A Framework for Developing Standardized Proficiency Assessments for Arabic as a Foreign Language Learners

A Framework for Developing Standardized Proficiency Assessments for Arabic as a Foreign Language Learners | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Framework for Developing Standardized Proficiency Assessments for Arabic as a Foreign Language Learners Bader Hudayban Alharbi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8584118/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Arabic language learners experience difficulties since there is a limited internationally accepted framework for standardised competency, which restricts the validity and comparability of tests. This study aims to close this gap by creating a thorough framework for creating standardised Arabic proficiency tests that are appropriate for both general learners and those pursuing specific goals. This study proposed a comprehensive framework for constructing standardized language proficiency assessments tailored to learners of Arabic as a foreign language. This study employed a descriptive-analytical methodology to identify the key standards necessary for developing a robust and reliable language proficiency test. The proposed standards encompass seven core aspects with 20 subtopics, resulting in 96 specific standards for Arabic language proficiency assessments for specialized purposes. Additionally, for general language learning, the framework presents 12 primary aspects branching into 41 standards, aligned with 133 performance indicators. This dual framework offers a systematic approach for test development, ensuring alignment with both theoretical foundations and practical language acquisition goals. The findings suggest that standardization is crucial for creating effective assessment tools that accurately measure the language proficiency of Arabic learners in diverse learning contexts. The framework offers educators, institutions, and policymakers clarity by offering a systematic approach to test construction. Enhancing assessment quality, improving student outcomes, and advancing the international acceptance of Arabic competence are some of its implications. Recommendations for further research and application of these standards in practical settings are provided. Language proficiency proficiency assessment Arabic as a foreign language standardized testing applied linguistics Figures Figure 1 Figure 2 Introduction Assessment is a fundamental element in the educational system, as it can be said that assessment ensures balance and integration among the various components of the educational system. This underscores the importance of educational assessment in the teaching process, particularly in language education. The new perspective, which is based on language proficiency in communication, has led to shifts in language teaching methods and the assessment of their educational outcomes. The growing interest in real-world language use in language competency exams is enhancing the use of task-based assessment and communicative language testing for more efficient evaluation techniques (Giraldo, 2020; Liao et al., 2023). Consequently, the use of performance-based language assessment has become widespread, marking a shift from traditional standardized tests to alternative language assessment methods. Language assessment is considered a principal component of language education. Language assessments are widely used to evaluate learners' linguistic proficiency, due to their systematic design and alignment with recognized proficiency frameworks like CEFR and ACTFL. These assessments ensure uniformity and reliability, accurately measuring constructs for effective educational practices, making them crucial for effective learning (Abimbola et al., 2024; Kostopoulou, 2010). Language assessments are widely used, but they have limitations in assessing communicative abilities and pragmatic competence (Cumming, 2009). Alternative methods like portfolios, role-plays, and oral proficiency interviews are being explored to better reflect students' real-world proficiency (Giraldo, 2020; Liao et al., 2023; Al-Fawzan, 2020, p. 295). The creation of a globally accepted, scientifically based standardised competence exam for Arabic as a foreign language is still a difficulty, despite tremendous progress in Arabic language instruction. There is currently no widely recognised competence exam for Arabic, despite efforts to match evaluations of the language with global standards like the Common European Framework of Reference for Languages (CEFR). The intricacy of the Arabic language, a lack of resources, and the requirement for frameworks that are culturally appropriate are some of the reasons for this disparity. These difficulties and the present situation of Arabic language testing are examined in the parts that follow (Pujiani, 2024; Sedghi et al., 2022). Arabic proficiency tests, such as ALPT, CIMA, and TAFL, lack a standard framework and requirements compared to internationally recognised exams like TOEFL or IELTS. Inconsistencies include a focus on certain skills, a lack of empirical support, and a distinct lack of progression. Despite occasional erratic evaluations, these tests can provide useful data on language skills and increase understanding of learning Arabic (Cumming, 2009; Pujiani, 2024). A general-purpose approach is suggested to address Arabic diglossia and provide a valid indicator of competency. Al-Damgh and Al-Hajri (2018) and Allam (2017) emphasize the need for clear proficiency standards in designing electronic tests for Arabic as a second language, as test developers face challenges without them. Hence, this research aims to establish general standards for developing a language proficiency test for learners of Arabic as a foreign language and to propose a framework for creating such a test that serves both general and specialized (academic/professional) purposes. This involves preparing a comprehensive list of proficiency criteria aligned with real-world communicative demands. Research Problem Several factors have highlighted the problem of this research. Despite numerous attempts to develop a test for measuring language proficiency for non-native speakers of Arabic, a globally recognized and standardized test has not been achieved. Current Arabic proficiency tests, such as ALPT, CIMA, and TAFL, have significant limitations due to inconsistent scoring rubrics, lack of construct validity, and lack of alignment with communicative language teaching approaches (Pujiani, 2024; Sedghi et al., 2022). They lack a comprehensive framework for comparability, fairness, and pedagogical relevance, unlike internationally accepted frameworks like CEFR or ACTFL. Al-Damgh and Al-Hajri (2018) further highlight the lack of well-defined frameworks for electronic evaluations of Arabic, particularly for non-native speakers. The current state of language proficiency tests for learners of Arabic as a foreign language reveals a lack of many fundamental and proper criteria for language tests, as indicated by studies such as Al-Fawzan (2020), Sharabi (2019), and Musa (2016). Moreover, it is sometimes unclear from previous research how test formulation criteria relate to language competency levels. This study makes a clear distinction between the two: "standards" correspond to the descriptors of language competency at different levels, whilst "criteria" refer to the technical and procedural rules for creating the exam. In light of the above, this study aims to provide a systematic and empirically supported framework for standardised Arabic proficiency assessment by addressing the limitations of existing tests and clarifying the functions of criteria and standards. Research Questions To address the aforementioned problem, the research seeks to answer the following questions: 1. What are the general criteria required for developing a language proficiency test for learners of Arabic as a foreign language? 2. What are the language proficiency standards for learners of Arabic as a foreign language in general purposes? 3. What is the proposed framework for constructing a language proficiency test for general purposes for learners of Arabic as a foreign language? Research Objectives The research aims to: 1. Identify the general criteria for developing a language proficiency test for learners of Arabic as a foreign language. 2. Propose a framework for creating a language proficiency test for learners of Arabic as a foreign language for general purposes. Research Terms 1. Criteria: Defined as "a set of standards and rules governing how things should be done; these are the general guidelines referred to by decision-makers" (Ma'lesh, Fatima, 2017). It is also defined as "a set of statements describing what learners should achieve in terms of knowledge, skills, and values, and consists of several performance indicators that learners are expected to demonstrate" (Al-Hudaibi et al., 2017, p. 13). In the context of this study, criteria refer to the fundamental ideas and design factors that direct the creation of a language proficiency test, such as validity, reliability, authenticity, and practicality. They stand for the test-construction standards that developers adhere to in order to guarantee the calibre of the evaluation instrument. 2. Standards: In contrast, refer to descriptions of expected language proficiency levels, such as what students should be able to perform at certain reading, writing, speaking, and listening stages. These serve as criteria for matching test material to learner competency and are derived from theoretical frameworks and international standards (such as the CEFR). 3. Frameworks: A framework is the all-encompassing structure that links and arranges the competency requirements and test building criteria. The framework used in this study (e.g., Al-Damgh & Al-Hajri, 2018) combines technical, pedagogical, and theoretical elements that direct the whole assessment design process. 4. Specifications: The technical blueprints needed to carry out the test are referred to as "Specifications." These turn theoretical blueprints into practical assessment tools and include item forms, grading rubrics, test administration strategies, and time allotments. 5. Language Proficiency Test: A language proficiency test measures an individual's ability to study a foreign language based on their previous experiences (Sayed Ali, 2016, p. 222). It is designed to measure overall language proficiency, including grammar, vocabulary, reading, and listening comprehension (Al-Omri, 2016, p. 15). It also assesses mastery of language skills acquired from various sources, such as curricula, textbooks, or teachers, based on a clear conception of expected language skills (Taimah, 2000, p. 46). Therefore, the operational definition of the criteria for a language proficiency test outlines guidelines for assessing learners' proficiency in listening, reading, speaking, writing, vocabulary, and grammar, reflecting best practices in language testing design. Significance of the study This research is significant because it aims to develop an internationally accepted Arabic language competency assessment, filling a gap in evaluation and assisting test creators and educational institutions in implementing evidence-based testing procedures using a methodical and open approach. Theoretical Framework This section of the research aims to develop two lists: the first regarding the criteria for constructing a language proficiency test, and the second regarding the proficiency standards for general purposes. This will help in proposing a framework for developing a language proficiency test for learners of Arabic as a foreign language for general purposes. The following is an overview of these two areas: Area One: Criteria for Constructing a Language Proficiency Test Several criteria are essential for developing a language proficiency test for learners of Arabic as a foreign language. According to Shatouh and Ibn al-Din (2021, pp. 527-528), a good test must include specifications such as validity, reliability, objectivity, ease of application, and discrimination. Additionally, Al-Damgh and Al-Hajri (2018, pp. 864-865) discussed the general frameworks for electronic tests of Arabic as a second language, as shown in Table 1. Table 1. Components of Electronic Language Proficiency Test Design Serial No. Component Description 1. Introduction Learner date, test title, test instruction, and type of test 2. Skill and Language Elements Covers skills and language elements addressed by the test 3. Test Stimuli Test questions and answers 4. Response Format Provide Options such as drag-and-drop, fill-in-blanks, and drop-down test 5. Time Limit Time allocated for the entire test and each question 6. Test Control Methods Test sequence, answer modification, etc. 7. Test Administration Methods Computer-based test with or without internet connectivity, or network-based test 8. Feedback and Results Feedback mechanism and test security Al-Fawzan (2020, p. 306) categorized test specifications into two types: formal specifications (test instructions and presentation) and intrinsic specifications (coverage, validity, reliability, objectivity, ease of application, and discrimination). Additionally, Allam (2017, pp. 350-351) compiled a list of criteria for electronic test quality, addressing the overall test characteristics, question properties, introduction page characteristics, content, and visual aspects. Area Two: Frameworks and Standards Shamrani (2016) identified several results, including the frameworks and standards for a standardized language test and its components: 1. Frameworks and Standards: The test adopts the Common European Framework of Reference for Languages (CEFR) and considers global testing experiences such as the University of Michigan English Test, TOEFL, and IELTS. 2. Test Objectives: The primary goal is to measure the language ability of non-native Arabic speakers. 3. Test Components: The test consists of five sections: language (grammar, morphology, vocabulary, semantics), listening and viewing, reading, speaking, and writing. Language Proficiency Standards Language proficiency tests measure a learner's skill level according to specific standards, describing listening, speaking, reading, and writing skills. Scientific efforts have been invested in developing these frameworks, as they are crucial for teaching foreign languages. As a result, several globally recognized frameworks have emerged, which are discussed below. Language Proficiency Frameworks: Global reference frameworks for language proficiency have emerged as guidelines for curriculum developers and test designers, establishing evaluation scales and descriptors for language learners at different levels. Some of the prominent frameworks include: · The Interagency Language Roundtable (ILR) Framework: This framework categorizes language proficiency into six basic levels, starting from level zero and ending at level five. Each level includes descriptors that define what a language learner can do (Elhadky, 2021, pp. 405-406). · The American Council on the Teaching of Foreign Languages (ACTFL) Proficiency Guidelines (2012): ACTFL established a classification and guidelines for language proficiency divided into five main levels, as follows (Al-Hajouri & Al-Jarrah, 2016, p. 86): 1. Novice: Divided into three sub-levels: High, Mid, and Low. 2. Intermediate: Divided into three sub-levels: High, Mid, and Low. 3. Advanced: Divided into three sub-levels: High, Mid, and Low. 4. Superior. 