Schema-Responsive Rubric Design for Epistemic Justice a Fairness-Aware Assessment Framework for Multilingual Learners in Computer-Delivered English Proficiency Testing

References

[1] Sadeghi K. Technology in language assessment: an overview. In: Technology-Assisted Language Assessment in Diverse Contexts. 2022.

[2] Galaczi E. Face-to-face and computer-based assessment of speaking: challenges and opportunities. In: Computer-Based Assessment of Foreign Language Speaking. 2010.

[3] Ma W. What the analytic versus holistic scoring of international teaching assistants can reveal: lexical grammar matters. Lang Test. 2022.

[4] Bachman L. What is the construct? The dialectic of abilities and contexts in defining constructs in language assessment. In: The Writings of Lyle F. Bachman. 2024.

[5] Bogorevich V. Cognitive processes of native and nonnative teachers as raters of L2 speaking performance [dissertation]. Flagstaff (AZ): Northern Arizona University; 2026.

[6] Pill J, Smart C. Raters: behavior and training. In: The Routledge Handbook of Second Language Acquisition and Language Testing. 2020.

[7] Carel H, Kidd IJ. Epistemic injustice in healthcare: a philosophical analysis. Med Health Care Philos. 2014.

[8] Ülgen H. Known but unseen: linguistically mediated testimonial injustice and academic recognition in Russian-medium higher education. J Interdiscip Stud Educ. 2026.

[9] Wong C. Creating a meaningful summative assessment in a Chinese immersion context: a translanguaging perspective. Appl Linguist Rev. 2025.

[10] Zhang S, Gu M. Development and validation of the digital trans-literacies scale (DTLS) for multilingual youth. System. 2026.

[11] Yamasaki J, Sharf B, Harter L. Narrative inquiry: attitude, acts, artifacts, and analysis. In: Research Methods in Health Communication. 2014.

[12] Kerdvibulvech C. Designing fairness: human-AI collaboration for social equity and ethical inclusion in interactive systems. Lect Notes Comput Sci. 2026.

[13] Hanumansetty R. Model based approach for context aware and adaptive user interface generation [master’s thesis]. Blacksburg (VA): Virginia Tech; 2004.

[14] Zhang W, Sun S, Hu L, Liu L, Gao W, Li S. Metacognitive training and AI-based Socratic scaffolding in pre-service teachers’ reflective practice: a dual-study experimental design. Front Psychol. 2026.

[15] Angelov P, Soares E, Jiang R, et al. Explainable artificial intelligence: an analytical review. WIREs Data Min Knowl Discov. 2021.

[16] Davies A, Elder C. Validity and validation in language testing. In: Handbook of Research in Second Language Teaching and Learning. 2005.

[17] Oliveri M, Nastal J, Slomp D. Reflections on equity-centered design. ETS Res Rep Ser. 2020.

[18] Klepsch M, Seufert T. Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instr Sci. 2020.

[19] Zhong H. The migration of bias in law: from human cognition to predictive AI system and large language models. In: International Handbook of Legal Language and Communication. 2026.

[20] Cicatiello C, Fusco P. Gender bias in generative artificial intelligence: genealogies of inequality, technological reproduction, and feminist futures. Encyclopedia. 2026.

[21] Ragamsetty V. Beyond predictive accuracy: a critical synthesis of AI-powered student performance modeling and the problem of algorithmic personalization in education. J Integr Sci Technol Eng. 2026.

[22] Yi R. Algorithmic fairness in AI-mediated institutional communication: a computational framework for multilingual professional interaction. 2026.

[23] Yi R, Li Z, Zhang W. Fairness and bias evaluation. In: SpringerBriefs in Computer Science. 2026.

[24] Manojkumar R. Reframing AI bias for equitable and culturally responsive education. In: Advances in Computational Intelligence and Robotics. 2027.

[25] Bajaj S. Linguistic justice and algorithmic bias in the Indian digital sphere: ethical and legal challenges. 2026.

[26] de-Lima-Santos M, Medrado A. Expanding the boundaries of methodologies. In: Handbook of Global Journalism and Communication. 2026.

@article{1724287,
      author = {Dr. Jaya Sinsinwar},
      title = {Schema-Responsive Rubric Design for Epistemic Justice a Fairness-Aware Assessment Framework for Multilingual Learners in Computer-Delivered English Proficiency Testing},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {4186-4203},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724287.pdf},
      abstract = {Computer-delivered English proficiency tests frequently penalize multilingual learners whose responses deviate from monolingual academic norms, thereby perpetuating epistemic injustice through rubrics that conflate communicative resourcefulness with linguistic deficit. We propose a schema-responsive rubric interface that mediates between learners’ epistemic resources and assessment outcomes across four interconnected sub-modules. The Epistemic Justice Criteria Module replaces deficit-oriented descriptors with translanguaging recognition and oral narrative structure validation, wherein mixed-language segments are evaluated by pragmatic function rather than grammatical purity, and non-linear storytelling traditions are accommodated through adjusted coherence scoring. The Dynamic Rubric Rendering Engine then visually links response features to epistemic justice descriptors via context-aware highlighting, while an interactive calibration slider allows raters to modulate emphasis on communicative effectiveness against grammatical accuracy. Furthermore, the Rater Cognitive Support Layer embeds mandatory bias mitigation prompts and a standardized exemplar library to reduce inter-rater variability. The Audit and Feedback Loop subsequently ensures accountability through tamper-proof decision logging and generates feedback that explicitly validates recognized epistemic practices. The novelty of this framework lies in its integration of sociolinguistic theory with human-computer interaction design, transforming the rubric from a static judgment instrument into an adaptive mediator of fairness. Significance is twofold: methodologically, it offers a replicable architecture for embedding epistemic justice into assessment interfaces; practically, it fosters learner agency by acknowledging community-referenced reasoning and local idioms as legitimate evidence of proficiency.},
      keywords = {epistemic justice; schema-responsive rubric design; fairness-aware assessment; multilingual learners; computer-delivered English proficiency testing},
      month = {October},
  }