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In postsecondary education institutions, where innovative technologies continually reshape research and pedagogical approaches, the integration of generative artificial intelligence (GenAI) tools presents promising avenues for enhancing student learning experiences. This study assesses the efficacy of integrating GenAI tools, specifically chatbots, to ameliorate learning outcomes and mitigate test anxiety among postsecondary students. Within the context of an introductory psychology course, 40 participants engaged with chatbots as supplementary "study confidantes" for exam preparation and as facilitators for an essay grading task. The results from surveys administered to the participants indicated that most students found the chatbots helpful in aiding comprehension of course materials. Moreover, a substantial proportion of respondents reported an enhanced understanding of how to structure an academic paper because of their essay grading activity. Despite the favourable perception of chatbot assistance with learning course material and fostering insights into structuring scholarly essays, no discernible alterations in the levels of test anxiety among students were observed. Overall, this research underscores the latent potential of chatbots as pedagogical adjuncts, furnishing instructive insights for educators aiming to innovate instructional methodologies and optimize student learning paradigms within the domain of postsecondary education.

Despite existing research on AI applications in education (AIEd), the release of ChatGPT has disrupted the status quo in the educational landscape. Although this technology can personalize learning, decrease teacher workload, and offer access to a wealth of information, concerns around generative AI (GenAI) tools have emerged, including academic integrity, data accuracy, and bias in information. Given research highlights and acknowledging educators' varied levels of awareness and conflicting views toward AIEd, two teacher educators (also authors of this paper) in the Faculty of Education at Brock University facilitated three workshops among different groups of teacher educators. The workshops focused on the emerging nature of GenAI tools, their affordances, and their implications for educators' practices. Adopting a narrative inquiry approach, the authors describe the details of these workshops and present their reflections on the process of preparing for and facilitating them. Implications for teacher education research and practice are also presented and discussed.

Academic integrity concerns related to students' use of technology have renewed calls for teaching, assessment, and learning best practices, including those that involve and empower students. Empowerment is a benefit of developing students' assessment literacies, or how students contextually understand, plan, and undertake assessment and use assessment information to monitor and progress their learning. Informed by Bandura's (1986) social cognitive theory and reflexivity (Dewey, 1933; Schön, 1983), a qualitative exploratory case study examined first-year university students' experiences with assessment and the development of their assessment literacies. The findings highlighted student autonomy and empowerment benefits while stressing the importance of reflexivity and assessment literacies for both students and teachers. Teaching, assessment, and learning best practices commonly suggested to promote academic honesty in the GenAI context were also evident. Accordingly, this paper explores the role of students' assessment literacies as part of these best practices, with implications for all levels of education.

Artificial intelligence (AI) and automation are newly impacting the governance of international students, a temporary resident category significant for both direct economic contributions and the formation of a "pool" of potential future immigrants in many immigrant-dependent countries. This paper focuses on tensions within Canada's education-migration ("edugration") system as new technologies intersect with migration regimes, which in turn relate to broader issues of security, administrative burdens, migration governance, and border imperialism. Using an Accidental Ethnography (AccE) approach drawing from practitioner-based legal research, we discuss three themes: (1) "bots at the gate" and the guise of AI's objectivity; (2) a murky international edu-tech industry; and (3) the administrative burdens of digitalized application systems. We suggest that researchers, particularly in education, can benefit from the insights of immigration practitioners who often become aware of potential trends before those less embedded in the everyday negotiation of migration governance.

Purpose: This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice. Design/methodology/approach: A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively. Findings: The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain. Research limitations/implications: If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation. Practical implications: While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks. Social implications: As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students. Originality/value: The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

For over two decades now, the application of Citizen Science to Education has been evolving, and fundamental topics, such as the drivers of motivation to participate in Citizen Science projects, are still under discussion. Some recent developments, though, like the use of Artificial Intelligence to support data collection and validation, seem to point to a clear-cut divergence from the mainstream research path. The objective of this paper is to summarise the development trajectory of research on Citizen Science in Education so far, and then shed light on its future development, to help researchers direct their efforts towards the most promising open questions in this field. We achieved these objectives by using the lens of the Affordance-Actualisation theory and the Main Path Analysis method.