5. Distinguished. · The Common European Framework of Reference for Languages (CEFR): Many European countries have established specific standards through the European Language Policy Council, which led to the design of the CEFR. This framework consists of six broad levels suitable for European language learners. There is a broad, if not global, consensus on describing these levels, as illustrated in the figure below (CEFR, 2020, pp. 36-37). Each of these levels has descriptors that outline the linguistic proficiency that the learner should achieve or that their proficiency is assessed against. The study by Shtouh and Ibn El-Din (2021, pp. 527-528) identified a set of standards for each level based on the common classification (beginner - intermediate - advanced), with each main level divided into three sub-levels symbolized by (A-B-C). The description covered linguistic skills - listening, speaking, reading, and writing - at each sub-level. Shuaib (2014, pp. 33-120) addressed specific objectives for linguistic skills - listening, speaking, reading, and writing - dividing the objectives in each skill into three levels: the first level corresponds to the beginner level, the second to the intermediate level, and the third to the advanced level. The ACTFL OPI (2012) set standards for speaking skills that describe speaking levels from beginner to distinguished. This comprehensive evaluation of speaking ability is based on a set of general standards: · Functions or Tasks: The functions that the speaker can perform. · Social Contexts and Content Areas: The specific social contexts and content areas where the speaker can perform those functions. · Accuracy: The degree to which the message is understood. · Type of Oral Text or Discourse: The type of oral text or discourse the speaker can produce. The theoretical framework was used to create standards for assessing Arabic language learners and general linguistic proficiency, proposing a model for evaluating non-native Arabic speakers' linguistic proficiency. Literature Review Introduction The growing global interest in Arabic as a Foreign Language (AFL) has led to the need for standardized proficiency assessments to measure learners' language abilities across various educational contexts. This literature review explores recent developments in AFL proficiency testing, highlighting challenges and proposing future directions for standardized assessment frameworks. A thorough review of current Arabic proficiency assessments, including the Certificate of Intermediate Modern Arabic (CIMA), the Test of Arabic as a Foreign Language (TAFL), and the Arabic Language Proficiency Test (ALPT), reveals notable shortcomings in terms of validation, coverage, and standardisation (Pujiani, 2024; Sedghi et al., 2022). These assessments frequently have a limited scope, concentrate mostly on Modern Standard Arabic (MSA), and are opaque regarding their content validity, fairness, and psychometric reliability. By putting out a paradigm based on language reality and empirical norms, this study seeks to overcome these problems. Theoretical Foundations of Language Proficiency Test Recent decades have seen a substantial shift in the theoretical underpinnings of language competence assessment, driven by context-specific findings in Arabic as a Foreign Language (AFL) as well as generic frameworks in applied linguistics. Comprehensive guidelines for test design, construct definition, and validation are offered by seminal books including Davidson and Lynch's Testcraft (2002), Bachman and Palmer's Language Assessment in Practice (2010), and Bachman's Fundamental Considerations in Language Testing (1990). Internationally accepted standards that prioritise validity, reliability, fairness, and ethical concerns, such as the Standards for Educational and Psychological Testing (AERA, APA, & NCME, 1999; 2014), further reinforce these. While Davidson and Fulcher (2007) emphasise the function of frameworks like the CEFR in ensuring that test results are in line with communicative competence benchmarks, Green (2014) also emphasises the significance of connecting test design to theoretical structures and empirical validation. Recent scholarship focuses on the unique challenges of Arabic proficiency testing, arguing that proficiency assessments must differentiate between learners' proficiency in Modern Standard Arabic (MSA) and their competence in spoken dialects (Albirini & Benmamoun, 2022). However, there is a substantial validity gap for students who plan to utilise Arabic in real-world spoken situations because existing tests like TAFL and ALPT remain largely focused on MSA. Further expanding the theoretical scope, Ibrahim and Taha (2021) integrate cognitive linguistic theories into AFL testing. Their framework emphasizes the cognitive processes involved in acquiring Arabic, given its complex morphological and syntactic structures. This perspective echoes Bachman’s (1990) call to define language proficiency not merely in terms of observable performance but also in relation to the cognitive strategies learners employ. Ibrahim and Taha (2021) therefore, advocate for assessments that go beyond surface-level skills to capture the depth of mental engagement necessary for mastering Arabic. The integration of cultural competence into proficiency assessments also represents a crucial dimension. Hassan (2020) argues that Arabic proficiency cannot be adequately measured without considering cultural knowledge, given the centrality of idiomatic expressions, proverbs, and culturally embedded references. This emphasis aligns with Bachman and Palmer’s (2010) assertion that language use is inseparable from context, as well as with broader trends in applied linguistics that highlight the interdependence of language and culture. However, a review of current AFL proficiency tests reveals limited integration of cultural content, particularly in listening and speaking tasks, thereby reducing their relevance for learners preparing for authentic communicative interactions. In summary, while general frameworks for language proficiency testing provide robust guidelines for test construction and validation, their application to AFL remains limited. The diglossic nature of Arabic, its cognitive complexity, and its deep cultural embeddedness raise special issues that current assessments have not fully addressed. Thus, the development of a globally recognized, standardized Arabic proficiency test requires both adherence to established theoretical principles (e.g., as outlined by AERA, APA, & NCME, 2014; Bachman, 1990; Davidson & Lynch, 2002; Green, 2014) and careful integration of Arabic-specific sociolinguistic, cognitive, and cultural considerations. Methodological Approaches in Proficiency Assessment Saleh and Mahdi (2021) analyze technology-enhanced AFL proficiency assessments, highlighting their potential for personalized testing experiences. They argue that these assessments improve accuracy, make the process more engaging, and make the assessment process more accessible for learners. However, the lack of adaptive technology integration in popular Arabic language assessments like TAFL and ALPT limits their ability to adjust to the unique needs of each student. Younes and Al-Othman (2023) propose a novel method for proficiency tests using large-scale Arabic corpora. They developed a prototype test that incorporates diverse inputs, ensuring assessments are representative and sensitive to language variations across different contexts. However, the lack of corpus-informed design in most current standardised assessments restricts their lexical and grammatical authenticity, particularly when it comes to dialectal input. Furtehrmore, recent research by Khan and Malik (2022) introduces a psychometric framework for validating AFL proficiency tests, emphasizing reliability, validity, and fairness. This approach ensures accurate and equitable evaluations for all learners, aligning with current best practices in language testing. However, a shortage of publicly accessible psychometric validation reports for important AFL assessments like TAFL and ALPT raises concerns about the fairness and quality of their measurements. Challenges in Standardizing Arabic Proficiency Assessments Despite advancements in AFL proficiency assessments, challenges persist in standardization. Ali and Rahman (2020) identify that the lack of consensus on Arabic proficiency is a major obstacle, as the wide variation in Arabic dialects complicates the creation of a unified standard. MSA does not fully capture learners' abilities in diverse dialects.The issue is made more difficult by the fact that existing evaluations do not specify which Arabic dialect is being evaluated, which raises questions regarding interpretative validity and concept under-representation. However, El-Sayed and Zayed (2021) highlight the logistical challenges of administering standardized tests in the Arab world due to disparities in resources and teacher training, urging collaboration among Arab nations to develop a standardized framework for diverse educational settings. Additionally, in areas with limited resources, the scalability of technology-enhanced evaluations is hampered by unequal access to dependable internet and digital platforms. Moreover, Harb and Jaber (2023) discuss bias in AFL proficiency testing, highlighting how current assessments may favor certain dialects or cultural backgrounds. They propose a more inclusive approach, considering linguistic and cultural diversity, to reduce bias and ensure a more equitable assessment process. The majority of AFL assessments use an implicit MSA standard or default to one dominant dialect (such as Egyptian or Levantine) in speaking and listening parts, which reinforces regional prejudice. There is currently insufficient publicly accessible AFL test that satisfies this inclusion requirement. Applications of Standardized Proficiency Assessments Standardized proficiency assessments, as explored by Zaki and Farah (2022), can significantly improve the credibility and global recognition of international Arabic language programs, ensuring graduates meet consistent language competence levels, thus maintaining the quality and reputation of these programs. Musa and Khalil's 2023 study highlights the importance of aligning curriculum development with standardized proficiency assessments to improve language learning outcomes. They argue that these assessments provide valuable feedback for curriculum designers, enabling targeted teaching strategies. Ahmad and Ibrahim (2024) discuss the increasing use of standardized assessments in language certification and their impact on AFL learners' career prospects. They argue that these tests provide a reliable measure of language skills, enhancing employability and academic opportunities. However, these advantages are undermined if the tests themselves lack a foundation of reliable, inclusive, and well-defined competency standards, an issue that this study specifically aims to address. Diglossia and the Rationale for Focusing on Modern Standard Arabic (MSA) The diglossic structure of Arabic language, with colloquial dialects and Modern Standard Arabic (MSA) being the primary varieties, presents challenges for competency evaluation (Jabbari, 2013; R. Ageli, 2013). This study focuses on MSA for competency assessment due to its improved test reliability and pedagogical neutrality. MSA provides a consistent baseline for evaluating students from diverse backgrounds, ensuring fairness and comparability in test outcomes. This approach promotes worldwide acceptance and uniformity in assessing linguistic proficiency (Bachman & Palmer, 2010). The proposed framework addresses the issue of Arabic proficiency examinations exclusion of dialects by implementing a phased approach with MSA as the foundation and future expansions (Davidson & Lynch, 2002; Green, 2014). In summary, the study prioritizes MSA exam fairness, comparability, reliability, and Arabic diglossia while maintaining educational neutrality and psychometric soundness, considering the communicative significance of dialects. Methodology Introduction This section aims to explain the procedures followed in the research to answer its questions. Therefore, it covers the research methodology used, its tools, and the statistical processing employed. The details are as follows: Research design This research utilized the descriptive-analytical method to determine a list of standards for constructing a language proficiency test for non-native Arabic learners and another list of general language proficiency standards. This methodology aligns with achieving the research objectives by analyzing specific experiences to benefit from them when making similar decisions (Al-Assaf, 2012, p. 180). Since it enables a methodical review of expert knowledge, documented experiences, and worldwide testing models, this approach was deemed suitable. Furthermore, expert review and piloting were used to establish validity, and inter-rater agreement coefficients were used to statistically assess reliability. Research Tools To achieve the research objectives, the study employed a data collection tool consisting of the preparation of a list of standards for constructing a language proficiency test for non-native Arabic learners, along with another list of standards for general language proficiency. The procedures followed to address the research questions are outlined as follows: First: Preparing a List of Standards for Constructing a Language Proficiency Test for Non-Native Arabic Learners: A list of standards for constructing a language proficiency test for non-native Arabic learners was prepared according to the following steps: · Defining the Objective of Preparing the List: The objective was to define a list of standards for constructing a language proficiency test for non-native Arabic learners and to build the test accordingly. · Identifying the Sources for Preparing the List: The sources for preparing the list included: 1. Previous studies that addressed the construction of language proficiency tests such as: Isa's Study (2016) Al-Hajri's Study (2018) Al-Fawzan's Study (2020) Meloud and Al-Shorbini's Study (2015) 2. Books specialized in linguistic tests for non-native Arabic learners, such as: "Linguistic Tests: A Methodological and Practical Approach for Arabic Teachers for Non-Native Speakers" "Measurement and Evaluation in the Field of Evaluating Arabic for Non-Native Speakers" 3. International language proficiency tests such as: IELTS (International English Language Testing System) TOEFL (Test of English as a Foreign Language) · Preparing the Initial Version of the List : The initial version of the list included: 1. An introduction explaining the objective of preparing the list to the evaluators. 2. What is required from the evaluators to express their opinions. 3. How to record responses that match the evaluator's opinion. 4. The operational definition of language proficiency tests. The initial version of the standards list consisted of six main areas: the general framework of the test, the formal specifications of the test, the standards for writing each type of test question, the implicit standards of the test, the cultural aspect included in the test, and securing the test. These areas were divided into twenty axes and further subdivided into ninety-six standards. · Presenting the Initial Version of the List to Evaluators: The list was presented to nine evaluators specialized in applied linguistics, curriculum and pedagogy, applied statistics, and computer sciences. Purposively chosen for their areas of expertise, these evaluators included two experts in Educational Measurement and Applied Statistics (with experience in test validation and psychometrics), two computer science scholars specialising in Computer-Assisted Language Testing, and five doctorate-level specialists in Applied Linguistics and Curriculum & Pedagogy. This made sure that linguistic and technology experts were included in the review process, along with assessment specialists. · Making the Modifications Suggested by the Evaluators. Additionally, a preliminary study was conducted involving 25 advanced non-native Arabic speakers to evaluate the suitability, intelligibility, and usefulness of the suggested criteria. Prior to finalising the list, further adjustments were made based on feedback received during the preliminary phase. · Calculating the Reliability of the List: This was done using the agreement coefficient formula (Al-Mufti, 1984, pp. 10-62): Agreement Coefficient = Number of Agreements ÷ (Number of Agreements + Number of Disagreements). However, it should be made clear that the "agreement coefficient" in this context does not relate to Cronbach's alpha but rather to an inter-rater agreement indicator. The coefficient in this case measured evaluators' consensus, whereas alpha assesses internal consistency. In accordance with psychometric norms for content validation (Lawshe, 1975; Polit & Beck, 2006), where ≥80% is normally acceptable, a cut-off threshold of 85% was used. This made sure that only things that were extremely consensual were kept. Based on the results of the previous formula, one standard was deleted because its agreement coefficient value was less than 85%. The remaining standards adopted in the final list had agreement rates among the evaluators ranging from 86% to 94%. · Preparing the Final List of Standards for Constructing an Electronic Test to Determine the Language Proficiency of Non-Native Arabic Learners. Thus, to ensure methodological rigour and practical application, the final list was not only based on the opinions of the evaluators but also supported by preliminary findings and statistical validation, as shown in Table 2.: Table 2. Description of the Final Version of the Criteria List for Developing an Arabic Language Proficiency Test for Non-Native Speakers. Secondly: Preparing a List of Language Proficiency Criteria for Non-Native Arabic Learners for General Purposes. To determine the language proficiency criteria for non-native Arabic learners, a list was prepared according to the following steps: 1. Defining the Objective of Preparing the List: The objective was to establish a list of language proficiency criteria for non-native Arabic learners for general purposes and to develop the test in light of these criteria. 