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: Animesh Paul
Animesh Paul
Biography (under construction)
Google Scholar
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Developing the Permanent Symposium on AI (poster): Presented at Engineering and Public Policy Division (EPP) Poster Session
A multidisciplinary, reflective autoethnography by some of the people who are building the Permanent Symposium on AI. Includes the history of the project.
RQ 1: Challenges that unite AI policy & tech
RQ 2: How to design the PSAI?
RQ 3: What factors influence the adoption and scalability of the PSAI?
This is the Flagship project of the Aula Fellowship.
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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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Work in Progress: Exclusive Rhetoric in AI Conference Mission Statements
AI conferences are pivotal spaces for knowledge exchange, collaboration, and shaping the trajectory of research, practice, and education. This paper presents preliminary findings from an analysis of AI conference mission statements, investigating how their stated goals affect who is welcomed into AI conversations. We find that many mission statements reflect assumptions that may unintentionally narrow participation and reinforce disciplinary and institutional silos. This limits engagement from a broad range of contributors—including educators, students, working professionals, and even younger users —who are essential to a thriving AI ecosystem. We advocate for clearer framing that supports democratizing and demystifying AI. By broadening participation and intentionally fostering cross-sector and interdisciplinary connections, AI conferences can help unlock more innovation.
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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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Path to Personalization: A Systematic Review of GenAI in Engineering Education
This systematic review paper provides a comprehensive synthesis across 162 articles on Generative Artificial Intelligence (GenAI) in engineering education (EE), making two specific contributions to advance research in the space. First, we develop a taxonomy that categorizes the current research landscape, identifying key areas such as Coding or Writing Assistance, Design Methodology, and Personalization. Second, we highlight significant gaps and opportunities, such as lack of customer-centricity and need for increased transparency in future research, paving the way for increased personalization in GenAI-augmented engineering education. There are indications of widening lines of enquiry, for example into human-AI collaborations and multidisciplinary learning. We conclude that there are opportunities to enrich engineering epistemology and
competencies with the use of GenAI tools for educators and students, as well as a need for further research into best and novel practices. Our discussion serves as a roadmap for researchers and educators, guiding the development of GenAI applications that will continue to transform the engineering education landscape, in classrooms and the workforce. -
Exploring and Expanding Support for International Students in Engineering: Faculty Reflections Beyond Academic Boundaries
This is a student paper:
Expanding upon our previous work in the blinded for review paper, this research seeks to delve into the realm of self-reflection among engineering faculty members who regularly interact with international students. The primary objective is to investigate how these faculty members address the unique needs of the international student community. The Challenge and Support model by Nevitt Sanford serves as our guiding framework for this research, and we employ narrative analysis due to its potential in analyzing differences in cases and describing the dynamics of individual narratives within their distinct contexts (Floersch et al., 2010; Simons et al., 2008).
This paper aims to answer the following research question: How do engineering faculty members address the multifaceted and distinct needs of international students? It is important to understand these perspectives when considering how to support international engineering students given that each student has unique and intricate experiences in both academic and non-academic aspects.
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Reimagining AI Conference Mission Statements to Promote Inclusion in the Emerging Institutional Field of AI
AI conferences play a crucial role in education by providing a platform for knowledge sharing, networking, and collaboration, shaping the future of AI research and applications, and informing curricula and teaching practices. This work-in-progress, innovative practice paper presents preliminary findings from textual analysis of mission statements from select artificial intelligence (AI) conferences to uncover information gaps and opportunities that hinder inclusivity and accessibility in the emerging institutional field of AI. By examining language and focus, we identify potential barriers to entry for individuals interested in the AI domain, including educators, researchers, practitioners, and students from underrepresented groups. Our paper employs the use of the Language as Symbolic Action (LSA) framework [1] to reveal information gaps in areas such as no explicit emphasis on DEI, undefined promises of business and personal empowerment and power, and occasional elitism. These preliminary findings uncover opportunities for improvement, including the need for more inclusive language, an explicit commitment to diversity, equity, and inclusion (DEI) initiatives, clearer communications about conference goals and expectations, and emphasis on strategies to address power imbalances and promote equal opportunities for participation. The impact of our work is bi-fold: 1) we demonstrate preliminary results from using the Language as Symbolic Action framework to text-analysis of mission statements, and 2) our preliminary findings will be valuable to the education community in understanding gaps in current AI conferences and consequently, outreach. Our work is thus of practical use for conference organizers, engineering and CS educators and other AI-related domains, researchers, and the broader AI community. Our paper highlights the need for more intentional and inclusive conference design to foster a diverse and vibrant community and community of AI professionals.