MODERN LINGUISTIC STUDIES AND ARTIFICIAL INTELLIGENCE OBJECTIVES AND OBSTACLES / THE ARABIC LANGUAGE AS A MODEL

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Abstract: Artificial Intelligence (AI) integration in education, particularly in Arabic language instruction, has become increasingly prevalent due to ongoing technological advancements. However, implementing AI in Arabic language acquisition presents challenges, especially in ensuring that this technology not only enhances linguistic proficiency but also contributes to students’ character development. This study explores the prospective applications of AI in Arabic language education with a focus on character reinforcement. The research employs a literature review method with descriptive analysis of relevant academic sources. The findings indicate that AI has the potential to facilitate personalized learning experiences, foster technology-mediated interactions, and provide adaptive feedback that promotes character development, including discipline, responsibility, and cooperation. With proper integration, AI can serve as an effective tool in supporting Arabic language education that encompasses cognitive, affective, and moral dimensions. Keywords: Artificial Intelligence, Arabic Language Learning, Character

Since the dawn of technological advancement the debate on what the term Artificial Intelligence (AI) entails has expanded to a humongous front. This discipline has evolved since the early 1960s as one of the most important and innovative areas that has influenced our existence. It has made an extremely and fast development, which promoted various types of application in different spheres of human activity including medicine, industry, agriculture, education, social communication and many others. Artificial Intelligence is a field of computer science which focuses on developing systems and applications that could mimic human intelligence. It has the ability to comprehend, reason and produce language as any human can do with their intelligence. In today’s world human intelligence plays a key role in improving the language and fine-tuning it to perfection. This research aims at assessing the contribution of AI in the area of language processing. It seeks to examine how AI technologies in general help to develop the Arabic language specifically by improving technical solutions for various processes such as machine translation, linguistic analysis and the creation of text processing tools. It also aims at identifying potential and existing difficulties of using AI technologies in enhancing and promoting the Arabic language. Arabic, in today’s globalized world, is an outstanding and shining example and history and a treasure trove of various forms of wealth among all languages. It is described by a number of peculiarities and possesses the potential for the adaptation within different civilizations and cultures. Over the last few years, there has been an increasing tendency toward applying the contemporary innovative technologies, including the AI technologies, to various spheres connected with the usage of the Arabic language.

IntroductionRadiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. MethodsA Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. Results86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. ConclusionsResponses indicate that AI will have a strong impact on reporting radiographers’ decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. Implications for practiceThis survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.

Background: Handwriting skills are the outcome of a multiplex integration of sensorimotor, cognitive, perceptual, visual, and sensory systems cooperating to complete a successful writing activity. Objective: To detect the relationship between the development of cognitive abilities and handwriting skills in primary school children. Methods: one hundred and fifty volunteer children with ages ranging from nine to eleven years, taught in the Arabic language as a primary language, right-handed children not suffering from any musculoskeletal problems or physical disabilities, with normal visual acuity even with eyeglasses participated in this study. Handwriting legibility was assessed using the handwriting legibility scale, and cognitive abilities such as verbal comprehension, perceptual reasoning, working memory, and processing speed were assessed using the Wechsler scale. Results: The results revealed that there was a weak positive non-significant correlation between handwriting skills and cognitive abilities (perceptual reasoning, verbal comprehension, working memory, and total score) while there was a weak negative non-significant correlation between handwriting skills and working processing speed. Conclusion: according to the current research results, there is a weak positive non-significant correlation between handwriting skills and the development of cognitive abilities in primary school children.

