  Question generation for education assessments is a growing field within
artificial intelligence applied to education. These question-generation tools
have significant importance in the educational technology domain, such as
intelligent tutoring systems and dialogue-based platforms. The automatic
generation of assessment questions, which entail clear-cut answers, usually
relies on syntactical and semantic indications within declarative sentences,
which are then transformed into questions. Recent research has explored the
generation of assessment educational questions in Arabic. The reported
performance has been adversely affected by inherent errors, including sentence
parsing inaccuracies, name entity recognition issues, and errors stemming from
rule-based question transformation. Furthermore, the complexity of lengthy
Arabic sentences has contributed to these challenges. This research presents an
innovative Arabic question-generation system built upon a three-stage process:
keywords and key phrases extraction, question generation, and subsequent
ranking. The aim is to tackle the difficulties associated with automatically
generating assessment questions in the Arabic language. The proposed approach
and results show a precision of 83.50%, a recall of 78.68%, and an Fl score of
80.95%, indicating the framework high efficiency. Human evaluation further
confirmed the model efficiency, receiving an average rating of 84%.