2. Identifying the Sources for Preparing the List: The sources were as follows: o The Common European Framework of Reference for Languages (Study... Teaching... Assessment). o Previous studies addressing language proficiency criteria, such as the study by Muhammad (2022), the study by Al-Ghafir (2021), and the study by Chitoh; Ibn Al-Deen (2021; pp. 527-528). o Specialized books on language skills for non-native Arabic learners, such as the book Language Skills: Levels, Teaching, Difficulties (Taaimah; 2009); the book Language Skills: Concept – Objectives – Teaching Methods – Evaluation (Shuaib; 2014); the book Language Tests: A Methodological Practical Approach for Teachers of Arabic to Non-Native Speakers (Sayyid Ali; 2016); the book Measurement and Evaluation in the Field of Evaluating Arabic for Non-Native Speakers (Mousa et al.; 2016); and the book Functional and Creative Writing (Abdulbari; 2009). 3. Preparing the Preliminary Version of the List: The preliminary version included: o An introduction explaining the objective of the list to the reviewers. o The specific items for the reviewers to evaluate. o How to record responses that match the reviewers' opinions. o The operational definition of language proficiency. The preliminary list of criteria included twelve main domains representing the four language skills, distributed across the main levels (Beginner – Intermediate – Advanced), from which 44 criteria emerged, accompanied by 131 indicators. - Presenting the Preliminary List to Reviewers: A total of 9 reviewers, specialized in the field of Applied Linguistics, Curriculum, and Educational Foundations, were involved. - Implementing Reviewers’ Feedback: Modifications suggested by the reviewers were made. - Calculating the Stability of the List: Stability was calculated using the agreement coefficient formula (Mufti; 1984; pp. 10-62), where the agreement coefficient = number of agreements / (number of agreements + number of disagreements). Based on the previous formula’s result, three criteria were removed because their agreement coefficient was below 85%. The remaining criteria, which were approved in the final list, had an agreement rate between 88% and 96% among the reviewers. 7. Preparing the Final Version of the Language Proficiency Criteria List for Non-Native Arabic Learners for General Purposes as illustrated in the table above. Description of the List of Language Proficiency Standards for Non-Native Arabic Learners for General Purposes (Table 3) Table 3. Description of Language Proficiency Standards and Performance Indicators for Non-Native Arabic Learners (General Purposes) The table outlines standards and indicators for evaluating non-native Arabic learners' language proficiency across various skill levels, quantifying expected proficiency levels in each field. The process of developing standards for a language proficiency test followed a systematic sequence of stages, as illustrated in Figure 2. Result and Discussion The study developed a Comprehensive Framework for Arabic Proficiency Test Development, focusing on seven categories to create a valid and trustworthy exam for learners. The framework aligns with international standards like CEFR and ACTFL, incorporating dialectal and Modern Standard Arabic abilities. It emphasizes scope and sequence matrices, cognitive progression, and combining scenario-based exercises with authentic texts. The framework also suggests section weightings, expert validation, and balanced item design for fairness. The framework reveals several significant benefits. Its inclusion of intercultural-awareness exercises improves communication ability, and its thoroughness and methodical structure push the field beyond models that only emphasise MSA. Furthermore, the use of cutting-edge security measures like biometric authentication and AES encryption shows a response to new difficulties in preserving test integrity, particularly in distant environments. Nevertheless, various types of limitations need careful consideration. Despite being recognised, dialect integration lacks precise operational standards, which increases the possibility of inconsistent results across testing scenarios. Many Arabic learners are located in under-resourced environments, where the focus on cutting-edge security measures may limit accessibility. Additionally, even if the suggested psychometric requirements are strict, they require a lot of resources and might not be scalable for smaller schools. Despite their importance in influencing Arabic teaching methods, washback effects are likewise given little consideration in the framework. The suggested skill weightings (e.g., speaking 30%, listening 40%, reading 20%, and writing 10%), on the other hand, seem inflexible and might not adequately represent the varied demands of students in academic vs. professional settings. Lastly, even while intercultural awareness exercises improve communication skills, they must be carefully tempered to prevent unforeseen political or cultural sensitivities. The framework effectively measures Arabic competency through theoretical alignment, psychometric rigor, and cultural responsiveness. However, improvements in dialect inclusion, skill-weighting models, tiered psychometric techniques, and security measures are needed for effective deployment and technical validity. The second study question was to determine the language skill requirements for general-purpose Arabic language learners. Twelve domains, 41 standards, and 133 performance indicators are covered by the finalised standards, which are divided into three levels: beginner, intermediate, and advanced. Basic MSA communication skills, including greetings, basic directions, and literacy activities like reading signs or filling out forms, are expected of learners at the beginner level. Measurable and situationally appropriate competencies are provided by these criteria. In order to help students communicate effectively in social situations, deduce meanings from context, and create coherent, organised writings, the intermediate level includes dialectal comprehension and production (such as Egyptian and Levantine). The advanced level has a strong emphasis on academic literacy and critical engagement. Students are expected to understand lengthy discourses, assess suggested meanings, use rhetorical devices, and create complex writing, including argumentative essays with appropriate references. Framework for Developing an Arabic Proficiency Test The suggested framework for assessing non-native speakers' Arabic competency strikes a compromise between the sociolinguistic characteristics of Arabic and worldwide comparability. Based on the Common European Framework of Reference for Languages (CEFR), the framework incorporates regional dialects starting at the intermediate level to guarantee fairness and authenticity. It covers six progressive levels (A1–C2) and prioritises Modern Standard Arabic (MSA) as the primary testing medium. Theoretical and Pedagogical Foundations The framework, which prioritises MSA to overcome the difficulties of Arabic diglossia, is founded on the ideas of pedagogical neutrality and dependability. Beginning with listening and speaking exercises at the intermediate level, dialectal competency is progressively included to mimic actual communication situations without disadvantaged students from a variety of backgrounds. Through the steady development of sociolinguistic awareness, this strategy guarantees that all candidates acquire functional literacy in MSA. Proficiency Progression Competencies are structured in three broad bands aligned with CEFR levels. i. Beginning (A1–A2): Students understand basic greetings, classroom directions, and basic MSA discussions; they generate courteous expressions and brief exchanges; they read short, real-world texts like menus or signs; and they finish simple written assignments like notes, forms, and brief emails. ii. Intermediate (B1–B2): At this level, students are able to deduce meaning from context and comprehend well-known MSA and common dialect subjects. They recount events, engage in prolonged social relationships, and properly adjust their register. Writing stresses well-structured and coherent writings, whereas reading entails evaluating relatively lengthy materials and summarising important concepts. iii. Advanced (C1–C2): The ability to critically assess inferred meanings in prolonged, fast-paced MSA and dialectal information, such as news broadcasts and academic lectures, is the pinnacle of proficiency. Reading concentrates on the examination of intricate texts, rhetorical devices, and hidden meanings, whereas speaking calls for convincing and educational discourse in discussions and official presentations. Argumentative and academic essays that incorporate several viewpoints and reference academic sources are examples of writing assignments. This phased approach provides horizontal integration across speaking, listening, reading, and writing as well as vertical complexity increase. Test Content and Specifications The test balances skills across modalities, as seen in Table 4 below, including speaking (15%), listening (30%), reading (30%), limited writing (15%), and extended writing (10%) all of which are comparable to the IELTS. The tasks gradually increase in difficulty to mirror the demands of real-life communication. They are scenario-based and culturally genuine. According to Bloom's taxonomy, the exam ensures thorough evaluation across domains by covering both lower-order (literal comprehension) and higher-order (critical evaluation) cognitive skills. Test Content (Table 4) Table 4. Relative Weighting of Skills and Linguistic Components in the Proposed Arabic Proficiency Test But each CEFR level's operational abilities are defined in full in Table 5 of the specifications, which also connects them to Bloom's cognitive taxonomy. This guarantees both horizontal skill integration and vertical task complexity growth. Specifications Table for Test Items (Table 5): Table 5. Test Specifications Matrix Across CEFR Levels and Cognitive Domains Skill Levels Content of Questions Question Methods Classification of Questions According to Bloom's Levels Number of Procedural Skills Relative Weight of Questions Listening Comprehension A1 4 Listening Texts (Simple Conversation - Short Text - Medium Text - Extended Text) There will be questions under each text (listening - reading) and related to it, such as: - Multiple Choice. - True or False. - Completion. 36 33% A2 B1 B2 C1 C2 Reading Comprehension 3 Reading Texts (Texts vary between simple, medium, and advanced) 36 33% Oral Production Completing the dialogue with appropriate expressions. Commenting on a picture according to specific conditions. Speaking for two minutes on a specific topic. Response will be oral 18 17% Written Production Measuring constrained writing ability (substitution – conversion, etc.) Writing on a specific topic with a specific number of words, for example, 200 words Response will be written Constrained 18 17% Creative 108 100% Item Development and Assessment Rubrics The reading and listening portions include a variety of real-world materials and multiple-choice, true/false, and completion questions. Descriptive rubrics derived from the finalised standards are used to assess speaking and writing exercises, and their validity and reliability are tested with non-native learners. Writing assignments at the B1 level, for instance, call for organised descriptions, whereas argumentative essays with citations are required at the higher levels. By offering numeric scales and tiered descriptions, rubrics improve uniformity and lessen rater bias. Scoring Mechanism The total score of the test is equal to 6, where the score is calculated based on the average of the four test sections. For a clearer explanation, refer to the scoring mechanism as outlined in the tables below. As shown in Table 6, the scoring method uses a six-band scale (0–6) that corresponds to CEFR competence levels. As indicated in a for application, each band reflects a range of correct answers, and total scores are calculated by averaging the outcomes of each individual segment. Mechanism for Calculating Test Scores for Each Section and the Overall Score (108). Table (6) Table 6. Score Conversion Scheme and CEFR Alignment for Receptive Skills Listening Comprehension Reading Comprehension Proficiency Level Correct Answers Score C2 34-36 6 31-33 5.5 C1 28-30 5 25-27 4.5 B2 22-24 4 19-21 3.5 B1 16-18 3 13-15 2.5 A2 10-12 2 7-9 1.5 A1 4-6 1 1-3 0.5 0 0 0 Oral Production Written Production Furthermore, as shown in Table 7, the average of the scores from the four skill categories is used to calculate total competence. Pilot findings show internal consistency, practical practicality, and good validity. Example Application: Student's Scores in Each Section (Total Test Score: 108) | Table (7) Table 7. Example of Overall Proficiency Calculation Based on Section Scores Skills Score Obtained by the Student Score Conversion Proficiency Level According to CEFR Proficiency Level Description Listening Comprehension 26 4.5 C1 Advance Reading Comprehension 22 4 B2 Independent Oral Production 12 4 B2 Independent Written Production 9 3 B1 Intermediate Total Score 69 15.5 ÷ 4 = 4 B2 Independent In addition to thorough pilot testing to determine difficulty indices, discrimination power, and time efficiency, the platform integrates cutting-edge security procedures, including encryption, biometric identification, and AI-supported remote proctoring. Integrity and fairness are intended to be protected by these procedures. The suggested structure has definite advantages: i. Dialectal exposure and MSA integration that respects Arabic's diglossia while maintaining equity. ii. Conformity to the CEFR, which improves comparability and worldwide acceptance. iii. Assessing productive talents using approved rubrics ensures accurate and transparent results. Therefore, the framework for assessing Arabic competence has limitations, including inconsistent implementation, inability for specialized learners, and limited measures of intercultural ability. Future iterations should improve dialect inclusion, incorporate intercultural competency descriptors, use variable skill-weighting models, and undergo extensive piloting for scalability and positive washback. Assessment and Rubrics Analytical rubrics that measure lexical range, grammatical precision, coherence, fluency, and pragmatic appropriateness are used to assess speaking and writing. To improve descriptions and guarantee inter-rater reliability, these rubrics were tested with non-native students. This method supports educational and evaluative roles by allowing the exam to deliver both summative scores and diagnostic comments. Psychometric Quality Assurance Through statistical studies for dependability and multifaceted validity tests (content, construct, predictive, and concurrent), the system incorporates strict quality assurance. Cronbach's α is used to quantify internal consistency, and item-level analysis guarantees that the difficulty and discrimination indices are adequate. Timing, clarity, and fairness are further confirmed by pilot testing. Security and Integrity In addition to in-person procedures, the test uses AI-assisted remote proctoring, biometric identification, and AES encryption to ensure integrity. By preventing misconduct and ensuring test-taker authenticity, these steps improve confidence in both domestic and foreign settings. Electronic Test Delivery: