Artificial Intelligence-Enhanced Learning:A New Paradigm in the“Business Data Analysis and Application”Course

This paper explores the transformative impact of generative artificial intelligence(AI)on the“Business Data Analysis and Application”course in the post-2023 era,marking a significant paradigm shift in educational methodologies.It investigates how generative AI reshapes teaching and learning dynamics,enhancing the processing of complex data sets and nurturing critical thinking skills.The study highlights the role of AI in fostering dynamic,personalized,and adaptive learning experiences,addressing the evolving pedagogical needs of the business sector.Key challenges,including equitable access,academic integrity,and ethical considerations such as data privacy and algorithmic bias,are thoroughly examined.The research reveals that the integration of generative AI aligns with current professional demands,equipping students with cutting-edge AI tools,and tailoring learning to individual needs through real-time feedback mechanisms.The study concludes that the incorporation of generative AI into this course signifies a substantial evolution in educational approaches,offering profound implications for student learning and professional development.

Large-Scale Assessments,Personalized Learning,and Creativity:Paradoxes and Possibilities

Purpose:This article,based on an invited talk,aims to explore the relationship among large-scale assessments,creativity and personalized learning.Design/Approach/Methods:Starting with the working definition of large-scale assessments,creativity,and personalized learning,this article identified the paradox of combining these three components together.As a consequence,a logic mode of large-scale assessment and creativity expressions is illustrated,along with an exploration of new possibilities.Findings:Smarter design of large-scale assessments is needed.Firstly,we need to assess creative learning at the individual level,so complex tasks with high uncertainty should be presented to students.Secondly,additional process and experiential data while students are working on problems need to be captured.Thirdly,the human-artificial intelligence(AI)augmented scoring should be explored,developed,and refined.Originality/Value:This article addresses the drawbacks of current large-scale assessments and explores possibilities for combining assessment with creativity and personalized learning.A logic model illustrating variations necessary for creative learning and considerations and cautions for designing large-scale assessments are also provided.

The impact of ChatGPT on foreign language teaching and learning:Opportunities in education and research

The revolutionary online application ChatGPT has brought immense concerns to the education field.Foreign language teachers being some of those most reliant on writing assessments were among the most anxious,exacerbated by the extensive media coverage about the much-fantasized functionality of the chatbot.Hence,the article starts by elucidating the mechanisms,functions and common misconceptions about ChatGPT.Issues and risks associated with its usage are discussed,followed by an in-depth discussion of how the chatbot can be harnessed by learners and teachers.It is argued that ChatGPT offers major opportunities for teachers and education institutes to improve second/foreign language teaching and assessments,which similarly provided researchers with an array of research opportunities,especially towards a more personalized learning experience.

Revisiting Educational Issues in the Age of Generative Artificial Intelligence

The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.

Exercise Recommendation with Preferences and Expectations Based on Ability Computation

In the era of artificial intelligence,cognitive computing,based on cognitive science;and supported by machine learning and big data,brings personalization into every corner of our social life.Recommendation systems are essential applications of cognitive computing in educational scenarios.They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress.The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model(LFCKT-ER).First,the model computes students’ability to understand each knowledge concept,and the learning progress of each knowledge concept,and the model consider their forgetting behavior during learning progress.Then,students’learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences.Then students’ability is used to evaluate whether their expectations of the difficulty of the exercises are reasonable.Then,the model filters the exercises that best match students’expectations again by students’expectations.Finally,we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest diversity.From the experimental results,the LFCKT-ER model can better meet students’personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.

ChatGPT:The New Trend of Smart Education

In response to the limitations of the traditional education and teaching model,this article proposes a smart education model based on ChatGPT.The model actively breaks the constraint of time and space and the design patterns of traditional education,providing smart education services including personalized learning,smart tutoring and evaluation,educational content creation support,and education big data analysis.Through constructing an open and inclusive learning space and creating flexible and diverse educational models,ChatGPT can help to meet students’individuality and overall development,as well as assist teachers in keeping up with the students’learning performance and developmental requirements in real-time.This provides an important basis for optimizing teaching content,offering personalized and accurate cultivation,and planning the development path of students.

FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication

Most finger vein authentication systems suffer from the problem of small sample size.However,the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity.So the researchers resort to pre-training or multi-source data joint training methods,but these methods will lead to the problem of user privacy leakage.In view of the above issues,this paper proposes a federated learning-based finger vein authentication framework(FedFV)to solve the problem of small sample size and category diversity while protecting user privacy.Through training under FedFV,each client can share the knowledge learned from its user′s finger vein data with the federated client without causing template leaks.In addition,we further propose an efficient personalized federated aggregation algorithm,named federated weighted proportion reduction(FedWPR),to tackle the problem of non-independent identically distribution caused by client diversity,thus achieving the best performance for each client.To thoroughly evaluate the effectiveness of FedFV,comprehensive experiments are conducted on nine publicly available finger vein datasets.Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data.To the best of our knowledge,FedFV is the first personalized federated finger vein authentication framework,which has some reference value for subsequent biometric privacy protection research.

Personalized Federated Learning for Heterogeneous Residential Load Forecasting

Accurate load forecasting is critical for electricity production,transmission,and maintenance.Deep learning(DL)model has replaced other classical models as the most popular prediction models.However,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy risks.Edge nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw data.However,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy disclosure.Accordingly,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these issues.The obtained personalized model outperforms the global model on local data.Further,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy guarantee.Based on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the game.We perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

Continuing professional development:progress beyond continuing medical education

Continuing medical education(CME)is rapidly evolving into competency-based continuing professional development(CPD)and this is driving change in self-directed CPD programs undertaken by individual practitioners as well as CPD programs or frameworks offered by CPD educators.This progression is being led by many factors including the rapid change in medical knowledge and medical practitioners along with changes in patients and society,healthcare systems,regulators and the political environment.We describe our experiences primarily concerning low-resource environments,in creating the International Council of Ophthalmology(ICO)Guide to Effective CPD/CME and in developing a CPD program for the Cambodian Ophthalmological Society(COS)twinned with the Royal Australian and New Zealand College of Ophthalmologists(RANZCO).At the conclusion of the project,47(100%)Cambodian practicing ophthalmologists were registered in the CPD program and 21(45%)were actively participating in the online COS-CPD program recording.We discuss challenges in CPD,propose solutions to overcome them and recommend developing research in CPD as needed to effectively enhance educational activities with impact in public health.

MpFedcon: Model-Contrastive Personalized Federated Learning with the Class Center

Federated learning is an emerging distributed privacypreserving framework in which parties are trained collaboratively by sharing model or gradient updates instead of sharing private data. However, the heterogeneity of local data distribution poses a significant challenge. This paper focuses on the label distribution skew, where each party can only access a partial set of the whole class set. It makes global updates drift while aggregating these biased local models. In addition, many studies have shown that deep leakage from gradients endangers the reliability of federated learning. To address these challenges, this paper propose a new personalized federated learning method named MpFedcon. It addresses the data heterogeneity problem and privacy leakage problem from global and local perspectives. Our extensive experimental results demonstrate that MpFedcon yields effective resists on the label leakage problem and better performance on various image classification tasks, robust in partial participation settings, non-iid data,and heterogeneous parties.

Time to Rethink:Educating for a Technology-Transformed World

Purpose:We hope to provoke a conversation about preparing students for an uncertain future that unforeseeable technological innovations will transform in ways we cannot predict.The unprecedented disruption caused by the COVID-19 pandemic makes this an opportune time to reconsiderall dimensions of education.Design/Approach/Methods:We present information on how technology is transforming virtually every aspect of our lives and the threats we face from social media,climate change,and growing inequality.We then analyze the adequacy of proposals for teaching new skills,such as 2Ist-Century Skills,to prepare students for a world of work that is changing at warp speed.Findings:Despite harbingers of a radically different future,most schools continue to operate much as they have for centuries,providing a one-size-fits-all education.Technology now enables an unprecedented degree of personalization.We can tailor learning opportunities to individual students'interests,talents,and potential with teachers serving as guides,resources,and critical friends.The Internet afford a cornucopia of learning opportunities-online courses,international experts,global collaborations,accessible databases,and libraries.Learning can occur virtually anywhere.Originality/Value:The future depends on decisions we are making today about education.The value of this article is that we call for rethinking every component of education rather than considering each element independently.