Integrated Skills in Language Teaching 3 PDF

topic: date: Week 213 content then.tn IEainingmeenoas Defining AI refers to the d...

topic: date: Week 213 content then.tn IEainingmeenoas Defining AI refers to the developmentof intelligent 1 Supervised Learning systems that can mimichumanbehaviorand useslabeled data forpredictions decision makingprocesses example a model fedwiththousandsof imagesthat are AI simulateshuman intelligencethrough tagged with theanimaltheycontain totrain a modelto classify 1 Learning theacquisition ofinformation futureimages by whichanimal ispresent in thepicture andrulesforusingtheinformation 2 unsupervised Learning 2 Reasoning usingtherulestoreach identifiespatterns inunlabeleddata approximateor definiteconclusions example an algorithmmightbe provided withdifferentbooks 3Selfcorrection knowingwhen amistake whichmust be groupedbasedontheirsubjects readinglevelsand hasbeenmadeandcorrectingit languages 3 ReinforcementLearning Fundamentals of AI andMachineLearning Learns via actions andrewards coreconcepts example AI learnsto winbyplayingthe gamemultipletimes AI encompasses variousstagesof and improving its strategybasedon whether it wonor costprevious intelligence games stagesof a 4DeepLearning 1 Narrow AI weak AI utilizesneuralnetworks forcomplextasks Performs specific narrowtasks exampledeeplearningmodels aredesigned to automaticallylearn e g voicerecognition Siri to representdata bytrainingonlargeamounts ofdata andtheyare recommendationsystems youtube image particularly effective at learningpatternsfromunstructed datasuchas recognitionFaceID images audioand text 2 ArtificialGeneralIntelligenceStrongAI 5TranferLearning Learnsandadaptsacrossvarious it involvestaking apreexistingtrainedmodelfor ataskand taskslike a human reusing it as the startingpointfora differentbut relatedassignment 3Super intelligent AI outperformshumanintelligence in Building AI systems all fields AI having consciousness and Machinelearningbasics self awareness AI understandingthoughts AI systems arebuilt on algorithmsthatlearnfromdata to emotions andentities that affecthumans improveperformance still theoretical Qualityandquantity oftrainingdata significantlyaffectmodel Current situation betweenNarrowandAGI outcomes predictions decisions  topic: date: Week213 content ExploringGenerative AI GenAI 3Transformers e.gGPT GenAI is any AI systemwhoseprimary transformers likeGPT arehighlyeffective at generating functionis to generatecontent text They createsnew content suchas text images a cangenerateresponses to questions Generative musicvideosbasedonthepatterns ithaslearned b was trained in advanceon alargeamount of the fromexistingdata writtenmaterial available on theweb Pretrained it uses machinelearningtechniques especially c can processsentencesdifferentlythanother previous deeplearningmodelslikemenetworksto analyze typesof models transformer andlearnfrom vastdatasets 4LargeLanguageModels Neuralnetworks whichare computational theyfocuson understanding text inputs usingnatural structurestinspiredbythehuman brainprocessdata languageprocessing andcreatinghuman like textbasedon agiven instages to Learnhighlycomplexpatterns P operatesby using extensivedatasets tolearnpatternsand Generative models relationships betweenwords andphrases Generative AI isbasedonvariouskindsof models suchas Capabilities of Generative AI 1Autoencoders 1 Text Generation thesemodelscompressinputdata 2 ImageGeneration all E Mid Journey into a simpler representation anddecode it 3 MusicandAudioCreation backintosomething similarbutslightlydifferent 4 Video Generation from the original often usedforgeneratingvariations challenges with Generative AI ofinputdata suchas slightly acteredversions Quality controlissues ofan image potential forinaccuracies and hallucinations 2 GenerativeAdversarialNetworks GANS Ethical concerns consists of two networks risksofmisinformation e.gdeep faces orfakenews plagiarism thegenerator generates andbiasinoutputs Computational costs a Ih discriminator evaluates asignificant energy consumption intraining largemodels how realdata is Future considerations the generatorimprovesovertimeby aneedforefficientsolutions to mitigate environmental costs trying tofoolthe discriminatorleadingto highlyrealistic outputs  topic: date: Week213 content with AI a teacherspeaks to an AI assistant and it speaks back A teacher student starts to write something and the computerfinishes their sentence Possible actions in the future