Implications and Integration The suggested framework's use of electronic test delivery is a noteworthy aspect that reflects recent changes in international language assessment standards. The design, item formats, user interface, security protocols, and accessibility features have all been influenced by the shift to digital means. Influence on Test Design and Item Formats Beyond traditional paper-based forms, the electronic platform allows for a variety of item kinds, such as drag-and-drop vocabulary completion, adaptive reading comprehension exercises, and interactive listening comprehension activities with embedded audio. Digital recording of spoken replies enables asynchronous assessment and improves inter-rater reliability. In order to maintain authenticity and reduce construct-irrelevant variation, writing assignments are typed within a regulated interface that provides automated word counts and simple text formatting without turning on external resources. User Interface and Accessibility Clear navigation, on-screen timers, and sample practice questions before each session were all features of the learner-friendly layout. To ensure inclusion, accessibility features were incorporated, including screen readers, scalable font sizes, and support for assistive devices. Because it adheres to universal design principles, applicants with a range of technological skills and physical requirements can take the test. Test Security and Integrity Strong security measures, such as role-based access restrictions, biometric or two-factor authentication, and AES encryption, were required for electronic distribution. The framework makes a distinction between in-person proctoring and AI-assisted remote proctoring (which includes screen monitoring, audio-video surveillance, and keyboard analysis) in order to combat academic dishonesty. To lessen predictability without sacrificing standardisation, test items are randomised within predetermined boundaries. Potential Challenges and Mitigation Challenges, including digital inequality, technical issues, and possible test-taker anxiety, are also brought about by the delivery of tests electronically. These were lessened through several tactics: i. Digital Equity: Support for low-bandwidth situations and offline test module availability. ii. Technical Reliability: Thorough pilot testing on many platforms and devices, along with backup plans in case of disruptions. iii. Affective Support: Practice sessions and orientation materials help acquaint test-takers with the platform and ease their nervousness. Critical Implications The framework for evaluating Arabic combines linguistic and cultural sensitivity with CEFR standards, ensuring sociolinguistic validity, authenticity, and fairness. It incorporates dialects, follows international tests, and provides detailed insights into student achievement. Thus, the digital approach enhances scalability, supports diverse item formats, and strengthens test security. Addressing dialect integration and intercultural competency elements would improve Arabic proficiency examinations' global comparability. Recommendations and Conclusion Recommendations: The study suggests enhancing Arabic language proficiency assessments by creating a general-purpose test matrix, developing academic standards, and creating descriptive rubrics for procedural skills, particularly speaking and writing, to ensure consistency and clarity in evaluation. Future Directions and Recommendations: The study suggests two future research directions for Arabic language proficiency tests: a general-purpose test for non-native speakers and an academic test for academic success. While progress has been made in developing standardized assessments, challenges remain. As highlighted by Albirini and Benmamoun (2022) and Ibrahim and Taha (2021), theoretical frameworks need refinement to account for cognitive and sociolinguistic complexities of the Arabic language. Practical assessment tools should be integrated for more accurate measures. As demonstrated by Saleh and Mahdi (2021), technology-enhanced assessments offer potential for personalized and adaptive testing environments, making them more accessible to a wider range of learners. Furthermore, Future iterations will investigate the incorporation of integrated and interactive tasks (e.g., reading-to-writing or listening-to-speaking activities), which are increasingly recognised for their ability to capture authentic language use and communicative competence (Giraldo, 2020). This phased approach strikes a balance between the pedagogical benefits of holistic language assessment and the need for reliability and comparability. The current framework emphasises the traditional four-skill model for standardisation and benchmarking purposes. Another area that warrants further investigation is the challenge of bias in proficiency testing. As Harb and Jaber (2023) have shown, existing assessments may inadvertently favour certain linguistic or cultural groups. Future research should focus on developing more inclusive assessment tools that account for the diverse linguistic backgrounds of AFL learners. This could involve the creation of test items that are representative of a broader range of dialects and cultural contexts, thereby reducing bias and ensuring a fairer assessment process. Finally, greater collaboration among researchers, educators, and policymakers is essential to overcome the challenges identified in this review. Standardizing proficiency assessments for AFL learners is a complex but necessary task that requires a coordinated effort from all stakeholders involved. By working together, the academic and professional communities can develop a unified framework for proficiency assessment that is both theoretically sound and practically effective. Additionally, the review highlights the need for further investigation into the issue of bias in proficiency testing, suggesting that more inclusive assessment tools should be developed to account for the diverse linguistic backgrounds of AFL learners. Collaboration among researchers, educators, and policymakers is also crucial for a unified framework for proficiency assessment. Abbreviations AFL Arabic as a Foreign Language CEFR Common European Framework of Reference for Languages ACTFL American Council on the Teaching of Foreign Languages MSA Modern Standard Arabic OPI Oral Proficiency Interview ILR Interagency Language Roundtable AES Advanced Encryption Standard Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding Not applicable. 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Journal of Language Studies, 40 (3), 151–173. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviews received at journal 17 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 26 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8584118","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591021515,"identity":"603c47d3-7816-4f93-afb6-50b611626194","order_by":0,"name":"Bader Hudayban Alharbi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwkGxsNAOoGNvQ0swNhAhBYGiBaeY6RqYZBII1KLuXTzg8MFNffy+CSfJX7mYbCR3XCA/eEHfFos5xwzODzjWHExm3TaYWkehjTjDQd4jCXwOuxGgsFhHraExDbp9AaglsOJQC0MBLSkfzjM8w+oRfJ4828ehv9ALeyPf+DXkmNwmLcNqEWC7RjQlgNALQxmeG2xnJFTcJi3L6GYjSctzXKOQbLxzMM8Zhb4tJhLpG98zPMtIU++/ZjxjTcVdrJ9x9sf38CnBd2dQMxMgvpRMApGwSgYBdgBAHSeS6ZY5Z6VAAAAAElFTkSuQmCC","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Bader","middleName":"Hudayban","lastName":"Alharbi","suffix":""}],"badges":[],"createdAt":"2026-01-12 17:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8584118/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8584118/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102760632,"identity":"558aabc2-23ff-4145-949d-2cffb391c28a","added_by":"auto","created_at":"2026-02-16 10:30:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94722,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Proficiency Levels According to the CEFR\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8584118/v1/63b012ae9d0bfa6389d8f6b0.jpeg"},{"id":102962386,"identity":"0a3f59c7-41f2-4673-9abc-3e5fc6105a1b","added_by":"auto","created_at":"2026-02-19 04:07:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":344495,"visible":true,"origin":"","legend":"\u003cp\u003eSteps for Developing Standards for a Language Proficiency Test for Non-Native Arabic Learners\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8584118/v1/6eb78c8171a8378e5281987c.png"},{"id":102965891,"identity":"eb5b4ab0-42de-4e6e-ad45-b52ba7b097ac","added_by":"auto","created_at":"2026-02-19 04:33:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1863469,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8584118/v1/c30ba2bf-0721-46a9-b1a1-f740493e39e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Framework for Developing Standardized Proficiency Assessments for Arabic as a Foreign Language Learners","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAssessment is a fundamental element in the educational system, as it can be said that assessment ensures balance and integration among the various components of the educational system. This underscores the importance of educational assessment in the teaching process, particularly in language education. The new perspective, which is based on language proficiency in communication, has led to shifts in language teaching methods and the assessment of their educational outcomes. The growing interest in real-world language use in language competency exams is enhancing the use of task-based assessment and communicative language testing for more efficient evaluation techniques (Giraldo, 2020; Liao et al., 2023). Consequently, the use of performance-based language assessment has become widespread, marking a shift from traditional standardized tests to alternative language assessment methods. Language assessment is considered a principal component of language education. Language assessments are widely used to evaluate learners\u0026apos; linguistic proficiency, due to their systematic design and alignment with recognized proficiency frameworks like CEFR and ACTFL. These assessments ensure uniformity and reliability, accurately measuring constructs for effective educational practices, making them crucial for effective learning (Abimbola et al., 2024; Kostopoulou, 2010). Language assessments are widely used, but they have limitations in assessing communicative abilities and pragmatic competence (Cumming, 2009). Alternative methods like portfolios, role-plays, and oral proficiency interviews are being explored to better reflect students\u0026apos; real-world proficiency (Giraldo, 2020; Liao et al., 2023; Al-Fawzan, 2020, p. 295).\u003c/p\u003e\n\u003cp\u003eThe creation of a globally accepted, scientifically based standardised competence exam for Arabic as a foreign language is still a difficulty, despite tremendous progress in Arabic language instruction. There is currently no widely recognised competence exam for Arabic, despite efforts to match evaluations of the language with global standards like the Common European Framework of Reference for Languages (CEFR). The intricacy of the Arabic language, a lack of resources, and the requirement for frameworks that are culturally appropriate are some of the reasons for this disparity. These difficulties and the present situation of Arabic language testing are examined in the parts that follow (Pujiani, 2024; Sedghi et al., 2022).\u003c/p\u003e\n\u003cp\u003eArabic proficiency tests, such as ALPT, CIMA, and TAFL, lack a standard framework and requirements compared to internationally recognised exams like TOEFL or IELTS. Inconsistencies include a focus on certain skills, a lack of empirical support, and a distinct lack of progression. Despite occasional erratic evaluations, these tests can provide useful data on language skills and increase understanding of learning Arabic (Cumming, 2009; Pujiani, 2024). A general-purpose approach is suggested to address Arabic diglossia and provide a valid indicator of competency.\u0026nbsp;Al-Damgh and Al-Hajri (2018) and Allam (2017) emphasize the need for clear proficiency standards in designing electronic tests for Arabic as a second language, as test developers face challenges without them.\u003c/p\u003e\n\u003cp\u003eHence, this research aims to establish general standards for developing a language proficiency test for learners of Arabic as a foreign language and to propose a framework for creating such a test that serves both general and specialized (academic/professional) purposes. This involves preparing a comprehensive list of proficiency criteria aligned with real-world communicative demands.\u003c/p\u003e"},{"header":"Research Problem","content":"\u003cp\u003eSeveral factors have highlighted the problem of this research. Despite numerous attempts to develop a test for measuring language proficiency for non-native speakers of Arabic, a globally recognized and standardized test has not been achieved. Current Arabic proficiency tests, such as ALPT, CIMA, and TAFL, have significant limitations due to inconsistent scoring rubrics, lack of construct validity, and lack of alignment with communicative language teaching approaches (Pujiani, 2024; Sedghi et al., 2022). They lack a comprehensive framework for comparability, fairness, and pedagogical relevance, unlike internationally accepted frameworks like CEFR or ACTFL. Al-Damgh and Al-Hajri (2018) further highlight the lack of well-defined frameworks for electronic evaluations of Arabic, particularly for non-native speakers.\u003c/p\u003e\n\u003cp\u003eThe current state of language proficiency tests for learners of Arabic as a foreign language reveals a lack of many fundamental and proper criteria for language tests, as indicated by studies such as Al-Fawzan (2020), Sharabi (2019), and Musa (2016). Moreover, it is sometimes unclear from previous research how test formulation criteria relate to language competency levels. This study makes a clear distinction between the two: \u0026quot;standards\u0026quot; correspond to the descriptors of language competency at different levels, whilst \u0026quot;criteria\u0026quot; refer to the technical and procedural rules for creating the exam.\u003c/p\u003e\n\u003cp\u003eIn light of the above,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethis study aims to provide a systematic and empirically supported framework for standardised Arabic proficiency assessment by addressing the limitations of existing tests and clarifying the functions of criteria and standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the aforementioned problem, the research seeks to answer the following questions:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; What are the general criteria required for developing a language proficiency test for learners of Arabic as a foreign language?\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; What are the language proficiency standards for learners of Arabic as a foreign language in general purposes?\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; What is the proposed framework for constructing a language proficiency test for general purposes for learners of Arabic as a foreign language?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research aims to:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Identify the general criteria for developing a language proficiency test for learners of Arabic as a foreign language.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Propose a framework for creating a language proficiency test for learners of Arabic as a foreign language for general purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Terms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eCriteria:\u003c/strong\u003e Defined as \u0026quot;a set of standards and rules governing how things should be done; these are the general guidelines referred to by decision-makers\u0026quot; (Ma\u0026apos;lesh, Fatima, 2017). It is also defined as \u0026quot;a set of statements describing what learners should achieve in terms of knowledge, skills, and values, and consists of several performance indicators that learners are expected to demonstrate\u0026quot; (Al-Hudaibi et al., 2017, p. 13). In the context of this study, criteria refer to the fundamental ideas and design factors that direct the creation of a language proficiency test, such as validity, reliability, authenticity, and practicality. They stand for the test-construction standards that developers adhere to in order to guarantee the calibre of the evaluation instrument.