Research on Artificial Intelligence, especially in the field of machine learning, has exploded in the twenty-first century. AI research in universities has long been funded by a combination of government and corporate sources. The funding of AI research in the contemporary university includes technology companies as both funders and generators of research areas. This paper looks at the links between technology companies and AI research in three areas: first, the ways in which technology companies influence both the content and practices of AI research in universities; second, how university research policies enable conditions that blur traditional boundaries between corporate and academic AI research; and third, how an ethos of 'open science', that is increasingly corporatised, moves ideas about AI from universities to companies. We conclude that technology companies influence AI research within established feedback loops in the transformed relationships between economy, society, research, and the contemporary university.

Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship. Given the increasing popularity of AI and machine learning in particular, we face growing demand for educational offerings in this area from our students. This paper describes two courses that introduce machine learning concepts to undergraduate, predominantly industrial engineering and operations research students. Instead of taking a methods-first approach, these courses use real-world applications to motivate, introduce, and explore these machine learning techniques and highlight meaningful overlap with operations research. Significant hands-on coding experience is used to build student proficiency with the techniques. Student feedback indicates that these courses have greatly increased student interest in machine learning and appreciation of the real-world impact that analytics can have and helped students develop practical skills that they can apply. We believe that similar application-driven courses that connect machine learning and operations research would be valuable additions to undergraduate OR curricula broadly.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper proposes a structured, tiered framework through a UNCITRAL Model Law to gradually integrate artificial intelligence (AI) into foreign investment law arbitration in an ethical and effective manner. It also explores the critical role arbitration institutions can play in facilitating AI implementation. It details how AI can assist arbitrators by searching vast datasets, automating routine tasks, and enhancing decision-making through analysis of previous cases. Current AI regulations in regions like the EU, UK, and Canada fall short in addressing the complexities of cross-border arbitration and ensuring interoperability. By emphasising the distinct contributions of the proposed UNCITRAL Model Law and arbitration institutions, the paper highlights a multi-faceted strategy to overcome challenges posed by outdated international conventions, inconsistencies in bilateral investment treaties, and the lack of comprehensive guidance. This approach aims to refine the integration of AI in arbitration processes, enhancing efficiency, fairness, and the legitimacy of the arbitration system. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

The paper explores the potential of Large Language Models to substitute for or to augment human participants in market research. This paper explores the potential of large language models (LLMs) to substitute for human participants in market research. Such LLMs can be used to generate text given a prompt. We argue that perceptual analysis is a particularly promising use case for such automated market research for certain product categories. The proposed new method generates outputs that closely match those generated from human surveys: agreement rates between human- and LLM- generated data sets reach over 75%. Moreover, this applies for perceptual analysis based on both brand similarity measures and product attribute ratings. The paper demonstrates that, for some categories, this new method of fully or partially automated market research will increase the efficiency of market research by meaningfully speeding up the process and potentially reducing the cost. Further results also suggest that with an ever larger training corpus applied to large language models, LLM-based market research will be applicable to answer more nuanced questions based on demographic variables or contextual variation that would be prohibitively expensive or infeasible with human respondents. History: Catherine Tucker served as the senior editor. This paper was accepted through the Marketing Science: Frontiers review process. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 430-2021-00057]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0454. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data, thereby supporting accurate "end-to-end learning" in industrial fault classification applications. Moreover, the generated polygons can play a significant role in the interpretation of trained deep learning fault classifiers. The performance of the proposed method was demonstrated using a benchmark dataset in the process industry. It was also tested successfully to classify challenging faults in major equipment in a thermomechanical pulp mill located in Canada. The results of the proposed method show better performance than other comparable fault classifiers. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Law Library Review is the property of Canadian Association of Law Libraries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence (AI) and automation are newly impacting the governance of international students, a temporary resident category significant for both direct economic contributions and the formation of a 'pool' of potential future immigrants in many immigrant-dependent countries. This paper focuses on tensions within Canada's education-migration ('edugration') system as new technologies intersect with migration regimes, which in turn relate to broader issues of security, administrative burdens, migration governance, and border imperialism. Using an Accidental Ethnography (AccE) approach drawing from practitioner-based legal research, we discuss three themes: (1) 'bots at the gate' and the guise of AI's objectivity; (2) a murky international edu-tech industry; and (3) the administrative burdens of digitalized application systems. We suggest that researchers, particularly in education, can benefit from the insights of immigration practitioners who often become aware of potential trends before those less embedded in the everyday negotiation of migration governance. [ABSTRACT FROM AUTHOR]

