AI Formative Assessment in Saudi Education: A Study Across Universities - Alenezi, Abdullah; Alenezi, Abdulhameed (Journal of Teaching and Learning, 2025)
Within the context of Saudi Arabia's digital transformation strategy, Vision 2030, the issue of artificial intelligence (AI) adoption in higher education is gaining momentum, changing the form of the formative assessment procedure and its perception. Given the scope of interests in the functions of Arabic Natural Language Processing (NLP) feedback, this study examines the impact of AI-based formative assessment on instructors' flexibility, writing scores, and student engagement in Saudi Arabian institutions. Ten undergraduate students and five faculty members from public and private institutions were invited to participate in the research, which employed a mixed-methods approach to reconstruct classroom settings. Although the qualitative data, gathered through responses in the form of stories, were analyzed using the theme-square approach to identify linguistic and cultural orientations in the interpretation of feedback, the quantitative data, such as revision rates, engagement, and performance ratings, were analyzed using descriptive statistical methods. Although concerns about forcing speakers to use Arabic language technology as a natural language do exist, which are relevant to cultural, linguistic, and fairness issues, the results suggest limited and gradual improvement in the quality and quantity of revisions, resulting in students writing with the assistance of AI-generated feedback. The results offer insight into why culturally specific AI systems are crucial for fostering an equitable culture, delivering practical instruction, and supporting the professional development of faculty members. The current study significantly impacts the ongoing debate about artificial intelligence in education and its consequences for educational settings, especially in non-English-speaking contexts.