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Standards: In contrast, refer to descriptions of expected language proficiency levels, such as what students should be able to perform at certain reading, writing, speaking, and listening stages. These serve as criteria for matching test material to learner competency and are derived from theoretical frameworks and international standards (such as the CEFR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; \u0026nbsp;Frameworks: A framework is the all-encompassing structure that links and arranges the competency requirements and test building criteria. The framework used in this study (e.g., Al-Damgh \u0026amp; Al-Hajri, 2018) combines technical, pedagogical, and theoretical elements that direct the whole assessment design process.\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; Specifications: The technical blueprints needed to carry out the test are referred to as \u0026quot;Specifications.\u0026quot; These turn theoretical blueprints into practical assessment tools and include item forms, grading rubrics, test administration strategies, and time allotments.\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eLanguage Proficiency Test:\u003c/strong\u003e A language proficiency test measures an individual\u0026apos;s ability to study a foreign language based on their previous experiences (Sayed Ali, 2016, p. 222). It is designed to measure overall language proficiency, including grammar, vocabulary, reading, and listening comprehension (Al-Omri, 2016, p. 15). It also assesses mastery of language skills acquired from various sources, such as curricula, textbooks, or teachers, based on a clear conception of expected language skills (Taimah, 2000, p. 46).\u003c/p\u003e\n\u003cp\u003eTherefore, the operational definition of the criteria for a language proficiency test outlines guidelines for assessing learners\u0026apos; proficiency in listening, reading, speaking, writing, vocabulary, and grammar, reflecting best practices in language testing design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is significant because it aims to develop an internationally accepted Arabic language competency assessment, filling a gap in evaluation and assisting test creators and educational institutions in implementing evidence-based testing procedures using a methodical and open approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section of the research aims to develop two lists: the first regarding the criteria for constructing a language proficiency test, and the second regarding the proficiency standards for general purposes. This will help in proposing a framework for developing a language proficiency test for learners of Arabic as a foreign language for general purposes. The following is an overview of these two areas:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArea One: Criteria for Constructing a Language Proficiency Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eSeveral criteria are essential for developing a language proficiency test for learners of Arabic as a foreign language. According to Shatouh and Ibn al-Din (2021, pp. 527-528), a good test must include specifications such as validity, reliability, objectivity, ease of application, and discrimination. Additionally, Al-Damgh and Al-Hajri (2018, pp. 864-865) discussed the general frameworks for electronic tests of Arabic as a second language, as shown in Table 1.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eTable 1. Components of Electronic Language Proficiency Test Design\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSerial No.\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eComponent\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eDescription\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eIntroduction\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eLearner date, test title, test instruction, and type of test\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eSkill and Language Elements\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eCovers skills and language elements addressed by the test\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTest Stimuli\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTest questions and answers\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eResponse Format\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eProvide Options such as drag-and-drop, fill-in-blanks, and drop-down test\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTime Limit\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTime allocated for the entire test and each question\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTest Control Methods\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTest sequence, answer modification, etc.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTest Administration Methods\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eComputer-based test with or without internet connectivity, or network-based test\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eFeedback and Results\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 363px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eFeedback mechanism and test security\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAl-Fawzan (2020, p. 306) categorized test specifications into two types: formal specifications (test instructions and presentation) and intrinsic specifications (coverage, validity, reliability, objectivity, ease of application, and discrimination). Additionally, Allam (2017, pp. 350-351) compiled a list of criteria for electronic test quality, addressing the overall test characteristics, question properties, introduction page characteristics, content, and visual aspects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArea Two: Frameworks and Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShamrani (2016) identified several results, including the frameworks and standards for a standardized language test and its components:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Frameworks and Standards: The test adopts the Common European Framework of Reference for Languages (CEFR) and considers global testing experiences such as the University of Michigan English Test, TOEFL, and IELTS.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Test Objectives: The primary goal is to measure the language ability of non-native Arabic speakers.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Test Components: The test consists of five sections: language (grammar, morphology, vocabulary, semantics), listening and viewing, reading, speaking, and writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLanguage Proficiency Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLanguage proficiency tests measure a learner\u0026apos;s skill level according to specific standards, describing listening, speaking, reading, and writing skills. Scientific efforts have been invested in developing these frameworks, as they are crucial for teaching foreign languages. As a result, several globally recognized frameworks have emerged, which are discussed below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLanguage Proficiency Frameworks:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobal reference frameworks for language proficiency have emerged as guidelines for curriculum developers and test designers, establishing evaluation scales and descriptors for language learners at different levels. Some of the prominent frameworks include:\u003c/p\u003e\n\u003cp\u003e\u0026middot; The Interagency Language Roundtable (ILR) Framework: This framework categorizes language proficiency into six basic levels, starting from level zero and ending at level five. Each level includes descriptors that define what a language learner can do (Elhadky, 2021, pp. 405-406).\u003c/p\u003e\n\u003cp\u003e\u0026middot; The American Council on the Teaching of Foreign Languages (ACTFL) Proficiency Guidelines (2012): ACTFL established a classification and guidelines for language proficiency divided into five main levels, as follows (Al-Hajouri \u0026amp; Al-Jarrah, 2016, p. 86):\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Novice: Divided into three sub-levels: High, Mid, and Low.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Intermediate: Divided into three sub-levels: High, Mid, and Low.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Advanced: Divided into three sub-levels: High, Mid, and Low.\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; Superior.\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; Distinguished.\u003c/p\u003e\n\u003cp\u003e\u0026middot; The Common European Framework of Reference for Languages (CEFR): Many European countries have established specific standards through the European Language Policy Council, which led to the design of the CEFR. This framework consists of six broad levels suitable for European language learners. There is a broad, if not global, consensus on describing these levels, as illustrated in the figure below (CEFR, 2020, pp. 36-37).\u003c/p\u003e\n\u003cp\u003eEach of these levels has descriptors that outline the linguistic proficiency that the learner should achieve or that their proficiency is assessed against.\u003c/p\u003e\n\u003cp\u003eThe study by Shtouh and Ibn El-Din (2021, pp. 527-528) identified a set of standards for each level based on the common classification (beginner - intermediate - advanced), with each main level divided into three sub-levels symbolized by (A-B-C). The description covered linguistic skills - listening, speaking, reading, and writing - at each sub-level.\u003c/p\u003e\n\u003cp\u003eShuaib (2014, pp. 33-120) addressed specific objectives for linguistic skills - listening, speaking, reading, and writing - dividing the objectives in each skill into three levels: the first level corresponds to the beginner level, the second to the intermediate level, and the third to the advanced level.\u003c/p\u003e\n\u003cp\u003eThe ACTFL OPI (2012) set standards for speaking skills that describe speaking levels from beginner to distinguished. This comprehensive evaluation of speaking ability is based on a set of general standards:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Functions or Tasks: The functions that the speaker can perform.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Social Contexts and Content Areas: The specific social contexts and content areas where the speaker can perform those functions.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Accuracy: The degree to which the message is understood.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Type of Oral Text or Discourse: The type of oral text or discourse the speaker can produce.\u003c/p\u003e\n\u003cp\u003eThe theoretical framework was used to create standards for assessing Arabic language learners and general linguistic proficiency, proposing a model for evaluating non-native Arabic speakers\u0026apos; linguistic proficiency.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe growing global interest in Arabic as a Foreign Language (AFL) has led to the need for standardized proficiency assessments to measure learners\u0026apos; language abilities across various educational contexts. This literature review explores recent developments in AFL proficiency testing, highlighting challenges and proposing future directions for standardized assessment frameworks. A thorough review of current Arabic proficiency assessments, including the Certificate of Intermediate Modern Arabic (CIMA), the Test of Arabic as a Foreign Language (TAFL), and the Arabic Language Proficiency Test (ALPT), reveals notable shortcomings in terms of validation, coverage, and standardisation (Pujiani, 2024; Sedghi et al., 2022). These assessments frequently have a limited scope, concentrate mostly on Modern Standard Arabic (MSA), and are opaque regarding their content validity, fairness, and psychometric reliability. By putting out a paradigm based on language reality and empirical norms, this study seeks to overcome these problems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical Foundations of Language Proficiency Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecent decades have seen a substantial shift in the theoretical underpinnings of language competence assessment, driven by context-specific findings in Arabic as a Foreign Language (AFL) as well as generic frameworks in applied linguistics. Comprehensive guidelines for test design, construct definition, and validation are offered by seminal books including Davidson and Lynch\u0026apos;s Testcraft (2002), Bachman and Palmer\u0026apos;s Language Assessment in Practice (2010), and Bachman\u0026apos;s Fundamental Considerations in Language Testing (1990). Internationally accepted standards that prioritise validity, reliability, fairness, and ethical concerns, such as the Standards for Educational and Psychological Testing (AERA, APA, \u0026amp; NCME, 1999; 2014), further reinforce these. While Davidson and Fulcher (2007) emphasise the function of frameworks like the CEFR in ensuring that test results are in line with communicative competence benchmarks, Green (2014) also emphasises the significance of connecting test design to theoretical structures and empirical validation.\u003c/p\u003e\n\u003cp\u003eRecent scholarship focuses on the unique challenges of Arabic proficiency testing, arguing that proficiency assessments must differentiate between learners\u0026apos; proficiency in Modern Standard Arabic (MSA) and their competence in spoken dialects (Albirini \u0026amp; Benmamoun, 2022). However, there is a substantial validity gap for students who plan to utilise Arabic in real-world spoken situations because existing tests like TAFL and ALPT remain largely focused on MSA.\u003c/p\u003e\n\u003cp\u003eFurther expanding the theoretical scope, Ibrahim and Taha (2021) integrate cognitive linguistic theories into AFL testing. Their framework emphasizes the cognitive processes involved in acquiring Arabic, given its complex morphological and syntactic structures. This perspective echoes Bachman\u0026rsquo;s (1990) call to define language proficiency not merely in terms of observable performance but also in relation to the cognitive strategies learners employ. Ibrahim and Taha (2021) therefore, advocate for assessments that go beyond surface-level skills to capture the depth of mental engagement necessary for mastering Arabic.\u003c/p\u003e\n\u003cp\u003eThe integration of cultural competence into proficiency assessments also represents a crucial dimension. Hassan (2020) argues that Arabic proficiency cannot be adequately measured without considering cultural knowledge, given the centrality of idiomatic expressions, proverbs, and culturally embedded references. This emphasis aligns with Bachman and Palmer\u0026rsquo;s (2010) assertion that language use is inseparable from context, as well as with broader trends in applied linguistics that highlight the interdependence of language and culture. However, a review of current AFL proficiency tests reveals limited integration of cultural content, particularly in listening and speaking tasks, thereby reducing their relevance for learners preparing for authentic communicative interactions.\u003c/p\u003e\n\u003cp\u003eIn summary, while general frameworks for language proficiency testing provide robust guidelines for test construction and validation, their application to AFL remains limited. The diglossic nature of Arabic, its cognitive complexity, and its deep cultural embeddedness raise special issues that current assessments have not fully addressed. Thus, the development of a globally recognized, standardized Arabic proficiency test requires both adherence to established theoretical principles (e.g., as outlined by AERA, APA, \u0026amp; NCME, 2014; Bachman, 1990; Davidson \u0026amp; Lynch, 2002; Green, 2014) and careful integration of Arabic-specific sociolinguistic, cognitive, and cultural considerations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodological Approaches in Proficiency Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSaleh and Mahdi (2021) analyze technology-enhanced AFL proficiency assessments, highlighting their potential for personalized testing experiences. They argue that these assessments improve accuracy, make the process more engaging, and make the assessment process more accessible for learners. However, the lack of adaptive technology integration in popular Arabic language assessments like TAFL and ALPT limits their ability to adjust to the unique needs of each student. Younes and Al-Othman (2023) propose a novel method for proficiency tests using large-scale Arabic corpora. They developed a prototype test that incorporates diverse inputs, ensuring assessments are representative and sensitive to language variations across different contexts. However, the lack of corpus-informed design in most current standardised assessments restricts their lexical and grammatical authenticity, particularly when it comes to dialectal input.