CHALLENGES AND SOLUTIONS FOR INTEGRATING ARTIFICIAL INTELLIGENCE INTO TRANSPORTATION ENGINEERING EDUCATION

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DOI:

https://doi.org/10.52326/jss.utm.2024.7(4).06

Keywords:

student equation, unique learning needs, dynamic knowledge co-creation, individualesed learning, large uncertainty educational situations

Abstract

This study introduces the "student equation" assumption to represent the individualized learning pathways of each student, highlighting their unique needs, challenges, and potentials. Standardized educational approaches, resembling to an "arithmetic mean solution", often fail to address the diverse cognitive abilities and developmental needs of students due to their one-size-fits-all nature. The basic hypothesis posits that standardized methods primarily serve the average student, neglecting individual learner diversity. The research aims to explore the complexities of student learning by acknowledging variations in reasoning processes, errors, and cognitive dilemmas influenced by known and unknown variables in their educational journey. The findings suggest that educators must evolve beyond traditional methods to guide students through personalized learning experiences, akin to explorers navigating unknown territories. This educational paradigm seeks to cultivate a more adaptable and inquisitive student body, prepared for discovery. By aligning teaching methods with individualized student needs, this approach aims to enhance learning outcomes and bridge the gap between standardized education and the unique learning equations of each student.

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Published

2025-01-15

How to Cite

Nantoi, V., Nantoi, D., & Ceban, D. (2025). CHALLENGES AND SOLUTIONS FOR INTEGRATING ARTIFICIAL INTELLIGENCE INTO TRANSPORTATION ENGINEERING EDUCATION. JOURNAL OF SOCIAL SCIENCES, 7(4), 64–95. https://doi.org/10.52326/jss.utm.2024.7(4).06

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