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An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

arXiv.org Artificial Intelligence

However, up until the COVID-19 pandemic caused a seismic shift in the education sector, few educational institutions had fully developed digital learning models in place and adoption of digital models was ad-hoc or only partially integrated alongside traditional teaching modes [1]. In the wake of the disruptive impact of the pandemic, the education sector and more importantly educators have had to move rapidly to take up digital solutions to continue delivering learning. At the most rudimentary level, this has meant moving to online teaching through platforms such as Zoom, Google, Teams and Interactive Whiteboards and delivering pre-recorded educational materials via Learning Management Systems (e.g., Echo). Digital learning is now simply part of the education landscape both in the traditional education sector as well as within the context of corporate and workplace learning. A key challenge future teachers face when delivering educational content via digital learning is to be able to assess what the learner knows and understands, the depths of that knowledge and understanding and any gaps in that learning. Assessment also occurs in the context of the cohort and relevant band or level of learning. The Teachers Guide to Assessment produced by the Australian Capital Territory Government [2] identified that teachers and learning designers were particularly challenged by the assessment process, and that new technologies have the potential to transform existing digital teaching and learning practices through refined information gathering and the ability to enhance the nature of learner feedback. Artificial Intelligence (AI) is part of the next generation of digital learning, enabling educators to create learning content, stream content to suit individual learner needs and access and in turn respond to data based on learner performance and feedback [3]. AI has the capacity to provide significant benefits to teachers to deliver nuanced and personalised experiences to learners.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


How Veterans Would Study Machine Learning If He Had to Start Today - AI Trends

#artificialintelligence

How one gets educated for AI continues to be an area worth exploring with many options available. Charting one's career as a member of a newly-formed team working to leverage AI to help the business is best met with creativity and patience. It's as much a mission to find out how organizations are setting up for AI development as it is about finding out what you really want to do. The experience of one now-veteran machine modeler could be timely guidance for many in this context. Daniel Bourke is an entrepreneur running a YouTube site and writing about technology.


How Machine Learning and AI are Making Online Learning More Beneficial

#artificialintelligence

Online learning (aka E-Learning) is now considered to be an integral part of the education sector. In simple words, online learning refers to the type of learning where the learning process is mediated by the internet i.e. the learners use the internet to learn. Online learning is gaining tremendous popularity. It is also said to increase the knowledge retention rates from 25-60% in comparison to face-to-face training. Online learning owes much of its popularity and efficiency to machine learning (ML) and artificial intelligence (AI).


An improved online learning algorithm for general fuzzy min-max neural network

arXiv.org Machine Learning

An improved online learning algorithm for general fuzzy min-max neural network Thanh Tung Khuat Advanced Analytics Institute University of T echnology Sydney Sydney, Australia thanhtung.khuat@student.uts.edu.au Abstract --This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new online learning algorithm, a simple ensemble method is also proposed. I NTRODUCTION Artificial neural networks (ANNs) are one of the most widely used methods for dealing with classification problems as well as real-world applications [1]. However, the main disadvantage of the original ANNs is that they do not have the capability of giving explanations of their predictive results to humans explicitly. This drawback restricts the widespread use of the ANNs for critical domains such as healthcare and criminal justice [2]. In a recent study, Rudin [2] has highlighted that there is a high demand for interpretable models to substitute black-box models in assisting decision-makers in areas with the requirement of high safety and trust.