\u003c/p\u003e\n\u003cp\u003eFurtehrmore, recent research by Khan and Malik (2022) introduces a psychometric framework for validating AFL proficiency tests, emphasizing reliability, validity, and fairness. This approach ensures accurate and equitable evaluations for all learners, aligning with current best practices in language testing. However, a shortage of publicly accessible psychometric validation reports for important AFL assessments like TAFL and ALPT raises concerns about the fairness and quality of their measurements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChallenges in Standardizing Arabic Proficiency Assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite advancements in AFL proficiency assessments, challenges persist in standardization. Ali and Rahman (2020) identify that the lack of consensus on Arabic proficiency is a major obstacle, as the wide variation in Arabic dialects complicates the creation of a unified standard. MSA does not fully capture learners\u0026apos; abilities in diverse dialects.The issue is made more difficult by the fact that existing evaluations do not specify which Arabic dialect is being evaluated, which raises questions regarding interpretative validity and concept under-representation.\u003c/p\u003e\n\u003cp\u003eHowever, El-Sayed and Zayed (2021) highlight the logistical challenges of administering standardized tests in the Arab world due to disparities in resources and teacher training, urging collaboration among Arab nations to develop a standardized framework for diverse educational settings. Additionally, in areas with limited resources, the scalability of technology-enhanced evaluations is hampered by unequal access to dependable internet and digital platforms.\u003c/p\u003e\n\u003cp\u003eMoreover, Harb and Jaber (2023) discuss bias in AFL proficiency testing, highlighting how current assessments may favor certain dialects or cultural backgrounds. They propose a more inclusive approach, considering linguistic and cultural diversity, to reduce bias and ensure a more equitable assessment process. The majority of AFL assessments use an implicit MSA standard or default to one dominant dialect (such as Egyptian or Levantine) in speaking and listening parts, which reinforces regional prejudice. There is currently insufficient publicly accessible AFL test that satisfies this inclusion requirement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApplications of Standardized Proficiency Assessments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandardized proficiency assessments, as explored by Zaki and Farah (2022), can significantly improve the credibility and global recognition of international Arabic language programs, ensuring graduates meet consistent language competence levels, thus maintaining the quality and reputation of these programs.\u003c/p\u003e\n\u003cp\u003eMusa and Khalil\u0026apos;s 2023 study highlights the importance of aligning curriculum development with standardized proficiency assessments to improve language learning outcomes. They argue that these assessments provide valuable feedback for curriculum designers, enabling targeted teaching strategies.\u003c/p\u003e\n\u003cp\u003eAhmad and Ibrahim (2024) discuss the increasing use of standardized assessments in language certification and their impact on AFL learners\u0026apos; career prospects. They argue that these tests provide a reliable measure of language skills, enhancing employability and academic opportunities. However, these advantages are undermined if the tests themselves lack a foundation of reliable, inclusive, and well-defined competency standards, an issue that this study specifically aims to address.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eDiglossia and the Rationale for Focusing on Modern Standard Arabic (MSA)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diglossic structure of Arabic language, with colloquial dialects and Modern Standard Arabic (MSA) being the primary varieties, presents challenges for competency evaluation (Jabbari, 2013; R. Ageli, 2013). This study focuses on MSA for competency assessment due to its improved test reliability and pedagogical neutrality. MSA provides a consistent baseline for evaluating students from diverse backgrounds, ensuring fairness and comparability in test outcomes. This approach promotes worldwide acceptance and uniformity in assessing linguistic proficiency (Bachman \u0026amp; Palmer, 2010). The proposed framework addresses the issue of Arabic proficiency examinations exclusion of dialects by implementing a phased approach with MSA as the foundation and future expansions\u0026nbsp;(Davidson \u0026amp; Lynch, 2002; Green, 2014).\u003c/p\u003e\n\u003cp\u003eIn summary, the study prioritizes MSA exam fairness, comparability, reliability, and Arabic diglossia while maintaining educational neutrality and psychometric soundness, considering the communicative significance of dialects.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section aims to explain the procedures followed in the research to answer its questions. Therefore, it covers the research methodology used, its tools, and the statistical processing employed. The details are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilized the descriptive-analytical method to determine a list of standards for constructing a language proficiency test for non-native Arabic learners and another list of general language proficiency standards. This methodology aligns with achieving the research objectives by analyzing specific experiences to benefit from them when making similar decisions (Al-Assaf, 2012, p. 180). Since it enables a methodical review of expert knowledge, documented experiences, and worldwide testing models, this approach was deemed suitable. Furthermore, expert review and piloting were used to establish validity, and inter-rater agreement coefficients were used to statistically assess reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo achieve the research objectives, the study employed a data collection tool consisting of the preparation of a list of standards for constructing a language proficiency test for non-native Arabic learners, along with another list of standards for general language proficiency. The procedures followed to address the research questions are outlined as follows:\u003c/p\u003e\n\u003cp\u003eFirst: Preparing a List of Standards for Constructing a Language Proficiency Test for Non-Native Arabic Learners:\u003c/p\u003e\n\u003cp\u003eA list of standards for constructing a language proficiency test for non-native Arabic learners was prepared according to the following steps:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Defining the Objective of Preparing the List: The objective was to define a list of standards for constructing a language proficiency test for non-native Arabic learners and to build the test accordingly.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Identifying the Sources for Preparing the List: The sources for preparing the list included:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Previous studies that addressed the construction of language proficiency tests such as:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIsa\u0026apos;s Study (2016)\u003c/li\u003e\n \u003cli\u003eAl-Hajri\u0026apos;s Study (2018)\u003c/li\u003e\n \u003cli\u003eAl-Fawzan\u0026apos;s Study (2020)\u003c/li\u003e\n \u003cli\u003eMeloud and Al-Shorbini\u0026apos;s Study (2015)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e2. \u0026nbsp; Books specialized in linguistic tests for non-native Arabic learners, such as:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u0026quot;Linguistic Tests: A Methodological and Practical Approach for Arabic Teachers for Non-Native Speakers\u0026quot;\u003c/li\u003e\n \u003cli\u003e\u0026quot;Measurement and Evaluation in the Field of Evaluating Arabic for Non-Native Speakers\u0026quot;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e3. \u0026nbsp; International language proficiency tests such as:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eIELTS (International English Language Testing System)\u003c/li\u003e\n \u003cli\u003eTOEFL (Test of English as a Foreign Language)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026middot; Preparing the Initial Version of the List\u003cstrong\u003e:\u003c/strong\u003e The initial version of the list included:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; An introduction explaining the objective of preparing the list to the evaluators.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; What is required from the evaluators to express their opinions.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; How to record responses that match the evaluator\u0026apos;s opinion.\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp; The operational definition of language proficiency tests.\u003c/p\u003e\n\u003cp\u003eThe initial version of the standards list consisted of six main areas: the general framework of the test, the formal specifications of the test, the standards for writing each type of test question, the implicit standards of the test, the cultural aspect included in the test, and securing the test. These areas were divided into twenty axes and further subdivided into ninety-six standards.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Presenting the Initial Version of the List to Evaluators: The list was presented to nine evaluators specialized in applied linguistics, curriculum and pedagogy, applied statistics, and computer sciences. Purposively chosen for their areas of expertise, these evaluators included two experts in Educational Measurement and Applied Statistics (with experience in test validation and psychometrics), two computer science scholars specialising in Computer-Assisted Language Testing, and five doctorate-level specialists in Applied Linguistics and Curriculum \u0026amp; Pedagogy. This made sure that linguistic and technology experts were included in the review process, along with assessment specialists.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Making the Modifications Suggested by the Evaluators.\u0026nbsp;Additionally, a preliminary study was conducted involving 25 advanced non-native Arabic speakers to evaluate the suitability, intelligibility, and usefulness of the suggested criteria. Prior to finalising the list, further adjustments were made based on feedback received during the preliminary phase.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Calculating the Reliability of the List: This was done using the agreement coefficient formula (Al-Mufti, 1984, pp. 10-62):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgreement\u0026nbsp;Coefficient =\u003c/strong\u003e Number of Agreements \u0026divide; (Number of Agreements + Number of Disagreements).\u003c/p\u003e\n\u003cp\u003eHowever, it should be made clear that the \u0026quot;agreement coefficient\u0026quot; in this context does not relate to Cronbach\u0026apos;s alpha but rather to an inter-rater agreement indicator. The coefficient in this case measured evaluators\u0026apos; consensus, whereas alpha assesses internal consistency. In accordance with psychometric norms for content validation (Lawshe, 1975; Polit \u0026amp; Beck, 2006), where \u0026ge;80% is normally acceptable, a cut-off threshold of 85% was used. This made sure that only things that were extremely consensual were kept.\u003c/p\u003e\n\u003cp\u003eBased on the results of the previous formula, one standard was deleted because its agreement coefficient value was less than 85%. The remaining standards adopted in the final list had agreement rates among the evaluators ranging from 86% to 94%.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Preparing the Final List of Standards for Constructing an Electronic Test to Determine the Language Proficiency of Non-Native Arabic Learners. Thus, to ensure methodological rigour and practical application, the final list was not only based on the opinions of the evaluators but also supported by preliminary findings and statistical validation, as shown in Table 2.:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2. Description of the Final Version of the Criteria List for Developing an Arabic Language Proficiency Test for Non-Native Speakers.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003c/table\u003e\n\u003cp\u003eSecondly: Preparing a List of Language Proficiency Criteria for Non-Native Arabic Learners for General Purposes.\u003c/p\u003e\n\u003cp\u003eTo determine the language proficiency criteria for non-native Arabic learners, a list was prepared according to the following steps:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Defining the Objective of Preparing the List: The objective was to establish a list of language proficiency criteria for non-native Arabic learners for general purposes and to develop the test in light of these criteria.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Identifying the Sources for Preparing the List: The sources were as follows:\u003c/p\u003e\n\u003cp\u003eo The Common European Framework of Reference for Languages (Study... Teaching... Assessment).\u003c/p\u003e\n\u003cp\u003eo Previous studies addressing language proficiency criteria, such as the study by Muhammad (2022), the study by Al-Ghafir (2021), and the study by Chitoh; Ibn Al-Deen (2021; pp. 527-528).\u003c/p\u003e\n\u003cp\u003eo Specialized books on language skills for non-native Arabic learners, such as the book \u003cem\u003eLanguage Skills: Levels, Teaching, Difficulties\u003c/em\u003e (Taaimah; 2009); the book \u003cem\u003eLanguage Skills: Concept \u0026ndash; Objectives \u0026ndash; Teaching Methods \u0026ndash; Evaluation\u003c/em\u003e (Shuaib; 2014); the book \u003cem\u003eLanguage Tests: A Methodological Practical Approach for Teachers of Arabic to Non-Native Speakers\u003c/em\u003e (Sayyid Ali; 2016); the book \u003cem\u003eMeasurement and Evaluation in the Field of Evaluating Arabic for Non-Native Speakers\u003c/em\u003e (Mousa et al.; 2016); and the book \u003cem\u003eFunctional and Creative Writing\u003c/em\u003e (Abdulbari; 2009).\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Preparing the Preliminary Version of the List: The preliminary version included:\u003c/p\u003e\n\u003cp\u003eo An introduction explaining the objective of the list to the reviewers.\u003c/p\u003e\n\u003cp\u003eo The specific items for the reviewers to evaluate.\u003c/p\u003e\n\u003cp\u003eo How to record responses that match the reviewers\u0026apos; opinions.\u003c/p\u003e\n\u003cp\u003eo The operational definition of language proficiency.\u003c/p\u003e\n\u003cp\u003eThe preliminary list of criteria included twelve main domains representing the four language skills, distributed across the main levels (Beginner \u0026ndash; Intermediate \u0026ndash; Advanced), from which 44 criteria emerged, accompanied by 131 indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003ePresenting the Preliminary List to Reviewers: A total of 9 reviewers, specialized in the field of Applied Linguistics, Curriculum, and Educational Foundations, were involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eImplementing Reviewers\u0026rsquo; Feedback: Modifications suggested by the reviewers were made.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eCalculating the Stability of the List: Stability was calculated using the agreement coefficient formula (Mufti; 1984; pp. 10-62), where the agreement coefficient = number of agreements / (number of agreements + number of disagreements).\u003c/p\u003e\n\u003cp\u003eBased on the previous formula\u0026rsquo;s result, three criteria were removed because their agreement coefficient was below 85%. The remaining criteria, which were approved in the final list, had an agreement rate between 88% and 96% among the reviewers.