The last few decades have witnessed rapid developments in computer science and information technologies, starting with data storage and retrieval, then moving to analysis and transformation into information, to knowledge production, and finally to artificial intelligence applications simulating some human mental capabilities such as reasoning, learning, and planning. The effects of these transformations were not limited to technical and economic aspects but extended to university education, raising fundamental questions about its philosophy, objectives, curricula, teaching methods, and assessment.

For decades, university education focused on knowledge acquisition and retrieval as a criterion for judging student performance and measuring learning outcomes, in response to the needs of the industrial economy, which relied on preparing a workforce possessing the knowledge and skills necessary to perform specific functions and tasks. However, the shift to a knowledge economy imposed new requirements: preparing individuals capable of continuous learning, understanding reality and analyzing its complexities, adapting to changes, generating knowledge and innovation, and making decisions in a fast-changing environment. Consequently, transferring knowledge content is no longer the main goal of university education; rather, it has become necessary to adopt a student-centered educational model that focuses on developing their mental, analytical, and creative abilities, empowering them to apply and produce knowledge instead of merely memorizing and recalling it.

From this perspective, artificial intelligence is seen as a supportive educational tool for learning, not as a threat to the educational process as some faculty members view it, who reduce the educational process to knowledge acquisition. The role of artificial intelligence in the educational process can be likened to that of a research assistant in universities. Therefore, the real challenge lies not in the technology itself, but in developing educational practices and training faculty members to transition from rote teaching to teaching methodologies that align with the requirements of the digital economy era.

In this context, AI can handle answering 'how' questions, including procedural tasks related to initial information search, data organization, proofreading, translation, and formatting. Meanwhile, the purpose of university education has become centered on building the student's ability to raise questions about 'what?', 'why?', and 'where to?', developing their skills in constructing logical arguments, forming evidence-based opinions, analyzing and discussing complex issues, making professional and ethical judgments, and providing innovative solutions to real-world problems. Thus, the perception of AI shifts from a feared tool to a learning partner.

This shift requires adopting teaching methods based on discussion and scientific debate, case studies, and group work. However, this transition remains complex in the traditional university context, as a significant number of faculty members still practice teaching within a traditional knowledge model that reduces education to content transmission and measures success by the volume of information retrieved by students. This reflects the continued dominance of an educational philosophy that views knowledge acquisition as an end in itself, rather than a tool for understanding reality, analyzing problems, and practically applying knowledge.

In this context, the challenge seems not only related to methodologies but also to the professional culture formed over decades of academic practice based on rote learning and examinations. As a result, many faculty members face real difficulties in transitioning to more interactive, collaborative, and critical roles, either due to limited proficiency in communication skills and managing academic dialogue, or due to weak application of theories and concepts in analyzing reality and formulating innovative solutions to emerging problems. This perpetuates a gap between the requirements of modern education and its actual practices within the classroom.

Accordingly, employing AI in university education requires adopting a comprehensive institutional strategy based on training and empowering faculty members as leaders of change, and reinforcing their conviction in AI's role as a supportive learning tool that accelerates access to information, organization, and preliminary analysis. This would redirect students' time and effort toward higher-value skills, shifting from focusing on information gathering and retrieval to developing their abilities in understanding, analysis, critique, comparison, decision-making, and problem-solving—skills that represent the essence of university education in the age of AI.

This necessitates developing teaching methodologies based on dialogue, case studies, academic discussions, and group work, along with reconsidering the criteria for evaluating faculty performance based on effective communication, discussion management, stimulating critical thinking, and developing students' self and analytical abilities. Thus, AI becomes a supportive technical tool that fosters university education that produces knowledge of social and economic value.