This study investigates the role of artificial intelligence (AI) in Arabic language processing, focusing on both its developmental potential and the challenges that threaten the language’s integrity and identity. Despite progress in natural language processing (NLP), the Arabic language still faces substantial barriers due to its intricate grammatical rules and semantic depth, which make it more complex to integrate into AI systems compared to other languages. In addition to technical obstacles, the application of AI raises ethical concerns—particularly around preserving the authenticity of Arabic and protecting intellectual property. Issues such as uncredited AI-generated content and reduced human involvement in language formulation may compromise the credibility and originality of information. The study aims to offer future-oriented solutions that address these ethical challenges while promoting a balanced approach between leveraging AI technologies and safeguarding the linguistic heritage of Arabic. Adopting a qualitative, literature-based methodology with a focus on ethical, linguistic, and technological analysis, the research underscores the urgent need for ethical and regulatory frameworks to guide AI implementation in this domain. The findings highlight the importance of continuous investment in research and development to support Arabic’s sustainable integration into AI-driven platforms. As a contribution, the study provides a conceptual foundation for responsible innovation, advocating for policies that protect the identity of the Arabic language while enabling its evolution within the digital age

Arabic Natural Language Processing (ANLP) is an area of Artificial Intelligence (AI) that enables computers to process and understand Arabic text and speech. It powers applications such as translation, sentiment analysis, and speech recognition, opening new horizons. However, despite the worldwide popularity of Arabic, ANLP is lagging. The Arabic language possesses unique linguistic features that complicate computational processing. In this context, our article seeks to explore these challenges and examine Arabic’s inherent characteristics in orthography, morphology, grammar, syntax, and linguistic diversity, which contribute to the complexities of its Natural Language Processing (NLP). Indeed, the main challenges are the diversity of dialects, the morphological and syntactic richness of Arabic, diglossia, and the absence of short vowels. In addition, the scarcity of Arabic resources further complicates NLP efforts. This review can serve as a guide for practitioners in the field of Arabic NLP, whether they are computer scientists or linguists. It also calls on the Arab community scientists to take steps to meet the potential challenges and increase efforts in the field to promote Arabic NLP

Abu Ya‘qub Sakkaki is regarded as one of the prominent scholars who left a profound impact on both classical and modern linguistic studies. His significance lies in the distinctive nature of his approach, which diverged from traditional methodologies. His work is marked by precise linguistic organization, a departure from conventional authorial styles, and a logical structure influenced by philosophical reasoning. Drawing upon the insights of earlier scholars most notably ‘Abd al-Qahir al-Jurjani and his theory of naẓm (systematic composition) Sakkaki’s Miftaḥ al-‘Ulum (The Key to the Sciences) rose to prominence as a foundational text in the field of Arabic linguistics. This work continues to hold a lasting place in the history of scientific linguistic inquiry in the Arabic language. Among the critical issues in which Sakkaki followed Jurjani are the concepts of meaning, the meaning of meaning, and the challenges related to reception and semantic indication.

This article provides an insightful exploration into the burgeoning field of Artificial Intelligence (AI) in the domain of Arabic language learning. With the rapid advancements in AI technologies, there has been a notable surge in interest and research focusing on its application within language education, particularly for less commonly taught languages like Arabic. This paper synthesizes the current state of AI in Arabic language learning, discussing various AI-driven approaches, tools, and applications employed in this domain. Furthermore, it highlights the challenges and opportunities encountered, and proposes future directions for harnessing AI to enhance Arabic language acquisition and proficiency. This article delves into the existing landscape of AI applications in Arabic language learning. It discusses the utilization of AI-driven techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) in developing intelligent tutoring systems, language learning platforms, and educational games tailored for Arabic learners.

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.

Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments. This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed. The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.

The thriving contemporary form of artificial intelligence (AI) called machine learning is often represented sensationally in popular media as a semi-mystical technology. Machine learning systems are frequently ascribed anthropomorphic capacities for learning, emoting and reasoning which, it is suggested, might lead to the alleviation of humanity’s woes. One critical reaction to such sensational proclamations has been to focus on the mundane reality of contemporary machine learning as mere inductive prediction based on statistical generalizations, albeit with surprisingly powerful abilities (Pasquinelli 2017). While the deflationist reaction is a necessary reply to sensationalist agitation, adequate comprehension of modern AI cannot be achieved while neglecting its material and social context. One does not have to subscribe wholeheartedly to the social construction of technology thesis1 to allow that the development and evolution of technologies are influenced by social factors. For AI, the most important aspect of the current social context is arguably capital, which increasingly dominates AI research and production. One former computer science professor describes a “giant sucking sound of [AI] academics going into industry” (Metz 2017). This paper introduces capital’s theory of AI as utility and initiates a discussion on its social consequences. First, I discuss utilities and their infrastructures and introduce a few critical thoughts on the topic. Second, I situate modern AI by way of a brief history. Third, I detail capital’s view of AI as a utility and the technical details underpinning it. Fourth, I sketch how AI as a utility frames a social problematic beyond the important issues of algorithmic bias and the automation of work. I do so by extrapolating from one consequence of AI as a utility which multiple capitalist firms predict: the curation of human subjectivities.