Health workforce planning has become a significant global problem considering there are estimates of an 18 million healthcare provider shortfall by 2030. There are two mechanisms to address healthcare worker shortages: (1) domestic education of those professions and (2) integration of internationally educated health professionals. Integration of internationally educated health professionals into the Canadian healthcare system requires: (1) reductions in systemic and administrative barriers and (2) development, testing, and implementation of credential equivalency recognition systems. The goal of this scoping review was to identify systems that are employed to determine credential equivalency, with a focus on Canada. The scoping review was carried by employing: (1) a systematic literature search (9) and (2) a website and grey literature Google search of professional governing bodies from a selection of medical/allied healthcare professions, but also other non-medical professions, such as law, engineering and accounting. Seven databases were searched to identify relevant sources: MEDLINE, CINAHL Plus with Full Text, PsycINFO, SPORT Discus, Academic Search Complete, Business Source Complete, and SCOPUS. The search strategy combined keyword, text terms, and medical subject headings (MeSH) and was carried out with the help of a health sciences librarian. Seven articles were included in the final manuscript review from the following professions: nursing; psychology; engineering; pharmacy; and multiple health professions. Twenty-four health-related professional governing body websites were hand searched to determine systems to evaluate international equivalency. There were many systems employed to determine equivalency, but there were no systems that were automated or that employed machine-learning or artificial intelligence to guide the evaluation process. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Law Library Review is the property of Canadian Association of Law Libraries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial Intelligence (AI) offers the promise of improving natural resource governance, including addressing bribery and corruption risks. The mobilisation of computing power requires access to large amounts of data, a task facilitated by disclosure instruments. This paper examines the rationale and potential of artificial intelligence and the Extractive Industries Transparency Initiative (EITI) as anti-corruption tools, with a focus on extractive companies based in Canada. The paper concludes that the integration of AI and the EITI Standard holds some promise to curtail corruption in extractive sectors, despite some ethical, legal and practical challenges. [ABSTRACT FROM AUTHOR]

This study examines through a systematic review, the reference services rendered in academic libraries using artificial intelligence (AI) and by collecting data through environmental scanning. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring the application of artificial intelligence for reference services in academic libraries. Data were collected from Web of Science, Scopus, and LISA databases. Following the rigorous/established selection process, a total of thirty five articles were finally selected, reviewed and analyzed. Thirty five papers were identified, analyzed and summarized on the subject relating to the application of AI and the methods which are most often used. The findings demonstrate that university libraries in Canada and China are leading in the deployment of AI for reference services. The AI techniques used mostly in the scanned university libraries are self-directed learning and natural language processing techniques; while the challenges of using AI for reference services are the problem of quality intelligence, linguistic style, privacy, a threat to intellectual freedom, bias, and cost; inadequate experts, poor network, poor training and lack of innovation, and limited knowledge about the technology. The study indicates university libraries take into account implementing AI for reference services. [ABSTRACT FROM AUTHOR]

