This study investigates disciplinary incidents involving engineers in Quebec, shedding light on critical gaps in engineering education. Through a comprehensive review of the disciplinary register of the Ordre des ingénieurs du Québec (OIQ)’s disciplinary register for 2010 to 2024, researchers from engineering education and human resources management in technological development laboratories conducted a thematic analysis of reported incidents to identify patterns, trends, and areas for improvement. The analysis aims to uncover the most common types of disciplinary incidents, underlying causes, and implications for the field in how engineering education addresses (or fails to address) these issues. Our findings identify recurring themes, analyze root causes, and offer recommendations for engineering educators and students to mitigate similar incidents. This research has implications for informing curriculum development, professional development, and performance evaluation, ultimately fostering a culture of professionalism and ethical responsibility in engineering. By providing empirical evidence of disciplinary incidents and their causes, this study contributes to evidence-based practices for engineering education and professional development, enhancing the engineering education community’s understanding of professionalism and ethics.
Category: Type: Research
In this category:
1. Peer-Reviewed:
Research Papers
Chapters
Conference Proceedings
2. Pre-Prints. Pre-Prints are standard in some fields. They are not always peer reviewed.
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Shifting the Gaze? Photojournalism Practices in the Age of Artificial Intelligence
In this article, we explore the impact of artificial intelligence (AI) technologies on photojournalism in less-researched contexts in Botswana and Zimbabwe. We aim to understand how AI technologies, proliferating aspects of news production, are impacting one of journalism’s respected and enduring trades- photojournalism. We answer the question: In what ways are AI-driven technologies impacting photojournalism practices? Furthermore, we investigate how photojournalists perceive their roles and the ethical considerations that come to the fore as AI begin to technically influence photojournalism. We deploy an eclectic analytical framework consisting of the critical technology theory, disruptive innovation theory and Baudrillard’s concept of simulation to theorise how AI technologies affect photojournalism in Botswana and Zimbabwe. Data were collected using in-depth interviews with practising photojournalists and …
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Whole-Person Education for AI Engineers: Presented to CEEA (Peer Reviewed)
This autoethnographic study explores the need for interdisciplinary education spanning both technical an philosophical skills – as such, this study leverages whole-person education as a theoretical approach needed in AI engineering education to address the limitations of current paradigms that prioritize technical expertise over ethical and societal considerations. Drawing on a collaborative autoethnography approach of fourteen diverse stakeholders, the study identifies key motivations driving the call for change, including the need for global perspectives, bridging the gap between academia and industry, integrating ethics and societal impact, and fostering interdisciplinary collaboration. The findings challenge the myths of technological neutrality and technosaviourism, advocating for a future where AI engineers are equipped not only with technical skills but also with the ethical awareness, social responsibility, and interdisciplinary understanding necessary to navigate the complex challenges of AI development. The study provides valuable insights and recommendations for transforming AI engineering education to ensure the responsible development of AI technologies.
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WIP: Gen AI in Engineering Education and the Da Vinci Cube (Peer Reviewed)
As generative AI (GenAI) tools rapidly transform the engineering landscape, a critical question emerges: Are current educational innovations adequately preparing engineers for the socio-technical challenges of the future? This work-in-progress paper presents two key contributions. First, we build on prior work presenting a systematic review of over 160 scholarly articles on GenAI implementations in engineering education, revealing a predominant focus on enhancing technical proficiency while often neglecting essential socio-technical competencies. Second, we apply an emerging framework—the da Vinci Cube (dVC)—to support engineering educators in critically evaluating GenAI-driven innovations. The dVC framework extends traditional models of innovation by incorporating three dimensions: the pursuit of knowledge, consideration of use, and contemplation of sentiment. Our analysis suggests that while GenAI tools can improve problem-solving and technical efficiency, engineering education must also address ethical, human-centered, and societal impacts. The dVC framework provides a structured lens for assessing how GenAI tools are integrated into curricula and research, encouraging a more holistic, reflective approach. Ultimately, this paper aims to provoke dialogue on the future of engineering education and to challenge the prevailing assumption that technical skill development alone is sufficient in an AI-mediated world.
