Procedural Transparency and Legal Accountability to Sustain AI-Mediated Language Assessment in Saudi Arabia
Bibliographic Data
| ID | 22006200 |
|---|---|
| Authors | Abdullah Al Fraidan (0000-0002-5152-9794, King Faisal University, corresponding author) |
| Year | 2025 |
| Volume | 15 |
| Issue | 4 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/21582440251396113 |
| OpenAlex | W7108067817 |
| Language | EN |
| References cited | 23 |
This study explores the influence of procedural transparency and legal-rights awareness on stakeholder perceptions of fairness in AI-mediated language assessments across Saudi universities. Despite the increasing integration of artificial intelligence (AI) tools into educational assessment, significant concerns persist regarding the transparency of algorithmic scoring processes and stakeholders’ understanding of their legal rights under relevant data-protection frameworks. Using a descriptive correlational approach, this research investigates these concerns by gathering insights from students, faculty members, and administrators from five universities across Saudi Arabia. The findings highlight a strong relationship between greater procedural transparency and reduced perceptions of algorithmic bias. Additionally, higher awareness of legal protections under the Saudi Personal Data Protection Law (PDPL) and the Saudi Data and Artificial Intelligence Authority (SDAIA) Ethics Principles further diminished bias perceptions among participants. Notably, perceptions of transparency varied significantly by stakeholder role, with administrators reporting the least clarity about AI assessment processes. These insights underline the necessity of clear, accessible explanations regarding AI decision-making processes and targeted educational initiatives to enhance stakeholder legal awareness. Overall, this study contributes to a deeper understanding of how transparency and legal accountability can foster sustainable trust and fairness in AI-based educational assessments, offering actionable recommendations for institutional governance and policy development
Accountability · CLARITY · Corporate governance · Perception · Plain language · Stakeholder · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI
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The effects of explainability and causability on perception, trust, and acceptance
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| Citation velocity | historical |
|---|---|
| Highly cited | No |