Artificial Intelligence (AI) Equality in Engineering Education: Strategies to Unite the AI Gap between the Global North & South

The rapid emergence of Artificial Intelligence (AI) already has, and will continue to, impact the global society by catalyzing innovation across socio-political, economic, and academic aspects. Within an engineering education context, AI-assisted technology can facilitate and accelerate active, experiential, reflective, and inquiry-based learning among university students. Presently, these initiatives dominate among countries comprising the Global North, and less concentrated among the Global South countries. Existence of this recent AI gap between the two country blocs has substantial impact on the learning and mentoring of students at higher education institutes (HEIs). This AI gap may be primarily attributed to infrastructural restrictions that limitlprevent the most efficient usage/harnessing of data in the Global South countries. A global bibliometric analysis performed across major scientific databases (Scopus, ScienceDirect, IEEE and Google Scholar) helped to identify some key causal factors either attributing to, or augmenting, the AI gap. An overwhelming majority of AI-based articleslpublications arise from the Global North, thereby quantitatively evidencing our hypothesis. The gap is exacerbated if key Global South contributors (India and China) are removed from the comparison. Identification of th ese factors can help to realize AI equality, thus minimizing the AI-gap in engineering education for university curricula. This gap may even be potentially closed if governments are suitably equipped to reevaluate their internal capabilities to implement necessary strategic investments to develop suitable facilities and infrastructure. Specific to the university level, our systematic mapping strategies can help educators (i) be aware of teachingllearning based challenges between the two country blocs, and (ii) in turn, plan course content and design curricula mindfully, with an attempt to doing their part towards unifying the learning experience, closing the gap, to create maximum student impact. Minimization and eradication of this gap paves the pathway for the creation of “AI Equality” - a non-discriminatory vision of global AI, that is equally accessible and empoweringfor both Global North and South economies. Since our mapping attempt is purely generic and devoid of any a priori assumptions, our proposed framework may be employed to spatio temp o rally track the effectiveness of AI implementation and reduction of the AI gap across countries. Our procedure may be made more granular to identify country specific measures taken towards eradicating this gap. The proposed procedure provides an unbiased, evidence-based framework (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>≥ 0.9 for most RQs) devoid of assumptions to comprehensively monitor AI impact on engineering education, curriculum design, student learning, etc., and can also provide detailed insights on which specific learning modalities (active/experiential/inquiry-driven) are most effective. Beyond the context of the present research study, our method is reproducible to other global scale research problems and may be used across domains that extend beyond STEM (Science, Technology, Engineering & Mathematics). The synergistic and conscious integration of our approach has immense potential towards creating the foundations of a fair, AI-inclusive global economy, that ensures AI equality between the Global North and South. Bridging this gap will prove critical towards implanting the Education 5.0 vision across Higher Education Institutes (HEIs) to meet Industry 5.0's ever-changing requirements from university graduates.