In the current digital era, the proliferation of online content has overwhelmed users with vast amounts of information, necessitating effective filtering mechanisms. Recommender systems have become indispensable in addressing this challenge, tailoring content to individual preferences and significantly enhancing user experience. This paper delves into the latest advancements in recommender systems, analyzing 115 research papers and 10 articles, and dissecting their application across various domains such as e-commerce, entertainment, and social media. We categorize these systems into content-based, collaborative, and hybrid approaches, scrutinizing their methodologies and performance. Despite their transformative impact, recommender systems grapple with persistent issues like scalability, cold-start problems, and data sparsity. Our comprehensive review not only maps the current landscape of recommender system research but also identifies critical gaps and future directions. By offering a detailed analysis of datasets, simulation platforms, and evaluation metrics, we provide a robust foundation for developing next-generation recommender systems poised to deliver more accurate, efficient, and personalized user experiences, inspiring innovative solutions to drive forward the evolution of recommender technology.
Category: Sector: Inclusion
Hard Questions: Inclusion
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Unravelling socio-technological barriers to AI integration: A qualitative study of Southern African newsrooms
This study explores the socio-technological barriers to the adoption of artificial intelligence (AI)-powered solutions in three countries of the global south – South Africa, Lesotho, Eswatini, Botswana and Zimbabwe. Through 20 in-depth interviews with key stakeholders, it examines the distribution and circulation of AI technologies within selected newsrooms. Furthermore, the article explores socio-technological obstacles to the integration of AI among journalists. Lastly, it examines the consequences of these socio-technological obstacles to journalism. The article specifically seeks to answer three questions: How are AI technologies integrated in southern African newsrooms? What are the socio-technological barriers attendant to the use of AI in selected news organisations of sub-Saharan Africa? What are the implications of these socio-technological barriers to the process of news production in these newsrooms?
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Skills Lab Panel “Building Bridges”, for the Culture and Cohesion Summit.
Join Victoria Kuketz for an intercultural Skills Lab Panel “Building Bridges”, for the Culture and Cohesion Summit.
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‘We Condemn the Sin, not the Sinner’: Understanding the Attitudes of Adventist and Baptist Believers in Montenegro towards Sexuality
Although we have seen studies on religion and sexuality in the West come to dominate the relevant scholarship, the postsocialist environment with a predominantly Orthodox population has received less attention. This paper fills the gap by examining the attitudes of Adventist and Baptist believers in Montenegro, with a particular focus on abortion, premarital sexual activities and non-heteronormative sexuality. Because we were dealing with a minority population, we opted for a qualitative methodology and conducted semistructured interviews with 17 Adventist and 15 Baptist believers. As observed, our research participants’ position towards sexuality is closely related to the teachings found in the Bible. However, it is possible to observe certain differences between the two groups; for example, the interviewed Adventists tend to be more conservative than the interviewed Baptists. In addition, the male Adventists seem slightly more liberal than female Adventists, whereas the situation is different among Baptists, where women come across as more liberal than men.
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OECD Gender Equality in Technology Governance
Director Mackenzie represented the Aula Fellowship and the AI context in conversations at this global conference for equity. We stand together, or we fall.
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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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A Review of AI-Enhanced Personalized Learning Systems: Implications for the Learning Sciences
This research focuses on recent studies of AI-Enhanced Personalized Learning, organized into three main sections: understanding key aspects, investigating practical methodologies, and elucidating motivations for AI integration into personalized learning to provide insights for future research in learning science. The methodology involves a rapid literature review, emphasizing eligibility criteria and a precise study selection process. The conclusion underscores the importance of seamlessly integrating AI analytics with humancentric approaches in personalized learning, enriching data, and training algorithms for efficiency, alongside emphasizing the role of human oversight.
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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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Beyond the algorithm: Empowering ai practitioners through liberal education
As AI technology continues to transform society, there is a growing need for engineers and technologists to develop interdisciplinary skills to address complex, society-wide problems. However, there is a gap in understanding how to effectively design and deliver inter-disciplinary education programs for AI-related training. This paper addresses this gap by reporting on a successful summer school program that brought together specialists from around the world to engage in deliberations on responsible AI, as part of a Summer School in Responsible AI led by Mila – Quebec Artificial Intelligence Institute. Through deep dive auto-ethnographic reflections from five individuals, who were either organizers or participants, augmented with end-of-program feedback, we provide a rich description of the program’s planning, activities, and impact. Specifically, our study draws from engineering education research, bridging the gap between research and practice to answer three research questions related to the program: (1) How did the program design enable a more effective understanding of interdisciplinary problem-sets? (2) How did participants experience the interdisciplinary work of the program? (3) Did the program affect participants’ impact on interdisciplinary problem-sets after the program? Our findings highlight the benefits of interdisciplinary, holistic, and hands-on approaches to AI education and provide insights for fellow engineering education researchers on how to design effective programs in this field.
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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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World AI: Women in AI
We organized with Women in AI for this 2024 World AI group photo on the main stage. Thank you to everyone who participated!
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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.