AI tools in Arab University English classrooms: Looking back and forward

• URL: http://arxiv.org/abs/2607.05403v1
• Date: Mon, 08 Jun 2026 19:44:58 GMT
• Title: AI tools in Arab University English classrooms: Looking back and forward
• Abstract summary: This paper synthesizes empirical research on AI tools used to support English as a second/foreign language (EL2) learners in Arab University classrooms (AUCs) between Jan 1st 2023 and Aug 31st 2025.<n>We utilized 3 large datasets, namely Google Scholar, Web of Science, and Scopus as the data sources.<n>Findings unveil that EL2 learners have positive attitudes towards AI for drafting, revision, and practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper aims to synthesize empirical research on AI tools used to support English as a second/foreign language (EL2) learners in Arab University classrooms (AUCs) between Jan 1st 2023 and Aug 31st 2025. We utilized 3 large datasets, namely Google Scholar, Web of Science, and Scopus as the data sources. Using PRISMA-guided searches across these well-known databases, we included only published articles. The search process results in 184 studies, but only 11 studies met the inclusion criteria. Findings unveil that EL2 learners have positive attitudes towards AI for drafting, revision, and practice. Empirical gains were most consistent for surface-level outcomes improvements in higher-order writing quality and speaking proficiency was mixed and often contingent on teacher mediation. The paper concludes by proposing a research agenda and practical guidelines for Arab universities seeking evidence-based AI integration in EL2 instruction. It also recommends scaffolded integration, teacher training, reflective tasks to reduce over-reliance on AI tools.

Related papers

• AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery [55.70879973230979]
  AutoResearchBench is a benchmark for autonomous scientific literature discovery.<n>Compared to previous benchmarks on agentic web browsing, AutoResearchBench is research-oriented.<n>Even the most powerful LLMs, despite having largely conquered general agentic web-browsing benchmarks such as BrowseComp, achieve only 9.39% accuracy on Deep Research and 9.31% IoU on Wide Research.
  arXiv  Detail & Related papers  (2026-04-28T06:05:17Z)
• Controlling Difficulty of Generated Text for AI-Assisted Language Learning [37.329743597873104]
  Large language models (LLMs) generate text at a near-native level of complexity, making them ill-suited for beginner learners.<n>We investigate whether controllable generation techniques can adapt LLM outputs to better support absolute beginners.<n>Our findings show that while prompting alone fails to control output difficulty, the use of future discriminators significantly improves output comprehensibility.
  arXiv  Detail & Related papers  (2025-06-04T15:38:21Z)
• Talking to Data: Designing Smart Assistants for Humanities Databases [41.94295877935867]
  This study introduces an LLM-based smart assistant designed to facilitate natural language communication with digital humanities data.<n>By enabling researchers to query complex databases with natural language, this tool aims to enhance accessibility and efficiency in humanities research.
  arXiv  Detail & Related papers  (2025-06-01T12:41:44Z)
• Artificial Intelligence in Sports: Insights from a Quantitative Survey among Sports Students in Germany about their Perceptions, Expectations, and Concerns regarding the Use of AI Tools [0.0]
  The manuscript presents findings from a quantitative survey conducted among sports students of all semesters in Germany.<n>Students express confidence that the proliferation of AI will not compromise their critical thinking skills.<n>Students have concerns about plagiarism, lecturer preparedness and their own skills and future skill development.
  arXiv  Detail & Related papers  (2025-02-27T05:37:53Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Generative AI in K-12 Education: The CyberScholar Initiative [1.3461796810226618]
  This paper focuses on the piloting of CyberScholar, a Generative AI assistant tool that aims to provide formative feedback on writing in K-12 contexts.<n>This study explores how students worked with CyberScholar in diverse subject areas, including English Language Arts, Social Studies, and Modern World History classes.
  arXiv  Detail & Related papers  (2025-01-28T18:23:03Z)
• Automatic answering of scientific questions using the FACTS-V1 framework: New methods in research to increase efficiency through the use of AI [0.0]
  This article presents the prototype of the FACTS-V1 (Filtering and Analysis of Content in Textual Sources) framework.<n>With the help of the application, numerous scientific papers can be automatically extracted, analyzed and interpreted from open access document servers.<n>The aim of the framework is to provide recommendations for future scientific questions based on existing data.
  arXiv  Detail & Related papers  (2024-12-01T18:55:39Z)
• Artificial Intelligence Driven Course Generation: A Case Study Using ChatGPT [0.0]
  The study aims to elaborate on using ChatGPT to create course materials. The main objective is to assess the efficiency, quality, and impact of AI-driven course generation. The study highlights the potential of AI to revolutionize educational content creation.
  arXiv  Detail & Related papers  (2024-11-02T21:59:02Z)
• A Multi-Year Grey Literature Review on AI-assisted Test Automation [46.97326049485643]
  Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations.<n>Given the prevalent usage of AI in industry, sources of truth are held in grey literature as well as the minds of professionals.<n>This study surveys grey literature to explore how AI is adopted in TA, focusing on the problems it solves, its solutions, and the available tools.
  arXiv  Detail & Related papers  (2024-08-12T15:26:36Z)
• Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis [49.1574468325115]
  The in-depth study manually analyses 133 ASA papers published in the English language between 2002 and 2020. The main findings show the different approaches used for ASA: machine learning, lexicon-based and hybrid approaches. There is a need to develop ASA tools that can be used in industry, as well as in academia, for Arabic text SA.
  arXiv  Detail & Related papers  (2024-03-04T10:37:48Z)
• Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool [0.0]
  This study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.
  arXiv  Detail & Related papers  (2024-02-09T17:20:48Z)
• Supporting Human-AI Collaboration in Auditing LLMs with LLMs [33.56822240549913]
  Large language models have been shown to be biased and behave irresponsibly. It is crucial to audit these language models rigorously. Existing auditing tools leverage either or both humans and AI to find failures.
  arXiv  Detail & Related papers  (2023-04-19T21:59:04Z)
• OpenHoldem: An Open Toolkit for Large-Scale Imperfect-Information Game Research [82.09426894653237]
  OpenHoldem is an integrated toolkit for large-scale imperfect-information game research using NLTH. OpenHoldem makes three main contributions to this research direction: 1) a standardized evaluation protocol for thoroughly evaluating different NLTH AIs, 2) three publicly available strong baselines for NLTH AI, and 3) an online testing platform with easy-to-use APIs for public NLTH AI evaluation.
  arXiv  Detail & Related papers  (2020-12-11T07:24:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.