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Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new policies strictly for addressing these new technologies, they should instead edit their existing academic integrity and intellectual property statements to incorporate explicit mentions of artificial intelligence.

Abstract Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.

Background: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research., Purpose: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms., Methods: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics., Results: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent., Conclusions: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat effectively, resulting in poor patient outcomes due to late-stage detection and limited therapeutic options. The emergence of biosensors has revolutionized cancer diagnosis and therapy, providing new avenues for early detection and personalized treatment. This paper explores the development and integration of biosensors within a unique expert system for pancreatic cancer diagnosis and drug delivery automation. It discusses the principles, types, and applications of biosensors in pancreatic cancer diagnosis, their role in automating drug delivery, and the design of an expert system that leverages these technologies to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

In this paper, we put in dialogue the local dimension of a nation-state with the global challenges faced by universities worldwide. We focus on the case of Denmark, a nation that was exceptionally active in implementing international university reforms and where digitalisation is a high priority of the public sector governance. The article seeks to contribute to speculative research and critical studies by presenting a mixed-methods study that explores higher education teachers' views about desirable and realistic future scenarios in Denmark. The study draws on data from a survey applied at a large Danish university, analysed both quantitatively (N = 755) and qualitatively (N = 53). The findings show that teachers share clusters of concern about uncontrolled digitalisation and teaching automation, commodification of education, and modularisation of university courses, the latter being a contested but realistic perspective in the latest political reforms. They reaffirm the mission of the university as preparing students for solving real problems and contributing to the challenges of the present time. The paper concludes with a call for university management to recalibrate future imaginaries to the values expressed by the teachers, and the university they wish to create.

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

Increased levels of digitalisation present major opportunities for efficiency in the oil and gas industry but can also contribute to new risks and vulnerabilities. Based on developments in the industry, the Norwegian Ocean Industry Authority (HAVTIL) has in recent years pursued targeted knowledge development and follow-up of company's digitalisation initiatives. This paper explores data collected through HAVTIL's audits of the development and use of automated systems within well operations. The analysis of the data resulted in the identification of five main topics related to the implementation of digital technologies. The five main topics were organisational complexity, follow-up and implementation of technology, analysis and documentation, user-interface and alarms and competence and training. Overall, the results support research findings within human factors and technology development, pointing out that there is a lack of focus on human factors in both development projects and in operations. In addition, this paper provides insight into how digitalisation initiatives are followed-up and explores the results from the analysis in light of the current developments in the industry.

Background: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive., Purpose: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease., Methods: A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[ 18 F]FDG and Na[ 18 F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values., Results: Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6-1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0-1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00-1.00] and 0.99 [0.93-1.00] for calcium and uptake values, respectively)., Conclusions: We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

This paper examines the feasibility of incorporating visual cueing systems within vehicles to mitigate the risk of experiencing motion sickness. The objective is to enhance passenger awareness and the ability to anticipate the forces associated with car travel motion. Through a comprehensive literature review, the findings demonstrate that visual cues can mitigate motion sickness for particular in-vehicle configurations, whereas their influence on situational awareness is not clear yet. Each type of visual cue proved more effective when presented in the peripheral field of view rather than solely in the central vision. Promising applications can be found within interactive screens and ambient lighting, while the use of extended reality shows potential for future investigations. In addition, integrating such systems into highly automated vehicles shows potential to improve their overall user acceptance.

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI. [ABSTRACT FROM AUTHOR]

This paper explores international students' engagement with educational technology for self-regulated English learning at an Australian university. Despite the increased use of automated feedback systems (AFSs) for language assessment, students' critical engagement with them for independent learning remains under-researched. The study primarily employed a qualitative approach to understand the students' preferred AFS tools and critical engagement throughout their personalised learning journeys but it also included a small-scale quantitative component. Data were gathered from seven students' e-portfolios, focus group interviews as well as a survey among 32 participants. Results highlight positive perceptions and successful use of AFSs, with students leveraging these tools to identify improvement areas, track progress and gain confidence. The study emphasises the importance of course structure, teacher guidance and a combination of human and automated feedback, in fostering learner autonomy and emotional self-regulation. The paper underscores the potential for sustained use of AFSs beyond the cours, and the significance of guiding learners to critically use these tools for ongoing learning and growth rather than dependence. These findings have significant implications, as readily available artificial intelligence tools such as ChatGPT hold great pedagogical potential for self-regulated learning within and beyond the language learning field.

