings revealed that AI-enhanced instruction significantly improved vocabulary recall and retention among ESP learners. The results offer robust evidence supporting the efficacy of AI-based instructional approaches in enhancing vocabulary learning outcomes within ESP settings.