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How to Effectively Integrate Innovative Artificial Intelligence Tools into Undergraduate Engineering Curriculum by Problem-Based Learning Approach

Faculty #95
Discipline: Technology & Engineering
Subcategory: STEM Research
- Texas Southern University
Co-Author(s): Youmei Liu, University of Houston, Houston, TX



This study aims to initiate research-supportive curricula in the Department of Engineering for undergraduate students. The overarching goal is to infuse innovative electrical/computer engineering specialized Artificial Intelligence (AI) tools into traditional engineering problem-solving routines by problem-based learning (PBL) approach to bridge current curricula gap in the Department of Engineering at Texas Southern University (TSU). Several major steps were taken towards three specific objectives during the first two and half years of funded effort: 1) to develop an interactive and comprehensive knowledge-based expert system (KBES) that can document, compare, and analyze cutting-edge AI applications in CE field and use it as the platform and educational media for curricula development and implementation; 2) to integrate AI approach into engineering curriculum; and 3) to support undergraduate students’ early involvement in research. The PI has put concentrated efforts on AI infused curriculum development including: 1) Three pilot studies that infused broader AI concepts, paradigms and tools to different level students including freshmen level introduction course (CIVE141 Civil Engineering Materials), junior and senior level design and analysis courses (CIVE 334 Transportation Engineering and CIVE 335 Geometric Design of Highway); 2) Two consecutive sessions of larger scale implementation study with modified and improved AI infusion course contents on the same sophomore level core course (CIVE 224 Geotechnical Engineering). The research team has developed several different instruments and methodology for measuring progress toward desired student learning outcomes and program goals. A t-Test was used to compare student performance of the two consecutive sessions of implementation studies (CIVE224). One of the focus was to find out whether there was any difference in student learning outcome due to different implantation approaches used in classroom. The contents of AI infusion are identical to both sessions. The results indicate that students with early preparation and introduction of the AI concepts outperformed those students without such preparation with higher mean value and the difference is statistically significant with alpha = 0.05. The generated P value = 0.021, which is much lower than the alpha value. Based on these results, it is imperative for future full-scale AI infusion curricula to provide students sufficient early preparation and introduction of basic AI concepts in order to achieve better learning outcomes. The proposed KBES can be utilized as an easy-to-access pre-exposure platform in this regard. Based on the 2nd year preliminary case study results, the PBL approach is an appropriate instructional method to infuse AI into traditional engineering curriculum. The infused curriculum of several civil engineering core courses have shown noticeably positive feedbacks by both direct and indirect assessment.

Funder Acknowledgement(s): NSF HBCU-UP Targeted Infusion Award HRD1533569

Faculty Advisor: None Listed,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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