2,437 research outputs found

    Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

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    Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

    Leveraging robotics to enhance accessibility and engagement in mathematics education for vision-impaired students

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    There is potential to use robotics in education to revolutionize teaching and learning in mathematics. This is particularly relevant for vision-impaired students, who face different challenges when accessing mathematical content. Educational robotics can potentially enhance accessibility, motivation, and engagement in mathematics for students through enjoyable and novel interactions. Students commonly experience positive interactions with educational robots during learning activities, which influences their learning motivation. Recent studies show that students with disabilities face issues related to classroom participation, lack of collaborative learning, reduced social engagement, and potential for isolation. Digital-based learning technologies have transformed how vision-impaired students engage with and learn mathematics. Leveraging robotics in mathematics teaching and learning through personalised guidelines offers considerable benefits for vision-impaired students, including enhanced engagement, multimodal learning opportunities, and improved collaboration and communication skills, which enhances the opportunities for inclusive classroom experiences. This paper outlines the role of educational robotics in inclusive education. It examines the challenges and benefits of using educational robotics in mathematics for vision-impaired students. The importance of human-robot interaction (HRI) in steering the design and functionality of educational robots and their potential use within the classroom to facilitate learning is also highlighted

    “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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    Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

    Mission Focus: Supporting executive functions in VLEs and the design of inclusive user interfaces.

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    Virtual learning environments (VLEs) use educational technologies to facilitate remote online learning in the absence of synchronous supervision and support. Most VLEs offer inclusive options for learners to access content at any time and to adapt content into a form suiting their interaction modes. They also facilitate online collaboration and peer communication. However, they do not fully consider the needs of pre-literate adolescents with developing executive functioning for engaging in asynchronous learning, resulting in barriers. Through an exploratory-cum-participatory research approach combined with a collaborative and iterative co-design process with the participants, this study explored and examined barriers to independent and asynchronous functions that pre-literate adolescent learners face when learning in a VLE, such as planning, focus, and setting and achieving goals (executive functions). Building on principles for user interface design, guidelines were developed to help enhance the design of VLEs to make them more inclusive of diverse executive functioning needs

    Machine Learning Clustering Analysis Towards Educator’s Readiness to Adopt Augmented Reality as a Teaching Tool

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    The advanced digital revolution has shifted conventional teaching and learning into digital education. In consistency with digital education, Augmented Reality (AR) applications started to shine in the education industry for their ability to create conducive teaching and learning environments, especially in remote learning during the COVID-19 pandemic. Movement Control Order (MCO) implemented in the year 2020 has led to emergency remote teaching and learning without much preparation for all educators and learners. Throughout these few years, most educators got familiar with digital teaching tools and online teaching platforms. Hence, this study aims to explore educators’ readiness to adopt AR as a teaching tool in their teaching during the endemic period. A quantitative approach via questionnaire has been distributed to the Private Higher Education Institutions (PHEIs) in the states of Selangor and Kuala Lumpur. Machine learning using a clustering technique was used to find patterns between the demographics of educators towards the AR perception of educators. The results revealed that educators' perceptions of AR technology are influenced by their familiarity with it, their personal beliefs, and their attitudes toward technology. This study provides an insightful overview of the benefits of AR applications in education and the implications of the adoption of AR in Malaysian schools and educational institutions. It also highlights the importance of motivating educators and students to embrace AR as an enhancement learning tool, providing a valuable discussion for the government, learning institutions, and educators on the implementation of AR in Malaysia

    Thematic Working Group 3 - Inclusion of Excluded Populations : Access and Learning Optimization via IT in the Post-Pandemic Era

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    Thematic Working Group (TWG) 3’s theme is “Inclusion of excluded populations: access and learning optimization via IT in the post-pandemic era”. A focal concern is established by the presence of the first word – ‘inclusion’ – and how this relates to ‘excluded populations’. Much of the research in this field has focused on inclusion for individuals; however, the evidence shows that educational exclusion has multiple dimensions (Passey, 2014). To accommodate this within the current focus, therefore, identifying key dimensions of ‘excluded populations’ will be a key concern of this document. ‘Access’ will be considered beyond physical technology access, involving aspects of accessibility, agency and empowerment. These aspects relate to a definition of access that concerns the needs for individuals to develop and have digital capabilities and abilities to select applications appropriate to purpose, as discussed, for example, by Helsper (2021) and Passey et al. (2018). Taking this wider concern for access, ‘learning optimization’ will be explored as a term that highlights the need to focus on technological access and provision enabling successful outcomes. Given the fact that the intention of the work of TWG3 is to explore findings in the ‘post-pandemic’ context, communication technologies as well as just information technology, ‘IT’, are clearly important and need to be considered. Additionally, exclusion factors to be addressed need to be clearly identified so that inclusion can be accommodated and ensured in the context of specific excluded populations. However, inclusion should not be implemented as an imposition in the context of digital technologies, as some populations do not wish to use digital technologies (Wetmore, 2007), and in this respect the issue of the need to acknowledge diversity is important

    A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

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    People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video
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