Integrating Artificial Intelligence into Whole-Person Education for a New Paradigm in Engineering Education
This investigation assesses the incorporation of artificial intelligence (AI) within the realm of engineering education, underscoring its congruence with Harvard University's 'Whole-Person Education' framework and the prevailing intelligentization trend in the Digital-Intelligent Era. The study delves into the imperatives, theoretical underpinnings, salient features, and intrinsic challenges of the paradigm shift towards intelligent engineering education. Employing a systematic review methodology, this research synthesizes findings from 82 empirical studies spanning the period from 2011 to 2024, all of which are anchored within the General Systems Theory (GST) framework. The analysis elucidates the complex roles and systemic interplays of AI technologies within the engineering educational paradigm. The paper meticulously articulates the intrinsic nature of intelligent engineering education, accentuating the strategic embedding and profound utilization of intelligent technologies to catalyze the metamorphosis and reconfiguration of engineering educational systems. It categorizes and examines six pivotal AI application domains within engineering education, dissecting their manifestation of pivotal intelligent education characteristics, such as technological synergy, resource amalgamation, and platform convergence. Moreover, the study offers an objective examination of the impediments to the realization of intelligent engineering education, encompassing the enhancement of educational milieus with intelligent features, the reconceptualization of pedagogical and student roles, and the evolution of pedagogical content and approaches. The paper proffered targeted solutions to these challenges, thereby contributing innovative theoretical perspectives and pragmatic directives that are instrumental in propelling academic dialogue and practical strides in educational innovation and quality within engineering education.
Authors
- Naixin Zhang (ORCID: https://orcid.org/0000-0001-8036-6835)
- Wai Yie Leong (ORCID: https://orcid.org/0000-0002-5389-1121)
Institutions
- INTI International University (MY)
Publication Details
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1109/ieir62538.2024.10959915
- Citations
- 6
- Primary Topic
- Engineering Education and Technology
- Type
- article
- Field-Weighted Citation Impact
- 4.67