Prayogi Pangestu Hadi
Universitas Islam Negeri Sultan Syarif Kasim Riau

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Assessment and Evaluation in Arabic Language Teaching: A Systematic Literature Review Prayogi Pangestu Hadi
Al-Badi’ Journal : Journal Of Arabic Language Education And Arabic Language Studies Vol. 1 No. 2 July - December 2026
Publisher : PT. Global Pustaka Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61590/abj.v1i2.281

Abstract

This systematic literature review (SLR) synthesizes assessment and evaluation practices in Arabic Language Teaching (ALT) based on Scopus-indexed publications from 2020 to 2025. This review appears to be among the earliest dedicated SLRs focusing specifically on assessment and evaluation in ALT, prior reviews in this domain have primarily addressed teaching methods, instructional media, or curriculum development, with assessment examined only tangentially. Guided by PRISMA 2020 and the PICo research question structure, a Boolean search string was applied to the Scopus TITLE-ABS-KEY field via Publish or Perish, yielding 122 initial records. After dual-reviewer independent screening (Cohen's κ = 0.81) and full-text eligibility assessment, 28 studies were included. Quality appraisal using an adapted MMAT/CASP checklist yielded predominantly high-quality studies (n=22, 78.6%) with a subset rated moderate quality (n=6, 21.4%), reflecting the heterogeneous methodological landscape of ALT assessment research. Narrative synthesis informed by Bachman and Palmer's (1996) validity framework, Black and Wiliam's (1998) formative assessment theory, and Kane's (2013) argument validity approach identified five thematic clusters: summative and achievement testing, technology-enhanced assessment, program evaluation, formative and diagnostic assessment, and AI-based evaluation. AI-mediated assessment emerged as a dominant trend from 2023 onward. Reading comprehension (35.7%) and oral proficiency (21.4%) dominate the skill focus. Six critical research gaps are identified, including the near-absence of formative assessment research, insufficient psychometric validation of digital instruments, and unaddressed ethical dimensions of AI-based assessment.