The emergence of new industries that are not closely related to existing regional paths remains an underexplained process in evolutionary economic geography. This paper responds to this gap through a case study of a maturing ecosystem of activity related to artificial intelligence in Montreal, Canada. Conceptually it brings together recent thinking in economic geography about agency in path development with complementary concepts from the literature on technological innovation systems. The empirical findings demonstrate the role of multiple agents in system-building and legitimation activities that have varied across pre-formative and formative phases of new path development in this analytical knowledge field. [ABSTRACT FROM AUTHOR]

Chatbots are "computer agents that can interact with the user" in a way that feels like human-to-human conversation.1 While the use of chatbots for reference service in academic libraries is a topic of interest for both library professionals and researchers, little is known about how they are used in library reference service, especially in academic libraries in Canada. This article aims to fill this gap by conducting a web-based survey of 106 academic library websites in Canada and analyzing the prevalence and characteristics of chatbot and live chat services offered by these libraries. The authors found that only two libraries were using chatbots for reference service. For live chat services, the authors found that 78 libraries provided this service. The article discusses possible reasons for the low adoption of chatbots in academic libraries, such as accessibility, privacy, cost, and professional identity issues. The article also provides a case study of the authors' institution, the University of Calgary, which integrated a chatbot service in 2021. The article concludes with suggestions for future research on chatbot use in libraries. [ABSTRACT FROM AUTHOR]

The aging population in Canada has been increasing continuously throughout the past decades. Amongst this demographic, around 11% suffer from some form of cognitive decline. While diagnosis through traditional means (i.e., Magnetic Resonance Imagings (MRIs), positron emission tomography (PET) scans, cognitive assessments, etc.) has been successful at detecting this decline, there remains unexplored measures of cognitive health that could reduce stress and cost for the elderly population, including approaches for early detection and preventive methods. Such efforts could additionally contribute to reducing the pressure and stress on the Canadian healthcare system, as well as improve the quality of life of the elderly population. Previous evidence has demonstrated emotional facial expressions being altered in individuals with various cognitive conditions such as dementias, mild cognitive impairment, and geriatric depression. This review highlights the commonalities among these cognitive health conditions, and research behind the contactless assessment methods to monitor the health and cognitive well-being of the elderly population through emotion expression. The contactless detection approach covered by this review includes automated facial expression analysis (AFEA), electroencephalogram (EEG) technologies and heart rate variability (HRV). In conclusion, a discussion of the potentials of the existing technologies and future direction of a novel assessment design through fusion of AFEA, EEG and HRV measures to increase detection of cognitive decline in a contactless and remote manner will be presented. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Pathology is the property of Canadian Association of Pathologists and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The demand for high-performance graphics systems used for artificial intelligence, cloud game, and virtual reality continues to grow; this trend requires graphics systems to achieve ever higher bandwidths. This article proposes a GDDR6 dynamic random access memory (DRAM) with a half-rate clocking architecture and optimized receiver and transmitter to improve high-speed operation. Furthermore, this article adopts a staggered PAD using the redistribution layer (RDL) to reduce the distance to four PADs; it enables the mitigation of bandwidth limitation of half-rate clocking, a lower phase mismatch, and a reduced propagation delay. The proposed half-rate clocking-based GDDR6 DRAM achieves 24 Gb/s/pin on a 1.35-V DRAM process. Also, the power-supply-induced-jitter (PSIJ) value is improved from 9.97 to 3.22 ps, compared to a GDDR6 design using a quarter-rate clocking. In addition, the phase mismatch of the proposed clock distribution network (CDN) is reduced compared to the conventional CDN, resulting in an improvement of the 3- $\sigma $ value of the phase skew from 4.16 to 2.25 ps. [ABSTRACT FROM AUTHOR]