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Nature Opinion: The path for AI in poor nations does not need to be paved with billions
NATURE
Researchers in low- and middle-income countries show that home-grown artificial-intelligence technologies can be developed, even without large external investments.
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What We Do Not Know: GPT Use in Business and Management
This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.
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‘Mind the gap’: artificial intelligence and journalism training in Southern African journalism schools
This article examines journalism schools (J-schools) responses to the Artificial Intelligence (AI) ‘disruption’. It critically provides an exploratory examination of how J-Schools in Southern Africa are responding to the AI wave in their journalism curriculums. We answer the question: How are Southern African J-Schools responding to AI in their curriculums? Using a disruptive innovation theoretical lens and through documentary review of university teaching initiatives and accredited journalism curriculums, augmented by in-depth interviews, we demonstrate that AI has opened up new horizons for journalism training in multi-dimensional ways. However, this has brought challenges, including covert forms of resistance to AI integration by some Journalism educators. Furthermore, resource constraints and the obduracy of J-schools’ curriculums also contribute to the slow introduction of AI in J-schools.
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A labeled clinical-MRI dataset of Nigerian brains
There is currently a paucity of neuroimaging data from the African continent, limiting the diversity of data from a significant proportion of the global population. This in turn diminishes global health research and innovation. To address this issue, we present and describe the first Magnetic Resonance Imaging (MRI) dataset from individuals in the African nation of Nigeria. This dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality, with 35 images from healthy control subjects, 31 images from individuals diagnosed with age-related dementia, and 22 from individuals with Parkinson’s Disease. Given the potential for Africa to contribute to the global neuroscience community, this unique MRI dataset represents both an opportunity and benchmark for future studies to share data from the African continent.
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Community energy justice: A review of origins, convergence, and a research agenda
The transition to zero‑carbon sustainable energy systems is critical and must take an equity-oriented approach to avoid exacerbating societal injustices. We explore the concept of “community” and its potential as a viable and effective tool for studying, understanding, and fostering justice and equity in energy transitions. This paper outlines community energy justice as an area of scholarship emerging through convergence around three key concepts: community, energy transition, and justice. Using a narrative literature review approach, we unpack the origins of community energy justice research, rooted in two scholarship pillars of energy justice and community energy. We outline four driving forces and two key approaches leading to convergence between both areas of scholarship. Encompassing energy transition initiatives that incorporate both justice and community themes, we find that the overarching objective …
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Options and Motivations for International AI Benefit Sharing
Advanced AI systems could generate substantial economic and other societal benefits, but these benefits may not be widely shared by default. For a range of reasons, a number of prominent actors and institutions have called for efforts to expand access to AI’s benefits. In this report, we define the concept of international AI benefit sharing (“benefit sharing”) as efforts to support and accelerate international access to AI’s economic or broader societal benefits. Calls for benefit sharing typically invoke at least one of three motivations: 1) supporting inclusive economic growth and sustainable development, 2) fostering technological self-determination in low- and middle-income countries, and 3) advancing geopolitical objectives, including strengthening international partnerships on AI governance. Notably, as a subset of the third motive, some powerful actors – like the US government – may support benefit sharing as a tool to further their economic and national security interests. Benefit sharing could be implemented by (1) sharing AI resources (e.g., computing power or data), (2) expanding access to AI systems, or (3) transferring a portion of the financial proceeds from AI commercialisation or AI-driven economic growth. Depending on the objective that benefit sharing is intended to achieve, each of these approaches offers distinct opportunities and implementation challenges. These challenges include the potential for some benefit-sharing options to raise security concerns and increase certain global risks. Actors interested in benefit sharing may consider implementing low-risk forms of benefit sharing immediately, while launching cooperative international discussions to develop more comprehensive, mutually-beneficial initiatives.