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation, and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques' feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability, or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools' performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research.

It is well-known that digital learning materials influence the classroom curriculum and didactics. At the same time, few studies examine the role of the data visualizations offered by digital learning materials. Data visualizations signpost the emergence of students as data subjects who can be observed and compared on a computer screen. They thus shape teachers' socio-technical ways of seeing student learning and are likely to influence student assessments. Drawing inspiration from Science and Technology Studies (STS), this paper advances an analytical framework for eliciting the politics of data visualizations, focusing on automation, agencies, and aesthetics. The framework is exemplified through a case study of a popular Danish digital mathematics platform. In a concluding discussion, the paper considers the importance of addressing unsettled and ethical questions about the role of automated digital systems in teachers' knowledge practices.

Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully designed mathematical activities with interactive feedback were experimented with a sample of 299 students of grade 8 for a school year. A control group of 257 students learned the same topics using traditional methodologies. Part of the sample belonged to low SES. The learning achievement was assessed through pre-and post-tests to understand if the adoption of AFA impacted learning and whether the results depended on the students' SES. The results show a positive effect of the experimentation (effect size: 0.42). Moreover, the effect size of the experimentation restricted to the low-SES group is high (0.77). In the treatment group, the results do not depend on SES, while in the control group, they do, suggesting that AFA is an equitable approach while traditional instruction risks perpetuating inequalities.

Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research's sample size may restrict its findings' generalizability. Furthermore, the study's focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system's chatbots and AI features must be user-friendly, considering the customers' familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs' perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.

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.

Effective collaboration and teamwork skills are critical in high-risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real-world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.

Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings from an evaluation of collaboration quality estimation models. We collected audio, video and log data from two different Estonian schools. These data were used in different combinations to build collaboration estimation models and then assessed across different subjects, different types of activities (collaborative-writing, group-discussion) and different schools. Our results suggest that the automated collaboration model can generalize to the context of different schools but with a 25% degradation in balanced accuracy (from 82% to 57%). Moreover, the results also indicate that multimodality brings more performance improvement in the case of group-discussion-based activities than collaborative-writing-based activities. Further, our results suggest that the video data could be an alternative for understanding collaboration in authentic settings where higher-quality audio data cannot be collected due to contextual factors. The findings have implications for building automated collaboration estimation systems to assist teachers with monitoring their collaborative classrooms.

Computer-supported learning technologies are essential for conducting hands-on cybersecurity training. These technologies create environments that emulate a realistic IT infrastructure for the training. Within the environment, training participants use various software tools to perform offensive or defensive actions. Usage of these tools generates data that can be employed to support learning. This paper investigates innovative methods for leveraging the trainee data to provide automated feedback about the performed actions. We proposed and implemented feedback software with four modules that are based on analyzing command-line data captured during the training. The modules feature progress graphs, conformance analysis, activity timeline, and error analysis. Then, we performed field studies with 58 trainees who completed cybersecurity training, used the feedback modules, and rated them in a survey. Quantitative evaluation of responses from 45 trainees showed that the feedback is valuable and supports the training process, even though some features are not fine-tuned yet. The graph visualizations were perceived as the most understandable and useful. Qualitative evaluation of trainees' comments revealed specific aspects of feedback that can be improved. We publish the software as an open-source component of the KYPO Cyber Range Platform. Moreover, the principles of the automated feedback generalize to different learning contexts, such as operating systems, networking, databases, and other areas of computing. Our results contribute to applied research, the development of learning technologies, and the current teaching practice.

