AI

AI is developing so fast that philosophers of technology could not keep up with philosophizing it. AI promises to augment human capabilities, offering new insights and efficiencies. However, it also raises concerns about diminished autonomy and decision-making skills. Balancing AI's potential with ethical considerations is crucial to ensure it acts as a tool that enriches, rather than diminishes, human agency. In the present study, we interviewed a group of 62 tech-savvy professionals from Iran's technology sector to see how they think about the relationship between a much more powerful AI in the future and its relationship with human agency. Since these participants were -supposedly- more acquaint with AI and its capabilities, we decided interviewing them would yield important insights. After qualitatively analyzing our data, we came into five main categories of perspectives on AI and future of human agency: Augmentation and Enhancement, Displacement and Dependency, Collaboration and Partnership, Control and Ethics, and, Transformation and Transcendence. For each category, we provided examples from our interviews.

English as a Foreign Language (EFL) University students usually write in their native tongue and translate it into English using Artificial Intelligence programs. The study evaluated the quality of translations generated by AI in one hand and investigated the impact of Artificial Intelligence Generated Translation (AIGT) on EFL students in another. A human translator and an AI tool were used to translate two sample texts from English into Persian. The texts were given to 30 EFL teachers to examine the quality of AI translations. In addition, 152 students randomly divided into control or experimental groups were exposed to translated texts. Results from an independent t-test showed that there was a negligible difference between the two groups. The qualitative analysis of the interview data that involved 30 participants revealed that language teachers perceived omission, addition, syntax and punctuation errors in AIGT as adequately acceptable, despite their prevalence. However, a majority of the teachers were dissatisfied with AIGT’s accuracy in rendering idiomatic expressions. Based on the results, EFL educators should acknowledge the prevalence and usefulness of AI among students, and aim to incorporate it effectively in their teaching instead of prohibiting its use.

The advent of advanced artificial intelligence (AI) technology, represented by ChatGPT, has ushered in new possibilities in the realm of language learning and teaching. This pre-application pedagogic position study delves into the potential benefits and associated challenges of employing ChatGPT as a potent pedagogical instrument for the acquisition of legal vocabulary. The proposed pedagogy is based on the authors’ primary experiences with the use of ChatGPT for their personal and pedagogical purposes and an unsystematic application of the pedagogy in EAP classes for undergraduate students at a law school of a university in Bangladesh. Our pre-application proposed innovative ChatGPT-mediated approach to vocabulary instruction is theoretically grounded in constructivism in language teaching and (web) technology in constructivist language pedagogy. The proposed pedagogy utilizes online newspapers with legal terminology, creating an interactive learning environment that encourages active participation and covers pronunciation, meaning, and spelling. It leverages primary language word definitions, refines pronunciation, fosters bilingual comprehension, and enhances spelling proficiency, offering a comprehensive learning experience. However, the study also predicts that the implementation of ChatGPT-based language instruction may involve challenges related to educator proficiency, student participation, resource limitations, and technology requirements. This study not only underscores the value of ChatGPT in language education but also paves the way for future research and innovation in the field. 

Background: The world is rapidly becoming more intelligent, and AI is penetrating many fields, including international affairs. Based on this, we will soon witness the emergence of a new generation of embassies, namely AI embassies.Aims: This article answers the main question: "What are the prerequisites and requirements for using AI in embassies?"Methodology: This research, conducted using a qualitative approach and socio-technical theory, examines changes around embassies and operational experiences in this area and concludes that ambassadors need to align with modern developments to succeed in their diplomatic missions.Findings: The research findings indicate that a comprehensive and accurate understanding of the developments in the host country and benefiting from AI suggestions for developing relations with the government and people of the host country are among the advantages of AI embassies. Security issues and the need for skilled human resources are some of the challenges of AI embassies. Finally, the main achievement of this article is to provide an operational framework for the responsible use of AI in embassies.Conclusion: Designing and implementing an artificial intelligence strategy, ensuring data quality and security, empowering embassy staff, and continuous monitoring and evaluation are the most critical components of this proposed framework.

Artificial intelligence (AI) is changing language education through personalized and interactive practice. However, its impact on language achievement and emotional aspects of learning is unclear. Therefore, this mixed-methods study investigated the effects of Chatbot-Assisted Language Learning (CHALL) on learners’ English language achievement and sense of metavolvement—the deepest level of metacognitive engagement. In phase 1, the Sense of Metavolvement Scale (SOMS) was developed and validated. In phase 2, following a quasi-experimental design, 44 intermediate Iranian EFL learners were divided into an experimental group (n=22, using the AI chatbot Pi) and a control group (n=22, receiving conventional classroom instruction). Both groups had pre- and post-tests for language achievement and metavolvement, and the experimental group sat for semi-structured interviews post-intervention. There were significant gains in the experimental group’s in-class metavolvement and language achievement. There were also significant differences between the two groups in post-test metavolvement, while there was no significant difference in their post-test language achievement. Furthermore, interview data illustrated CHALL as a low-anxiety practice context with cultural and pragmatic limitations. Therefore, CHALL can complement but not replace conventional instruction.

