Category: Sector: Education

Hard Questions: Education

  • Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

    Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems

    Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models.

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  • Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling

    Qualitative Insights Tool (QualIT): LLM Enhanced Topic Modeling

    Topic modeling is a widely used technique for uncovering thematic structures from large text corpora. However, most topic modeling approaches e.g. Latent Dirichlet Allocation (LDA) struggle to capture nuanced semantics and contextual understanding required to accurately model complex narratives. Recent advancements in this area include methods like BERTopic, which have demonstrated significantly improved topic coherence and thus established a new standard for benchmarking. In this paper, we present a novel approach, the Qualitative Insights Tool (QualIT) that integrates large language models (LLMs) with existing clustering-based topic modeling approaches. Our method leverages the deep contextual understanding and powerful language generation capabilities of LLMs to enrich the topic modeling process using clustering. We evaluate our approach on a large corpus of news articles and demonstrate substantial improvements in topic coherence and topic diversity compared to baseline topic modeling techniques. On the 20 ground-truth topics, our method shows 70% topic coherence (vs 65% & 57% benchmarks) and 95.5% topic diversity (vs 85% & 72% benchmarks). Our findings suggest that the integration of LLMs can unlock new opportunities for topic modeling of dynamic and complex text data, as is common in talent management research contexts.

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  • Path to Personalization: A Systematic Review of GenAI in Engineering Education

    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.

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  • Reimagining AI Conference Mission Statements to Promote Inclusion in the Emerging Institutional Field of AI

    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.

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  • Bridging the Gap: Exploring Semiconductors Exposure and Motivation among Multidisciplinary Engineering Students

    Bridging the Gap: Exploring Semiconductors Exposure and Motivation among Multidisciplinary Engineering Students

    Several educational initiatives are currently underway to address workforce challenges in the semiconductors industry. Assessing students’ exposure to and motivation for semiconductors-related topics is an essential initial step toward recognizing areas where primary efforts should be concentrated. The primary objective of this study is to assess students’ awareness and motivation concerning semiconductors in the context of a multidisciplinary introduction to electrical engineering course. Through quantitative analysis and the administration of an existing validated survey instrument, we aim to explore students’ exposure to semiconductors-related topics and potential correlations between awareness, motivation, and demographic variables, including gender and class standing. The instrument was administered to a cohort of 255 students enrolled in a multidisciplinary course covering the fundamentals of electrical engineering. Preliminary data indicates that only 9% of the students in this cohort haven taken a class about semiconductors and only 3% have some interest in pursuing a career in the semiconductors field. The results of this analysis hold several significant implications for engineering education and the semiconductor industry. Firstly, the limited exposure to and interest in semiconductors among engineering students suggest the need for curriculum alignment with the demands of the semiconductor industry and interdisciplinary education. By doing so, we empower students from diverse disciplines to contribute to technological advancements, innovation, and problem-solving fostering a more inclusive, diverse, and well-rounded workforce within the semiconductor sector.

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  • Teaching Future-Makers: Outcomes of an International Design Workshop for Critical Action Educators

    Teaching Future-Makers: Outcomes of an International Design Workshop for Critical Action Educators

    This paper reports on recent developments of the Critical Action Learning Exchange (Carvalho et al., 2021), an international community of educators who seek to respond to social and environmental issues that affect their students. We report on an international design workshop that engaged a cohort of teachers in designing Critical Action Learning activities for their students in the Summer of 2023. Participants (n=39) completed 16 curriculum designs for grade levels from kindergarten to university, addressing a broad range of socio-environmental issues and adopting diverse approaches, such as Arts-Based Critical Action, Community Engagement, Critical Making, Games for Critical Action, and Storytelling. This paper examines our Professional Development model, together with an analysis of teacher participants’ ideas and their design products. We investigate what forms of scaffolding can facilitate the changes of practice needed for teachers to become critical action educators and support their Critical Action Learning designs.