Hypothesis Descriptions are a type of manuscript dedicated to the formal description of a hypothesis, as introduced in an accompanying editorial and an examplary Hypothesis Description for the Enemy Release Hypothesis that is used in invasion biology. This questionnaire provides a template for such a Hypothesis Description manuscript. The template's format was designed for simplicity to facilitate adoption, and it can be easily extended to capture additional information, e.g. instructions for falsification or generalization, taxonomic or geographic scope, etymology, or relevant information in other research fields or other languages. The template reflects the recommended structure for a Hypothesis Description manuscript in that each of its sections provides the title for a section in a Hypothesis Description manuscript and indicates whether that section is mandatory or optional. Four sections - Keywords (mandatory), Conflicts of interest (optional), Acknowledgments (optional) and References (mandatory) - are in this template filled in for the template itself but should otherwise be adjusted for the hypothesis at hand. Comments to guide authors who work on a Hypothesis Description manuscript are provided as well. [ABSTRACT FROM AUTHOR]

The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy (Cognitive, Psychomotor and Affective) level is crucial to ensure Constructive Alignment at the fundamental level of a course and to ensure meaningful outcome measurements. Unfortunately, this effort is often subject to bias and human error while the use of information technologies as a mediator in this area remains unexplored. This research paper proposes an automatic learning-based advisory system for engineering curriculum to ensure constructive alignment with programme outcomes and educational taxonomy. We demonstrated the use of natural language processing and machine learning techniques to mitigate human error and bias that is often present in such classification tasks. Textual/semantic embeddings, including Term Frequency-Inverse Document Frequency (TF-IDF), Universal Sentence Encoder (USE), and Word2Vec (W2V), machine learning models (Random Forest, Support Vector Machine, Logistic Regression, and Light Gradient Boosting Machine), and their corresponding techniques for optimizing the training process are extensively investigated. In terms of accuracy, we obtained an encouraging result of 78.83%, and 78.71% for TF-IDF with Random Forest, and USE with Support Vector Machine classifier, respectively. We transformed our work into a web-based solution named the Course Outcomes Diagnostic Tool, embedded in the faculty education web platform, Edu Centre that is ubiquitously adopted by the members in the Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology. The proposed solution has demonstrated great potential in reducing subjectivity, ambiguity, and human error, thereby improving the constructive alignment at the root level of course design to ensures teaching-learning activities are aligned with regulatory body expectations.

Video is an increasingly important resource in higher education. A key limitation of lecture video is that it is fundamentally a sequential information stream. Quickly accessing the content aligned with specific learning objectives in a video recording of a classroom lecture is challenging. Recent research has enabled automatic reorganization of a lecture video into segments discussing different subtopics. This paper explores AI generation of visual and textual summaries of lecture video segments to improve navigation. A visual summary consists of a subset of images in the video segment that are considered the most unique and important by image analysis. A textual summary consists of a set of keywords selected from the screen text in the video segment by analyzing several factors including frequency, font size, time on screen, and existence in domain and language dictionaries. Evaluation was performed against keywords and summary images selected by human experts with the following results for the most relevant formulations. AI driven keyword selection yielded an F-1 score of 0.63 versus 0.26 for keywords sampled randomly from valid keyword candidates. AI driven visual summary yielded an F-1 score of 0.70 versus 0.59 for K-medoid clustering that is often employed for similar tasks. Surveys showed that 79% (72%) of the users agreed that a visual (textual) summary made a lecture video more useful. This framework is implemented in Videopoints, a real-world lecture video portal available to educational institutions.

Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students' learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students' performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students' academic performance. The results revealed that the dashboard significantly enhanced students' learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students' performance and assisted them in self-regulation

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (eg, GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models.

Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoherent answers. One option is to automate the review with Large Language Models (LLM). They have a powerful discursive ability that can be used to explain decisions. In this paper, we analyze the responses of fourth graders in mathematics using three LLMs: GPT-3, BLOOM, and YOU. We used them with zero, one, two, three and four shots. We compared their performance with the results of various classifiers trained with Machine Learning (ML). We found that LLMs perform worse than MLs in detecting incoherent answers. The difficulty seems to reside in recursive questions that contain both questions and answers, and in responses from students with typical fourth-grader misspellings. Upon closer examination, we have found that the ChatGPT model faces the same challenges.