This article examines decision-making in Federal Court of Canada immigration law applications for stays of removal, focusing on how the rates at which stays are granted depend on which justice decides the case. The article deploys a form of computational natural language processing, using a large-language model machine learning process (GPT-3) to extract data from online Federal Court dockets. The article reviews patterns in outcomes in thousands of stay of removal applications identified through this process and reveals a wide range in stay grant rates across many justices. The article argues that the Federal Court should take measures to encourage more consistency in stay decision-making and cautions against relying heavily on stays of removal to ensure that deportation complies with constitutional procedural justice protections. The article is also a demonstration of how machine learning can be used to pursue empirical legal research projects that would have been cost prohibitive or technically challenging only a few years ago--and shows how technology that is increasingly used to enhance the power of the state at the expense of marginalized migrants can instead be used to scrutinize legal decision-making in the immigration law field, hopefully in ways that enhance the rights of migrants. The article also contributes to the broader field of computational legal research in Canada by making available to other non-commercial researchers the code used for the project, as well as a dataset of several thousand Federal Court dockets that can be used for future research. [ABSTRACT FROM AUTHOR]

• Develops a structure-agency analytical framework to examine the trajectories of emerging technologies in different national contexts. • Compares AI development and diffusion between a developed country and an emerging country based on 125 in-depth interviews. • Argues that the levels of institutionalisation exert different structural forces and leave different spaces for institutional work. • Yields practical implications for developing countries to address the trade-off between the opportunities and risks brought by emerging technologies. Development studies have paid less attention to the role of technological innovations and we are yet to understand how, and more importantly why, technological trajectories differ across countries. This gap becomes sharper as emerging technologies such as artificial intelligence (AI) are becoming increasingly important in addressing many world development challenges. Drawing on insights from institutional work literature, this paper develops a structure-agency interplay framework to unravel the various trajectories of emerging technologies at the national level and examines the development and diffusion of AI in Canada and China. The findings show that Canada's stable institutional environment, reinforced through institutional work by various actors, generated a national AI trajectory driven by technology development through a strong focus on scientific research and ethics, with slower organic commercialisation of AI. In China, a dynamic and loose institutional structure characterised by lax regulations, low entry barriers, and high openness to novelties has resulted in a market-driven AI trajectory focused on technology commercialisation, with domestic digital giants and the government as dominant players. National-level dynamics in formal institutions, informal institutions, technologies, and actor strategies determine heterogeneous approaches to technology development and diffusion, giving rise to varieties of technological trajectories. The levels of institutionalisation exert different forces and create different spaces for institutional work across different geographical contexts. [ABSTRACT FROM AUTHOR]

Rockfall constitutes a major threat to the safety and sustainability of transport corridors bordered by rocky cliffs. This research introduces a new approach to rockfall susceptibility modeling for the identification of potential rockfall source zones. This is achieved by developing a data-driven model to assess the local slope morphological attributes with respect to the rock slope evolution processes. The ability to address "where" a rockfall is more likely to occur via the analysis of historical event inventories with respect to terrain attributes and to define the probability of a given area producing a rockfall is a critical advance toward effective transport corridor management. The availability of high-quality digital volumetric change detection products permits new developments in rockfall assessment and prediction. We explore the potential of simulating the conceptualization of slope-scale rockfall susceptibility modeling using computer power and artificial intelligence (AI). We employ advanced 3D computer vision algorithms for analyzing point clouds to interpret high-resolution digital observations capturing the rock slope evolution via long-term, LiDAR-based 3D differencing. The approach has been developed and tested on data from three rock slopes: two in Canada and one in the UK. The results indicate clear potential for AI advances to develop local susceptibility indicators from local geometry and learning from recent rockfall activity. The resultant models produce slope-wide rockfall susceptibility maps in high resolution, producing up to 75% agreement with validated occurrences. [ABSTRACT FROM AUTHOR]

