The Academy of Sciences of I. R. of Iran Iranian Journal of Engineering Education 1607-2316 Articles in Press 2026 05 23 Developing AI-Enabled Hard and Soft Employability Skills in Engineering Students: A Theory-Building Meta-Synthesis Developing AI-Enabled Hard and Soft Employability Skills in Engineering Students: A Theory-Building Meta-Synthesis 244115 10.22047/ijee.2026.575317.2240 FA Mehdi Mohammadi Shiraz University 0000-0002-0121-5092 Fateme Mirghafari Ph.D. in Educational Administration, Department of Administration and Educational Planning, Faculty of Educational Sciences and Psychology, Shiraz University, Shiraz, Iran 0000-0002-9533-7065 Journal Article 2026 02 26 With the rapid expansion of artificial intelligence (AI) technologies and the changing expectations of the engineering labor market, revisiting university teaching and learning approaches has become increasingly necessary. This study aims to identify the antecedents, processes, and outcomes of teaching AI thinking to foster engineering students’ employability skills, using a theory-building meta-synthesis approach. A qualitative, inductive meta-synthesis was conducted based on the seven-step Finfgeld-Connett model. A systematic literature review was performed through structured searches in major Persian and international databases using guiding keywords. Studies were initially retrieved based on title–keyword relevance and then screened; ultimately, 50 articles were included for analysis and component extraction. Qualitative data were synthesized through open, axial, and selective coding, and the resulting model was subjected to descriptive, interpretive, theoretical, and pragmatic validation. The findings indicated that antecedents can be conceptualized as the knowledge, skills, and attitudes required of both students and faculty members. The process dimension comprised instructional mechanisms for developing AI thinking skills. Outcomes were articulated as the development of hard and soft employability skills among engineering graduates, particularly through engagement with AI tools and platforms. The proposed model provides a strategic framework for redesigning engineering education to enhance graduates’ employability. With the rapid expansion of artificial intelligence (AI) technologies and the changing expectations of the engineering labor market, revisiting university teaching and learning approaches has become increasingly necessary. This study aims to identify the antecedents, processes, and outcomes of teaching AI thinking to foster engineering students’ employability skills, using a theory-building meta-synthesis approach. A qualitative, inductive meta-synthesis was conducted based on the seven-step Finfgeld-Connett model. A systematic literature review was performed through structured searches in major Persian and international databases using guiding keywords. Studies were initially retrieved based on title–keyword relevance and then screened; ultimately, 50 articles were included for analysis and component extraction. Qualitative data were synthesized through open, axial, and selective coding, and the resulting model was subjected to descriptive, interpretive, theoretical, and pragmatic validation. The findings indicated that antecedents can be conceptualized as the knowledge, skills, and attitudes required of both students and faculty members. The process dimension comprised instructional mechanisms for developing AI thinking skills. Outcomes were articulated as the development of hard and soft employability skills among engineering graduates, particularly through engagement with AI tools and platforms. The proposed model provides a strategic framework for redesigning engineering education to enhance graduates’ employability.