\u003c/p\u003e\n\u003cp\u003e7. \u0026nbsp; Preparing the Final Version of the Language Proficiency Criteria List for Non-Native Arabic Learners for General Purposes as illustrated in the table above.\u003c/p\u003e\n\u003cp\u003eDescription of the List of Language Proficiency Standards for Non-Native Arabic Learners for General Purposes (Table 3)\u003c/p\u003e\n\u003cp\u003eTable 3. Description of Language Proficiency Standards and Performance Indicators for Non-Native Arabic Learners (General Purposes)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003ctable 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe table outlines standards and indicators for evaluating non-native Arabic learners\u0026apos; language proficiency across various skill levels, quantifying expected proficiency levels in each field. The process of developing standards for a language proficiency test followed a systematic sequence of stages, as illustrated in Figure 2.\u003c/p\u003e"},{"header":"Result and Discussion","content":"\u003cp\u003eThe study developed a Comprehensive Framework for Arabic Proficiency Test Development, focusing on seven categories to create a valid and trustworthy exam for learners. The framework aligns with international standards like CEFR and ACTFL, incorporating dialectal and Modern Standard Arabic abilities. It emphasizes scope and sequence matrices, cognitive progression, and combining scenario-based exercises with authentic texts. The framework also suggests section weightings, expert validation, and balanced item design for fairness. The framework reveals several significant benefits. Its inclusion of intercultural-awareness exercises improves communication ability, and its thoroughness and methodical structure push the field beyond models that only emphasise MSA. Furthermore, the use of cutting-edge security measures like biometric authentication and AES encryption shows a response to new difficulties in preserving test integrity, particularly in distant environments.\u003c/p\u003e\n\u003cp\u003eNevertheless, various types of limitations need careful consideration. Despite being recognised, dialect integration lacks precise operational standards, which increases the possibility of inconsistent results across testing scenarios. Many Arabic learners are located in under-resourced environments, where the focus on cutting-edge security measures may limit accessibility. Additionally, even if the suggested psychometric requirements are strict, they require a lot of resources and might not be scalable for smaller schools. Despite their importance in influencing Arabic teaching methods, washback effects are likewise given little consideration in the framework. The suggested skill weightings (e.g., speaking 30%, listening 40%, reading 20%, and writing 10%), on the other hand, seem inflexible and might not adequately represent the varied demands of students in academic vs. professional settings. Lastly, even while intercultural awareness exercises improve communication skills, they must be carefully tempered to prevent unforeseen political or cultural sensitivities. The framework effectively measures Arabic competency through theoretical alignment, psychometric rigor, and cultural responsiveness. However, improvements in dialect inclusion, skill-weighting models, tiered psychometric techniques, and security measures are needed for effective deployment and technical validity.\u003c/p\u003e\n\u003cp\u003eThe second study question was to determine the language skill requirements for general-purpose Arabic language learners. Twelve domains, 41 standards, and 133 performance indicators are covered by the finalised standards, which are divided into three levels: beginner, intermediate, and advanced. Basic MSA communication skills, including greetings, basic directions, and literacy activities like reading signs or filling out forms, are expected of learners at the beginner level. Measurable and situationally appropriate competencies are provided by these criteria. In order to help students communicate effectively in social situations, deduce meanings from context, and create coherent, organised writings, the intermediate level includes dialectal comprehension and production (such as Egyptian and Levantine). The advanced level has a strong emphasis on academic literacy and critical engagement. Students are expected to understand lengthy discourses, assess suggested meanings, use rhetorical devices, and create complex writing, including argumentative essays with appropriate references.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eFramework for Developing an Arabic Proficiency Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe suggested framework for assessing non-native speakers\u0026apos; Arabic competency strikes a compromise between the sociolinguistic characteristics of Arabic and worldwide comparability. Based on the Common European Framework of Reference for Languages (CEFR), the framework incorporates regional dialects starting at the intermediate level to guarantee fairness and authenticity. It covers six progressive levels (A1\u0026ndash;C2) and prioritises Modern Standard Arabic (MSA) as the primary testing medium.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTheoretical and Pedagogical Foundations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe framework, which prioritises MSA to overcome the difficulties of Arabic diglossia, is founded on the ideas of pedagogical neutrality and dependability. Beginning with listening and speaking exercises at the intermediate level, dialectal competency is progressively included to mimic actual communication situations without disadvantaged students from a variety of backgrounds. Through the steady development of sociolinguistic awareness, this strategy guarantees that all candidates acquire functional literacy in MSA.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eProficiency Progression\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003eCompetencies are structured in three broad bands aligned with CEFR levels.\u003c/p\u003e\n\u003cp\u003ei. \u003cstrong\u003eBeginning (A1\u0026ndash;A2):\u003c/strong\u003e Students understand basic greetings, classroom directions, and basic MSA discussions; they generate courteous expressions and brief exchanges; they read short, real-world texts like menus or signs; and they finish simple written assignments like notes, forms, and brief emails.\u003c/p\u003e\n\u003cp\u003eii. \u003cstrong\u003eIntermediate (B1\u0026ndash;B2):\u003c/strong\u003e At this level, students are able to deduce meaning from context and comprehend well-known MSA and common dialect subjects. They recount events, engage in prolonged social relationships, and properly adjust their register. Writing stresses well-structured and coherent writings, whereas reading entails evaluating relatively lengthy materials and summarising important concepts.\u003c/p\u003e\n\u003cp\u003eiii. \u003cstrong\u003eAdvanced (C1\u0026ndash;C2):\u003c/strong\u003e The ability to critically assess inferred meanings in prolonged, fast-paced MSA and dialectal information, such as news broadcasts and academic lectures, is the pinnacle of proficiency. Reading concentrates on the examination of intricate texts, rhetorical devices, and hidden meanings, whereas speaking calls for convincing and educational discourse in discussions and official presentations. Argumentative and academic essays that incorporate several viewpoints and reference academic sources are examples of writing assignments.\u003c/p\u003e\n\u003cp\u003eThis phased approach provides horizontal integration across speaking, listening, reading, and writing as well as vertical complexity increase.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTest Content and Specifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe test balances skills across modalities, as seen in Table 4 below, including speaking (15%), listening (30%), reading (30%), limited writing (15%), and extended writing (10%) all of which are comparable to the IELTS. The tasks gradually increase in difficulty to mirror the demands of real-life communication. They are scenario-based and culturally genuine. According to Bloom\u0026apos;s taxonomy, the exam ensures thorough evaluation across domains by covering both lower-order (literal comprehension) and higher-order (critical evaluation) cognitive skills. Test Content (Table 4)\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003eTable 4. Relative Weighting of Skills and Linguistic Components in the Proposed Arabic Proficiency Test\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eBut each CEFR level\u0026apos;s operational abilities are defined in full in Table 5 of the specifications, which also connects them to Bloom\u0026apos;s cognitive taxonomy. This guarantees both horizontal skill integration and vertical task complexity growth.\u003c/span\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e Specifications Table for Test Items (Table 5):\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTable 5. Test Specifications Matrix Across CEFR Levels and Cognitive Domains\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSkill Levels\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eContent of Questions\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eQuestion Methods\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eClassification of Questions According to Bloom\u0026apos;s Levels\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNumber of Procedural Skills\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eRelative Weight of Questions\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eListening Comprehension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eA1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4 Listening Texts (Simple Conversation - Short Text - Medium Text - Extended Text)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eThere will be questions under each text (listening - reading) and related to it, such as: - Multiple Choice. - True or False. - Completion.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e33%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eA2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eB1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eB2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eC1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eC2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eReading Comprehension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3 Reading Texts (Texts vary between simple, medium, and advanced)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e33%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eOral Production\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eCompleting the dialogue with appropriate expressions. Commenting on a picture according to specific conditions. Speaking for two minutes on a specific topic.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eResponse will be oral\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e17%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eWritten Production\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eMeasuring constrained writing ability (substitution \u0026ndash; conversion, etc.) Writing on a specific topic with a specific number of words, for example, 200 words\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eResponse will be written\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConstrained\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e17%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eCreative\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e108\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e100%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eItem Development and Assessment Rubrics\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reading and listening portions include a variety of real-world materials and multiple-choice, true/false, and completion questions. Descriptive rubrics derived from the finalised standards are used to assess speaking and writing exercises, and their validity and reliability are tested with non-native learners. Writing assignments at the B1 level, for instance, call for organised descriptions, whereas argumentative essays with citations are required at the higher levels. By offering numeric scales and tiered descriptions, rubrics improve uniformity and lessen rater bias.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eScoring Mechanism\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eThe total score of the test is equal to 6, where the score is calculated based on the average of the four test sections. For a clearer explanation, refer to the scoring mechanism as outlined in the tables below.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 6, the scoring method uses a six-band scale (0\u0026ndash;6) that corresponds to CEFR competence levels. As indicated in a for application, each band reflects a range of correct answers, and total scores are calculated by averaging the outcomes of each individual segment.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eMechanism for Calculating Test Scores for Each Section and the Overall Score (108).\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTable (6)\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTable 6. Score Conversion Scheme and CEFR Alignment for Receptive Skills\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eListening Comprehension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eReading Comprehension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eProficiency Level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eCorrect Answers\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eScore\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eC2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e34-36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e31-33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eC1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e28-30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e25-27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eB2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e22-24\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e19-21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eB1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e16-18\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e13-15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eA2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e10-12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e7-9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eA1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4-6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1-3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eOral Production\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eWritten Production\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eFurthermore, as shown in Table 7, the average of the scores from the four skill categories is used to calculate total competence. Pilot findings show internal consistency, practical practicality, and good validity.