Residential load control focuses on reducing both the monthly electricity expense and the peak demand for electricity. The efficient scheduling of smart home appliances' operational procedures can accomplish both goals. Because rescheduling appliances to reduce one goal can lead to an increase in the other, these two goals are inherently in conflict with one another. This work proposes an algorithm utilizing artificial intelligence methods to accomplish both goals simultaneously. The proposed method has been successfully tested on real data of energy dynamic pricing options applicable in two utilities, namely Alectra Utilities Corp., Canada, and ComEd Northern Illinois Power Company, serving residential consumers with varying monthly power use. It is also proposed that both utilities use a cost function that is based on real-time prices to mitigate the risks associated with real-time pricing. In addition, this research proposes a novel method for determining the absolute maximum hourly power usage. The suggested algorithm accomplishes its dual goals at once as proof of its efficacy in solving optimization problems. [ABSTRACT FROM AUTHOR]

Data‐driven techniques are used extensively for hydrologic time‐series prediction. We created various data‐driven models (DDMs) based on machine learning: long short‐term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow‐coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow‐coverage (S‐C), precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine‐recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow‐coverage dataset improves the models' prediction accuracies in the snowmelt‐dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high‐volume data processing. [ABSTRACT FROM AUTHOR]

Background: With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like. Objective: To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them. Design: 12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques. Setting: Virtual sessions. Participants: Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders Results: The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants. Limitations: Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective. Conclusions: These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped. [ABSTRACT FROM AUTHOR]

The distributed measured data in large regions and remote locations, along with a need to estimate climatic data for point sites where no data have been recorded, has encouraged the implementation of spatial interpolation techniques. Recently, the increasing use of artificial intelligence has become a promising alternative to conventional deterministic algorithms for spatial interpolation. The present study aims to evaluate some machine learning-based algorithms against conventional strategies for interpolating soil temperature data from a region in southeast Canada with an area of 1000 km by 550 km. The radial basis function neural networks (RBFN) and the deep learning approach were used to estimate soil temperature along a railroad after the spline deterministic spatial interpolation method failed to interpolate gridded soil temperature data on the desired locations. The spline method showed weaknesses in interpolating soil temperature data in areas with sudden changes. This limitation did not improve even by increasing the spline nonlinearity. Although both radial basis function neural networks and the deep learning approach had successful performances in interpolating soil temperature data even in sharp transition areas, deep learning outperformed the former method with a normalized RMSE of 9.0% against 16.2% and an R-squared of 89.2% against 53.8%. This finding was confirmed in the same investigation on soil water content. [ABSTRACT FROM AUTHOR]

In the United States and Canada, the legal framework for defining and protecting intellectual property (IP) rights has largely fallen under copyright, trademark, and patent legislation. Traditionally, the requisite requirement for IP protection was that a human contributed to creating the IP. However, the advent of artificial intelligence (AI) disrupts the existing IP frameworks. AI and machine learning systems create original IP by analyzing vast quantities of data. From there, data sets are combined, forming new IP. Without a human's contribution to the final product, the existing IP legal frameworks are inadequate to provide copyright protection to AI-generated creativity, trademark protection to AI-driven brand development, or patent protection to AI-built inventions. The gaps in legislation thus leaves room for uncertainty and exploitation of the IP. Without a legal framework, courts lack power to render consistent interpretations in the event of conflict resolution. The lack of reliable enforcement can thus impact ethical application of IP, since accountability safeguards are non-existent. [ABSTRACT FROM AUTHOR]

