مطالب

This article provides a philosophical analysis of identity within the context of artificial intelligence, from the perspective of Transcendent Philosophy (especially the views of Mulla Sadra) and contemporary philosophy. With the rapid advancements in artificial intelligence as one of the most significant innovations in computer science, issues such as the reconstruction of human identity and its comparison with natural intelligence have come to the fore. The paper examines the philosophical capacities and limitations of this phenomenon, exploring the perspectives of Mulla Sadra and John Searle, and offers a comparative analysis of their views on identity and consciousness in machines and artificial intelligence. According to the views of these two thinkers, artificial intelligence, despite its advancements, cannot achieve human identity, as it lacks essential characteristics such as consciousness, intentionality, and abstraction. Searle argues that artificial systems will never be able to attain genuine consciousness because understanding meaning and consciousness are intrinsic to the human brain and mind, qualities that cannot be attributed to machines. He consistently supports this belief through the Chinese Room experiment and various critiques of artificial systems.

Despite the importance of continuous professional development for schools principals, researches show that their professional development approaches need to be review and move towards programs based on collective learning. Therefore, the main goal of this research was to provide a model for professional learning communities of elementary school principals. The present study was conducted using the qualitative research method of grounded theory and interviews with a targeted sample, including 29 key informants (15 university professors and 14 school principals) with relevant research records. The interviews were analyzed using the coding methods of Strauss and Corbin (1998). Four criteria of data authenticity, transferability, reliability and verifiability were used to check the validity of the data. After conducting interviews and open, axial and selective coding; The research results showed 18 general selection codes including causal conditions (collective learning culture; feeling the need for collective learning; leadership and management styles), contextual conditions (dynamic and flexible organizational structure; infrastructures and facilities; empathy and common goals; Encouraging principals towards learning communities), intervening conditions (teaching soft skills; suitable time and place for meetings related to communities; management knowledge; correct plan and program), central phenomenon (professional learning communities), Strategies for developing professional learning communities (suitable work space; professional learning; the existence of a supportive policy for learning communities; modeling of the experiences of successful schools) and consequences (organizational consequences; student success; professional development of teachers) that reflected the pattern of professional learning communities of primary school principals and the relationships between its different dimensions.