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  • Decoding the Diversity: A Review of the Indic AI Research Landscape

    Decoding the Diversity: A Review of the Indic AI Research Landscape

    This review paper provides a comprehensive overview of large language model (LLM) research directions within Indic languages. Indic languages are those spoken in the Indian subcontinent, including India, Pakistan, Bangladesh, Sri Lanka, Nepal, and Bhutan, among others. These languages have a rich cultural and linguistic heritage and are spoken by over 1.5 billion people worldwide. With the tremendous market potential and growing demand for natural language processing (NLP) based applications in diverse languages, generative applications for Indic languages pose unique challenges and opportunities for research. Our paper deep dives into the recent advancements in Indic generative modeling, contributing with a taxonomy of research directions, tabulating 84 recent publications. Research directions surveyed in this paper include LLM development, fine-tuning existing LLMs, development of corpora, benchmarking and evaluation, as well as publications around specific techniques, tools, and applications. We found that researchers across the publications emphasize the challenges associated with limited data availability, lack of standardization, and the peculiar linguistic complexities of Indic languages. This work aims to serve as a valuable resource for researchers and practitioners working in the field of NLP, particularly those focused on Indic languages, and contributes to the development of more accurate and efficient LLM applications for these languages.

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  • Exploring and Expanding Support for International Students in Engineering: Faculty Reflections Beyond Academic Boundaries

    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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  • Investigating Transition Phases: An Autoethnographic Study of International Women of Color Engineering Educators in the US

    Investigating Transition Phases: An Autoethnographic Study of International Women of Color Engineering Educators in the US

    The study aims to explore the transitions experienced by international Women of Color (IWoC) engineers in the US as they navigate their academic and professional lives. Motivated by the lack of research on IWoC’s experiences, specifically around transition points of their lives, four international Women of Color participated in this qualitative auto-ethnographic deep-dive. All four researchers have attended college in the United States for their high educational degrees focused on education/engineering education and are currently involved in engineering education scholarship work.

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  • Making Space for Critical Action: Re-visioning Computational Thinking

    Making Space for Critical Action: Re-visioning Computational Thinking

    While school makerspaces promise to inspire and excite, the challenge of meaningfully integrating them into schools remains. Guided by a philosophy of praxis that stresses the need for education to interweave theory, action, and reflection to advance positive social change in our communities (Freire, 1970), this paper reports on the co-design of a school space called the Critical Action Learning Lab (CALL) for inclusive making to support computational thinking and critical action through curriculum-informed learning

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  • World AI: Women in AI

    World AI: Women in AI

    A collaborative event with Women in AI. We asked conference participants to tell us about their hard questions in AI, and had many fruitful conversations for future collaborations.

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  • Outsiders: Pathways and Perspectives from Engineering Education PhDs Outside Academia

    Outsiders: Pathways and Perspectives from Engineering Education PhDs Outside Academia

    This article presents a critical exploration and recommendation based on the lived experiences of PhD graduates in Engineering Education who have ventured into non-academic career paths. The work is rooted in an auto-ethnographic research approach, and the report aims to mimic a live virtual panel. It seeks to elucidate the experiences and challenges faced by PhD graduates who diverged from traditional academic roles to pursue careers in industry, entrepreneurship, consulting, and pre-college leadership. These narratives reveal a complex landscape of motivations, perceived hierarchical barriers, and under-recognition within academic and non-academic sectors, highlighting a divide between industry and academia. The paper delves into the unique challenges faced by non-academic engineering educators, such as confronting a culture that often questions their value outside traditional faculty roles and the overarching perception that non-research roles are less significant. Despite these challenges, the authors argue for the vital role these professionals play in bridging the gap between research, instruction, and practical application in engineering education. They emphasize the importance of ASEE or similar professional societies in recognizing and leveraging the diverse contributions of non-academic engineering educators to foster a more inclusive and supportive community. Key takeaways and recommendations include the necessity for ASEE and similar bodies to shift normative expectations, create inclusive and equitable environments, and actively value diverse career trajectories. The paper calls for actionable strategies to build more inclusive professional communities, create safe spaces for discussing career diversity, and establish stronger connections between current students and diverse alums. The overarching goal is to cultivate an environment where all forms of contribution to engineering education are valued, encouraging a broader spectrum of career considerations among graduates and professionals. The authors seek not only to share insights but also to galvanize a nascent community of like-minded engineering educators aspiring or working outside the traditional academic sphere.

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