This study discusses the revolution of Arabic language learning in the digital era, focusing on the role of Artificial Intelligence (AI) in enhancing language competence while fostering spiritual intelligence. Using a library research method, this study reviews recent literature (2020–2023) on the application of AI in language learning and Islamic education. The findings reveal that AI provides adaptive, interactive, and data-driven learning that strengthens the four core Arabic language skills (listening, speaking, reading, and writing), including specific aspects such as nahwu, sharaf, and phonetics. Furthermore, AI has the potential to support the integration of Islamic values through features such as prayer reminders, Qur’anic verses, and the internalization of learning ethics. This research highlights that AI should not be regarded merely as a technical tool but as a medium of technological spiritualization that combines intellectual competence with the cultivation of Islamic character and morality. The contribution of this article is to provide an integrative perspective on how AI can be utilized not only to strengthen Arabic linguistic competence but also as a medium for fostering Islamic spirituality within the context of education.

A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

Standardized tests are used to test students as they progress in the formal education system. These tests are readily available and have clear evaluation procedures.Hence, it has been proposed that these tests can serve as good benchmarks for AI. In this thesis, we propose approaches for solving some common standardized teststaken by students such as reading comprehensions, elementary science exams, geometry questions in the SAT exam and mechanics questions in the AP physics exam.Answering these test problems requires deep linguistic (and sometimes visual) understanding and reasoning capabilities which is challenging for modern AI systems. In the first part of this thesis, we explore novel approaches to answer natural language comprehension tests such as reading comprehensions and elementary science tests (chapters 4, 5 and 6). These tests evaluate the system’s ability to understand text through a question-answering task. We present new latent structure models for these tasks. We posit that there is a hidden (latent) structure that explains the relation between the question, the correct answer, and the piece of text. We call this the answer-entailing structure; given the structure, the correctness of the answeris evident. Since the structure is latent, it must be inferred. We present a unified max-margin framework that learns to find these hidden structures given a corpus ofquestion-answer pairs, and uses what it learns to answer questions on novel texts. We also describe a simple but effective extension of this framework to incorporatemulti-task learning on the different subtasks that are required to perform the overall task (chapter 4), a deeper representation of language based on AMRs (chapter5) and how can we incorporate external knowledge in the answer-entailing structure (chapter 6). These advances help us obtain state-of-the-art performance on two well-known natural language comprehension benchmarks. In the second part of this thesis (chapter 7), we tackle some hard reasoning problems in the domains of math and science - geometry questions in the SAT exam and mechanics question in the AP physics exam. Solving these problems requires an ability to incorporate the rich domain knowledge as well as an ability to perform reasoning based on this knowledge. We propose a parsing to programs (P2P) approach for these problems. P2P assumes a formal representation language of the domain and domain knowledge written down as programs. This domain knowledge can be manually provided by a domain expert, or, as we show in our work, can be extracted by reading a number of textbooks in an automated way. When presentedwith a question, P2P learns a representation of the question in the formal language via a multi-modal semantic parser. Then, it uses the formal question interpretation and the domain knowledge to obtain an answer by using a probabilistic reasoner. A key bottleneck in building these models is the amount of domain-specific supervisionrequired to build them. Thus, in the final part of this thesis (chapter 8), we propose a self-training method based on curriculum learning that jointly learns to generate and answer questions. This method obtains near state-of-the-art models on a number of natural language comprehension tests with lesser supervision.