Artificial Intelligence in Academic Staff Profile Assessment

University academic staff are regarded as elites in Kurdish society, and since they are active citizens of the community, their online profiles need to reflect their scholarly contributions and social responsibilities. This study presents the first comprehensive application of artificial intelligence (AI) in assessing university academic staff profiles within the Kurdistan Region of Iraq, employing Critical Discourse Analysis (CDA) as the theoretical framework. The research analyzed 200 academic staff profiles across eight universities in the Kurdistan Region: Salahaddin University-Erbil, University of Sulaymaniyah, University of Duhok, Koya University (Koysanjaq), Erbil Polytechnic University, University of Zakho, Raparin University, and Soran University, evaluating research skills, language proficiency, and citation practices using AI-powered assessment tools. The methodology integrates Fairclough's three-dimensional CDA framework with automated content analysis, natural language processing, and citation network analysis (Chen et al., 2023). Results reveal significant disparities in academic profile quality, with 71% of profiles showing inadequate research skill representation and 52% demonstrating limited international citation engagement. Statistical analysis using chi-square tests (χ² = 31.67, p < 0.001) and ANOVA (F (7,192) = 18.42, p < 0.001) revealed significant differences across institutions and disciplines. Salahaddin University-Erbil demonstrated the highest profile quality (M = 18.45), while regional universities showed developmental needs (Raparin University: M = 10.23). The findings contribute to understanding AI's transformative potential in academic evaluation while revealing underlying power structures and institutional hierarchies embedded in faculty profile construction within post-conflict educational contexts (Anderson & Williams, 2023). Regression analysis indicates that institutional ranking (β = 0.47, p < 0.01) and faculty experience (β = 0.34, p < 0.05) significantly predict profile quality scores. The study establishes a novel framework for AI-mediated academic assessment while addressing critical questions about algorithmic bias, cultural sensitivity, and equitable evaluation practices in the Kurdistan Region's diverse educational landscape (Roberts et al., 2024).