IMPLEMENTING CLAUDE AI-POWERED ASSESSMENT IN PAKISTANI GRAMMAR INSTRUCTION: A CASE STUDY OF TECHNOLOGY ACCEPTANCE MODEL AMONG FEMALE EDUCATORS

The rapid integration of artificial intelligence tools in higher education presents both opportunities and challenges, particularly in developing countries where gender disparities and technological barriers intersect. This study investigates Claude AI integration in grammar instruction through a single case study examining technology acceptance among female faculty and students in Pakistani higher education. The research employs an extended Technology Acceptance Model (TAM) framework to explore how perceived usefulness, perceived ease of use, trust, AI anxiety, and facilitating conditions influence Claude adoption for AI-powered grammar assessment. The study focuses on an ADP-level English Grammar practice at Govt. Graduate College for Women, Pakistan, where Claude AI was used to generate interactive quizzes with immediate feedback across five grammar domains: subject-verb agreement, pronoun usage, verb tenses, error correction, and reading comprehension. Using a mixed-methods case study design implemented over one academic semester (16 weeks), data collection involved pre-post student surveys (N=52), instructor reflective journal, student focus groups (N=16), classroom observations, and analysis of student performance data from 487 quiz attempts. Findings revealed significant improvements across all TAM constructs, with perceived ease of use showing the largest effect size (Cohen's d=2.15). AI anxiety decreased substantially from pre-implementation (M=5.67) to post-implementation (M=3.12), while trust emerged as the strongest predictor of behavioral intention through qualitative analysis. Student performance improved dramatically, with 78% achieving scores of 80-100% on AI-generated quizzes and a 12.7 percentage point improvement in final exam scores compared to the previous cohort without AI intervention. Gender-specific findings indicated that female educators prioritized trust-building through verification, peer support networks, and immediate student success feedback over technical sophistication. The study provides theoretical contributions through TAM validation for AI assessment tools in developing country contexts and practical implications for designing gender-inclusive, culturally-sensitive AI integration strategies in resource-constrained educational environments.