Copyright of Desafíos is the property of Colegio Mayor de Nuestra Senora del Rosario and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Emerging artificial intelligence (AI) technologies have diverse applications in medicine. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study examines undergraduate medical students' perceptions of AI, educational opportunities about of AI in medicine, and the desired medium for AI curriculum delivery. Methods: A 32 question survey for undergraduate medical students was distributed from May–October 2021 to students to all 17 Canadian medical schools. The survey assessed the currently available learning opportunities about AI, the perceived need for learning opportunities about AI, and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale survey questions were scored from 1 (disagree) to 5 (agree). Interview transcripts were analyzed using qualitative thematic analysis. Results: We received 486 responses from 17 of 17 medical schools (roughly 5% of Canadian undergraduate medical students). The mean age of respondents was 25.34, with 45% being in their first year of medical school, 27% in their 2nd year, 15% in their 3rd year, and 10% in their 4th year. Respondents agreed that AI applications in medicine would become common in the future (94% agree) and would improve medicine (84% agree Further, respondents agreed that they would need to use and understand AI during their medical careers (73% agree; 68% agree), and that AI should be formally taught in medical education (67% agree). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (85% disagree) and that AI-related learning opportunities were inadequate (74% disagree). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum. Conclusion: A lack of educational opportunities about AI in medicine were identified across Canada in the participating students. As AI tools are currently progressing towards clinical implementation and there is currently a lack of educational opportunities about AI in medicine, AI should be considered for inclusion in formal medical curriculum. [ABSTRACT FROM AUTHOR]

The article reports that the Canadian government introduced Bill C‑27, sponsored by the Minister of Innovation, Science and Industry, to update Canada's federal privacy legal landscape. Topics include earthshaking as that legislation was to the privacy regime in Canada; and Bill C‑27 also introduced the Artificial Intelligence and Data Act (AIDA), which aims to introduce regulations in Canada regarding the design, development, and use of artificial intelligence (AI) systems.

Background: Critical care research in Canada is conducted primarily in academically-affiliated intensive care units with established research infrastructure, including research coordinators (RCs). Recently, efforts have been made to engage community hospital ICUs in research albeit with barriers. Automation or artificial intelligence (AI) could aid the performance of routine research tasks. It is unclear which research study processes might be improved through AI automation. Methods:We conducted a cross-sectional survey of Canadian ICU research personnel. The survey contained items characterizing opinions regarding research processes that may be amenable to AI automation. We distributed the questionnaire via email distribution lists of 3 Canadian research societies. Open-ended questions were analyzed using a thematic content analysis approach. Results: A total of 49 survey responses were received (response rate: 8%). Tasks that respondents felt were time-consuming/tedious/tiresome included: screening for potentially eligible patients (74%), inputting data into case report forms (65%), and preparing internal tracking logs (53%). Tasks that respondents felt could be performed by AI automation included: screening for eligible patients (59%), inputting data into case report forms (55%), preparing internal tracking logs (51%), and randomizing patients into studies (45%). Open-ended questions identified enthusiasm for AI automation to improve information accuracy and efficiency while freeing up RCs to perform tasks that require human interaction. This enthusiasm was tempered by the need for proper AI education and oversight. Conclusions: There were balanced supportive (increased efficiency and re-allocation of tasks) and challenges (informational accuracy and oversight) with regards to AI automation in ICU research. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Bioethics / Revue canadienne de bioéthique is the property of Ecole de Sante Publique de l'Universite de Montreal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Prognosis of Alzheimer's disease (AD) progression has been recognized as a challenging problem due to the massive numbers of cognitive, and pathological features recorded for patients and controls. While there have been many studies investigated the diagnosis of dementia using pathological characteristics, predicting the advancement of the disease using cognitive elements has not been heavily studied particularly using technologies like artificial intelligence and machine learning. This research aims at evaluating items of the Alzheimer's Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13) test to determine key cognitive items that influence the progression of AD. A methodology that consists of machine learning and feature selection (FS) techniques was designed, implemented, and then tested against real data observations (cases and controls) of the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository with a narrow scope on cognitive items of the ADAS-Cog-13 test. Results obtained by ten-fold cross validation and using dissimilar classification and FS techniques revealed that the decision tree algorithm produced classification models with the best performing results from the cognitive items. For ADAS-Cog-13 test, memory and learning features including word recall, delayed word recall and word recognition were the key items pinpointing to AD advancement. When these three cognitive items are processed excluding demographics by C4.5 algorithm the models derived showed 82.90% accuracy, 87.60% sensitivity and 78.20% specificity. [ABSTRACT FROM AUTHOR]

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. [ABSTRACT FROM AUTHOR]