Background: The integration of artificial intelligence (AI) with augmented reality (AR) has significantly revolutionized educational practices. By blending digital content with the physical environment, AR enhances student engagement, while AI-driven tools personalize learning experiences. Objective: This article aims to explore the future of AI-powered AR in education, analyzing its potential to transform traditional learning environments by improving student interaction, knowledge retention, and personalized learning. Methods: A comprehensive literature review was conducted, examining current AI-AR applications in educational settings. Additionally, case studies from early adopters of this technology in classrooms were analyzed. Interviews with educators and experts were conducted to gain insights into the challenges and opportunities associated with AI-enhanced AR. Results: The findings indicate that AI-AR systems significantly enhance student engagement, promote interactive learning experiences, and offer personalized feedback based on individual learning styles. However, challenges such as high implementation costs, technical expertise requirements, and the need for curriculum alignment were identified. Conclusion: AI-AR has the potential to reshape educational practices by fostering a more interactive, engaging, and tailored learning experience. Future efforts should focus on addressing the technical and pedagogical challenges to ensure successful adoption across various educational contexts.

Background: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management. Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost. Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed. Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection. Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Background : Security has been a major issue of discussion due to increase in the number and sophistication of Cyber threats in the modern era. Conventional approaches to threat identification might face difficulties in a number of things, namely the relevancy and the ability to process new and constantly evolving threats. Machine learning (ML) and deep learning (DL) based Approaches present AI as a potential solution to the problem of efficient threat detection.   Objective : The article aims to compare the RF, SVM, CNNs, and RNNs models’ performance, computational time, and resilience in identifying potential cyber threats, such as malware, phishing, and DoS attacks.   Methods : The proposed models were trained as well as evaluated on the NSL-KDD and CICIDS 2017 datasets. This was done based on common scheme indicators including accuracy, precision, recollection, F1 measure, detection rate of efficiency, AUC-ROC, False Alarm Rate (FAR), and the stability to adversaries. Rating of computational efficiency was defined by training time and memory consumption.   Results : The findings indicate that the CNNs gave the best accuracy (96%) and resisted perturbation better, and the RF showed good performance with little computational load. RNNs have been proved effective in sequential data analysis and SVM also performed fairly well on binary data classification although there is a problem of scalability.   Conclusion : CNNs used in AI models are the best solutions to protection from the threats in the cybersecurity space. Nevertheless, some of them still require computational optimization in order to make those beneficial in scenarios with a limited usage of computational resources. It is suggested that these findings can be used in the context of subsequent research and practical applications.

The present study aimed to find the effect of artificial intelligence (AI)-mediated speaking assessment on speaking performance and willingness to communicate (WTC) of Iraqi EFL Learners. More specifically, the study sought to find whether AI-mediated speaking assessment enhance the speaking performance (grammar, vocabulary, pronunciation, intonation, and fluency) of Intermediate Iraqi EFL learners and whether AI-mediated speaking assessment enhanced their WTC in English. In so doing, 40 intermediate Iraqi EFL learners were randomly selected and assigned into experimental and control groups, each comprising 20 learners. The experimental group participants received ten 60-minute treatment sessions with ELSA Speech Analyzer, while the control group received no treatment. The speaking pre-test of both groups was run prior to the treatment, and all participants were post-tested at the end of the study. The Willingness to Communicate in a Foreign-Language Scale was also administered to groups prior to and after the treatment. A speaking assessment rubric, including vocabulary, grammar, intonation, pronunciation, and fluency, was used to assess the speaking performance of both groups. The findings demonstrated that AI-mediated speaking assessment enhanced the grammar, vocabulary, intonation, and fluency of the experimental group. However, the two groups did not differ in terms of pronunciation. Furthermore, this assessment tool enhanced the willingness to communicate with native speakers, the willingness to communicate with non-native speakers, and the willingness to communicate in the school context of this group. In general, the speaking assessment mediated by AI significantly enhanced the speaking performance and WTC of the learners. These findings might advance the current scholarly discourse on AI within the domains of language pedagogy and assessment.

AI-assisted translation has gained increasing attention in recent years, yet its effectiveness remains underexplored. The present study sought to shed light on the role of AI (ChatGPT) in mediating translation. To this end, 46 postgraduate ESP students majoring in three sub-disciplines of politics (across three classes) were selected through convenience sampling. No outliers were identified in these classes, and each was assigned to one experimental group (AI-assisted group, N = 16; AI–Human Revised group, N = 16; Human-only Scaffolded group, N = 14). A posttest-only control group design was adopted, and each group was mediated according to its respective intervention protocol. AI was instructed to follow a graduated mediation protocol developed for the purposes of this study. The final translations were evaluated both qualitatively and quantitatively. Findings revealed that the end product of the AI-assisted group, compared with the human-involved groups, exhibited major translation deficiencies ranging from the lexico-semantic level to syntax, the syntax–semantic interface, and rhetorical patterns. Additional procedural deficiencies were also observed and reported. Furthermore, participants’ translations were assessed using a rubric, and quantitative analysis showed that both human-involved groups significantly outperformed the AI-only group.