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eExample Application: Student\u0026apos;s Scores in Each Section (Total Test Score: 108) | Table (7)\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTable 7. Example of Overall Proficiency Calculation Based on Section Scores\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eSkills\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eScore Obtained by the Student\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eScore Conversion\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eProficiency Level According to CEFR\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eProficiency Level Description\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eListening Comprehension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e26\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eC1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eAdvance\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eReading Comprehension\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e22\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eB2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eIndependent\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eOral Production\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eB2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eIndependent\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eWritten Production\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eB1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eIntermediate\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eTotal Score\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e69\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003e15.5 \u0026divide; 4 = 4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eB2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cspan dir=\"LTR\"\u003eIndependent\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn addition to thorough pilot testing to determine difficulty indices, discrimination power, and time efficiency, the platform integrates cutting-edge security procedures, including encryption, biometric identification, and AI-supported remote proctoring. Integrity and fairness are intended to be protected by these procedures. \u0026nbsp;The suggested structure has definite advantages:\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp;Dialectal exposure and MSA integration that respects Arabic\u0026apos;s diglossia while maintaining equity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eii.\u0026nbsp; \u0026nbsp;Conformity to the CEFR, which improves comparability and worldwide acceptance.\u003c/p\u003e\n\u003cp\u003eiii.\u0026nbsp;\u0026nbsp;Assessing productive talents using approved rubrics ensures accurate and transparent results.\u003c/p\u003e\n\u003cp\u003eTherefore, the framework for assessing Arabic competence has limitations, including inconsistent implementation, inability for specialized learners, and limited measures of intercultural ability. Future iterations should improve dialect inclusion, incorporate intercultural competency descriptors, use variable skill-weighting models, and undergo extensive piloting for scalability and positive washback.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAssessment and Rubrics\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalytical rubrics that measure lexical range, grammatical precision, coherence, fluency, and pragmatic appropriateness are used to assess speaking and writing. To improve descriptions and guarantee inter-rater reliability, these rubrics were tested with non-native students. This method supports educational and evaluative roles by allowing the exam to deliver both summative scores and diagnostic comments.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003ePsychometric Quality Assurance\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough statistical studies for dependability and multifaceted validity tests (content, construct, predictive, and concurrent), the system incorporates strict quality assurance. Cronbach\u0026apos;s \u0026alpha; is used to quantify internal consistency, and item-level analysis guarantees that the difficulty and discrimination indices are adequate. Timing, clarity, and fairness are further confirmed by pilot testing.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSecurity and Integrity\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to in-person procedures, the test uses AI-assisted remote proctoring, biometric identification, and AES encryption to ensure integrity. By preventing misconduct and ensuring test-taker authenticity, these steps improve confidence in both domestic and foreign settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectronic Test Delivery: Implications and Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe suggested framework\u0026apos;s use of electronic test delivery is a noteworthy aspect that reflects recent changes in international language assessment standards. The design, item formats, user interface, security protocols, and accessibility features have all been influenced by the shift to digital means.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence on Test Design and Item Formats\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond traditional paper-based forms, the electronic platform allows for a variety of item kinds, such as drag-and-drop vocabulary completion, adaptive reading comprehension exercises, and interactive listening comprehension activities with embedded audio. Digital recording of spoken replies enables asynchronous assessment and improves inter-rater reliability. In order to maintain authenticity and reduce construct-irrelevant variation, writing assignments are typed within a regulated interface that provides automated word counts and simple text formatting without turning on external resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser Interface and Accessibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClear navigation, on-screen timers, and sample practice questions before each session were all features of the learner-friendly layout. To ensure inclusion, accessibility features were incorporated, including screen readers, scalable font sizes, and support for assistive devices. Because it adheres to universal design principles, applicants with a range of technological skills and physical requirements can take the test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest Security and Integrity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStrong security measures, such as role-based access restrictions, biometric or two-factor authentication, and AES encryption, were required for electronic distribution. The framework makes a distinction between in-person proctoring and AI-assisted remote proctoring (which includes screen monitoring, audio-video surveillance, and keyboard analysis) in order to combat academic dishonesty. To lessen predictability without sacrificing standardisation, test items are randomised within predetermined boundaries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Challenges and Mitigation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChallenges,\u0026nbsp;including digital inequality, technical issues, and possible test-taker anxiety,\u0026nbsp;are also brought about by the delivery\u0026nbsp;of tests electronically. These were lessened through several tactics:\u003c/p\u003e\n\u003cp\u003ei.\u0026nbsp; \u0026nbsp;\u0026nbsp;Digital Equity: Support for low-bandwidth situations and offline test module availability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eii. \u0026nbsp; Technical Reliability: Thorough pilot testing on many platforms and devices, along with backup plans in case of disruptions.\u003c/p\u003e\n\u003cp\u003eiii. \u0026nbsp;Affective Support: Practice sessions and orientation materials help acquaint test-takers with the platform and ease their nervousness.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eCritical Implications\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe framework for evaluating Arabic combines linguistic and cultural sensitivity with CEFR standards, ensuring sociolinguistic validity, authenticity, and fairness. It incorporates dialects, follows international tests, and provides detailed insights into student achievement. Thus, the digital approach enhances scalability, supports diverse item formats, and strengthens test security. Addressing dialect integration and intercultural competency elements would improve Arabic proficiency examinations\u0026apos; global comparability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations and Conclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Recommendations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study suggests enhancing Arabic language proficiency assessments by creating a general-purpose test matrix, developing academic standards, and creating descriptive rubrics for procedural skills, particularly speaking and writing, to ensure consistency and clarity in evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions and Recommendations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study suggests two future research directions for Arabic language proficiency tests: a general-purpose test for non-native speakers and an academic test for academic success. While progress has been made in developing standardized assessments, challenges remain. As highlighted by Albirini and Benmamoun (2022) and Ibrahim and Taha (2021), theoretical frameworks need refinement to account for cognitive and sociolinguistic complexities of the Arabic language. Practical assessment tools should be integrated for more accurate measures. As demonstrated by Saleh and Mahdi (2021), technology-enhanced assessments offer potential for personalized and adaptive testing environments, making them more accessible to a wider range of learners. Furthermore, Future iterations will investigate the incorporation of integrated and interactive tasks (e.g., reading-to-writing or listening-to-speaking activities), which are increasingly recognised for their ability to capture authentic language use and communicative competence (Giraldo, 2020). This phased approach strikes a balance between the pedagogical benefits of holistic language assessment and the need for reliability and comparability. The current framework emphasises the traditional four-skill model for standardisation and benchmarking purposes.\u003c/p\u003e\n\u003cp\u003eAnother area that warrants further investigation is the challenge of bias in proficiency testing. As Harb and Jaber (2023) have shown, existing assessments may inadvertently favour certain linguistic or cultural groups. Future research should focus on developing more inclusive assessment tools that account for the diverse linguistic backgrounds of AFL learners. This could involve the creation of test items that are representative of a broader range of dialects and cultural contexts, thereby reducing bias and ensuring a fairer assessment process. Finally, greater collaboration among researchers, educators, and policymakers is essential to overcome the challenges identified in this review. Standardizing proficiency assessments for AFL learners is a complex but necessary task that requires a coordinated effort from all stakeholders involved. By working together, the academic and professional communities can develop a unified framework for proficiency assessment that is both theoretically sound and practically effective.\u003c/p\u003e\n\u003cp\u003eAdditionally,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe review highlights the need for further investigation into the issue of bias in proficiency testing, suggesting that more inclusive assessment tools should be developed to account for the diverse linguistic backgrounds of AFL learners. Collaboration among researchers, educators, and policymakers is also crucial for a unified framework for proficiency assessment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArabic as a Foreign Language\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommon European Framework of Reference for Languages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACTFL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Council on the Teaching of Foreign Languages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModern Standard Arabic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Proficiency Interview\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eILR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInteragency Language Roundtable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdvanced Encryption Standard\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eFunding \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelbari, M. 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Standardized tests in international Arabic language programs. \u003cem\u003eJournal of Language Studies, 40\u003c/em\u003e(3), 151\u0026ndash;173.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"language-testing-in-asia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ltia","sideBox":"Learn more about [Language Testing in Asia](http://languagetestingasia.springeropen.com)","snPcode":"40468","submissionUrl":"https://submission.springernature.com/new-submission/40468/3","title":"Language Testing in Asia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Language proficiency, proficiency assessment, Arabic as a foreign language, standardized testing, applied linguistics","lastPublishedDoi":"10.21203/rs.3.rs-8584118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8584118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArabic language learners experience difficulties since there is a limited internationally accepted framework for standardised competency, which restricts the validity and comparability of tests. This study aims to close this gap by creating a thorough framework for creating standardised Arabic proficiency tests that are appropriate for both general learners and those pursuing specific goals. This study proposed a comprehensive framework for constructing standardized language proficiency assessments tailored to learners of Arabic as a foreign language. This study employed a descriptive-analytical methodology to identify the key standards necessary for developing a robust and reliable language proficiency test. The proposed standards encompass seven core aspects with 20 subtopics, resulting in 96 specific standards for Arabic language proficiency assessments for specialized purposes. Additionally, for general language learning, the framework presents 12 primary aspects branching into 41 standards, aligned with 133 performance indicators. This dual framework offers a systematic approach for test development, ensuring alignment with both theoretical foundations and practical language acquisition goals. The findings suggest that standardization is crucial for creating effective assessment tools that accurately measure the language proficiency of Arabic learners in diverse learning contexts. The framework offers educators, institutions, and policymakers clarity by offering a systematic approach to test construction. Enhancing assessment quality, improving student outcomes, and advancing the international acceptance of Arabic competence are some of its implications. Recommendations for further research and application of these standards in practical settings are provided.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"A Framework for Developing Standardized Proficiency Assessments for Arabic as a Foreign Language Learners","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:30:08","doi":"10.21203/rs.3.rs-8584118/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T04:41:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T09:31:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53552239002455620100870314528109577586","date":"2026-02-20T09:00:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T22:25:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T16:16:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141564959339047062080121926866906290499","date":"2026-02-13T02:28:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22892942391352413916924834904245725918","date":"2026-02-11T07:10:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123067087088574623055963691359813961692","date":"2026-02-11T02:56:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321184016638126884556266783527797674227","date":"2026-02-11T02:16:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T01:24:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T22:04:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T01:20:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Language Testing in Asia","date":"2026-01-12T16:45:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"language-testing-in-asia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ltia","sideBox":"Learn more about [Language Testing in Asia](http://languagetestingasia.springeropen.com)","snPcode":"40468","submissionUrl":"https://submission.springernature.com/new-submission/40468/3","title":"Language Testing in Asia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"652da9c9-e83a-4065-8a9a-9288e45e5388","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T06:26:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:30:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8584118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8584118","identity":"rs-8584118","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}