This paper explores the following questions: What is reading all about, as our technical milieu becomes increasingly digital and our reading increasingly automated? What is entailed in closely reading a book, in studying and handling the book as an object? And what is the role of philosophy—and in reading philosophy—as we grapple with new technical modes of reading? Guided by philosopher Gilbert Simondon, this paper compares the language heuristics of large language models (LLM) with human reading practices, revealing parallel and diverging technical tactics, with the aim of increasing our understanding of how and why these algorithms are part of our technical reality. This comparison moves beyond concerns with automation and alienation, using Simondon's notions of technicity and transindividuality to philosophically analyze the nature of collaborative reading in a distraction economy, and the extent to which transformer neural network models achieve an implicit embodied or grounded sense of language-use. [ABSTRACT FROM AUTHOR]

Purpose: To support senior leaders and HR practitioners with building the internal leadership capabilities to oversee automation in a virtual teams environment. Design/methodology/approach: This point of view paper presents the topic of workplace automation in a virtual teams environment through contextual practitioner literature sources. Findings: Six new capabilities are suggested for the modern leader. Originality/value: This paper introduces a scarcely researched area which is of enormous relevance in the post-covid age of remote working and digital transformation agendas, alongside presenting recommendations for HR practitioners and senior leaders to build internal leadership capabilities. [ABSTRACT FROM AUTHOR]

Rapid diagnostic tests (RDTs) for point-of-care (POC) testing of infectious diseases are popular because they are easy to use. However, RDTs have limitations such as low sensitivity and qualitative responses that rely on subjective visual interpretation. Additionally, RDTs are made using paper-bound reagents, which leads to batch-to-batch variability, limited storage stability and detection of only the analytes they were designed for. This work presents the development of a versatile technology, based on short magneto-assays and inexpensive paper-based microfluidic electro-analytical devices (PMEDs). PMEDs were produced locally using low-cost equipment, they were stable at room temperature, easy to use, and provided quantitative and objective results. The devices served to detect alternatively a variety of magneto-assays, granting quantitation of streptavidin-HRP, biotinylated HRP and Pasmodium falciparum lactate dehydrogenase (Pf-LDH) in less than 25 min, using either commercial or customized screen-printed electrodes and measurement equipment. Furthermore, Pf-LDH detection in diluted lysed whole blood displayed a linear response between 3 and 25 ng mL−1, detection and quantification limits ranging between 1 and 3 ng mL−1 and 6–12 ng mL−1, respectively, and provided results that correlated with those of the reference ELISA. In short, this technology is versatile, simple, and highly cost-effective, making it perfect for POC testing. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze systems requirements and conceptualize designs to automate the traditional administrative functions of public universities in Bangladesh. Before implementing any software or website, system analysis and design are the core functions to formulate. In this paper, Requirement analysis is done with interview and observation, and conceptual design is done with Microsoft Visio software to identify a use case model, activity diagram, sequence diagram, and UML diagram to automate the administrative functions of Noakhali Science and Technology University situated in Bangladesh. The first two phases of the waterfall method are used in this paper to validate the result. The result shows the number of users of the systems, user requirements, and sequential tasks to automate administrative functions. [ABSTRACT FROM AUTHOR]

Labor shortage and low labor productivity are critical issues in the construction industry. Robotics, as a powerful technology to improve productivity in the digitization era, is being used to assist in addressing these problems. Particularly in recent years, interest in construction robotics research has risen remarkably. To gain a deeper understanding of this burgeoning research field, this study provides a bibliometric analysis of 498 related papers retrieved from WoS (Web of Science). Performance analysis and science mapping were used in the paper to identify the research trends, relationship among authors, top publication sources, regional activities, as well as knowledge base and dominant research sub-fields. The results indicated that the number of papers focusing on construction robotics has been continuously growing since 2013. "Automation," "system," and "design" have been the most addressed topics in construction robotics. The findings are indicative of identifying the deficiencies in existing research and provide directions for future research. Those include greater attention paid to the experimental results of on-site construction robots, while ignoring the impacts of practical construction situations, and the development of off-site construction robots needs more support. For theoretical research, there is a lack of studies on human–robot collaboration workflow and management models. Therefore, the study would be valuable in providing practitioners and researchers with a better perspective on the development of construction robotics and facilitating the building of the intellectual wealth of robotics in the construction industry. Our study analyzed 498 papers related to construction robotics from 1974 to 2022 through bibliometric approaches (mathematical and statistical methods). The study counted the numbers of construction robotics papers by year, identified most active scholars and top publication sources, and analyzed the relationship among regions. Most importantly, the paper identified the knowledge base of the research field of construction robotics and reviewed various relevant studies from theoretical and practical application perspectives. [ABSTRACT FROM AUTHOR]