an AI system helpswithhomework reduces a teacher's workloadby recommending lessonplans AI and theFuture ofTeachingandLearning humanlike computersnowhavecapabilities that areverydifferentfromthe capabilities of early eatech applications educational applications will beableto converse withstudents andteachers copilot howactivitiesunfold in classrooms and take actions that impactstudentsandteachersmore EducationalBenefits of AI inELT Keyareasof application Intelligence Augmentation centres development of speakingwritingandreadingskills intelligence and decisionmaking in humansbut supportforpedagogyandself regulation recognizes thatpeople sometimes are overburdened NotableExclusion Listeningskillswerenothighlighted as afocus andbenefitfromassistivetools area AI SystemsEnableNewForms ofInteraction At in speakingskills supportforms ofinteraction that aremore pronunciation improvement naturalsuchasspeaking to an assistant teaching speaking Inclassicedtechplatforms limitedinteraction AI as a conversationalpartner choosingitemsfromacistor in a tailored instructionbased on individuallearningpatterns multiplechoicequestion improves fluency typingshort answers AI technologies forimprovingspeaking the computeroutput informsof text Googlespeech to text Apple's Siri speech recognition graphicsandmultimedia KhanAcademy Dreambox adaptivelearning human computerinteraction Dudingo automaticspeechanalysis AmazonAlexaGoogleAssistantAppleSiri voice assistant  topic: date: Week 213 content AI in writingskills Implications forPractice Vocabulary andgrammar enhancement teacher education trainingmust includea focuson AI Feedback mechanisms Literacy Common AI tools teachersshould carefullyconsiderhow models are chosen Grammarly grammarchecker AI can provide a conversational partner to students ProWritingAidwritingassistant GoogleTranslate translationtool weekl HemingwayEditor patternchecker Legal and Ethical Issues in AI AI in Readingskills vocabulary as theonlyaspect of DigitalEducation ActionPlan developingreadingskills vision establishing a highperformingEuropean digital education Gaming as a specific use to support ecosystem that includes AI and data drivensystems pedagogy Twostrategicprioritiesandhowto's 1 fostering highperformingdigitaleducationecosystems AI in Pedagogy infrastructuredigitalcapacityplanninganddevelopment the methodsstrategies andtechniques digitalcompetent educators educationhighqualityandsecure used to facilitateELT content platforms Lectures and explanations are stillin use 2 enhancingdigitalskills and competencies new approaches to provide a more digital literacy AI literacy personalised learning approach Challenges AddressedThedigitaldivide ensuring equitable accessto digitallearning andthe integration of advancedtechnologies suchasAI Challenges of AI in ELT technology breakdowns ArtificialIntelligence andDatause Limited capabilities given the largeamount of dataneeded to train AI systems the automatingnature of algorithms andthe scability inits applications Thalack of the use of AI raisesimportant questions in relation to personaldata clarity onhowpersonal informationwould bestoredandshared dataprotection and Private fear of the unknown deploymentanduse of AI that fearoflosing a naturallearning ensurescompliancewithethicalnorms ethical environment principles andrelated core values standardising languages andideologies  topic: date: content Ethical course of Action two primary ways of deciding theme 1too difficult to understand tnafm.ie tnEnreotfelatcher ethicas.IE EffI s 3 is not inclusive Deontological Perspective ethical actionstrictlymeans AI andData useExamples in Education adhering to established principles and 1 Studentteaching guidelines regardless of the situation using AI toteachstudents How to apply this perspective AI systems should adhere to ng systemdialogue based tutoring systems strict ethicalguidelines rulesand languagelearning applications regulations establishedbyhumans 2 Studentsupporting AI algorithms mustbe unbiased using AI to supportstudent learning transparent and protectprivacy Teachers using AI wouldbe rningenvironmentsformativewritingassessment required to followestablished guidelines Atsupported collaborative learning Students mustuseas inways 3Teacher supporting that alignwithacademicintegritypolicies using AI to supporttheteacher ensuringthat theydon't use AI to cheatplagiarize orunfairly enhance their ing assessment essayscoring studentforum monitoring at teaching assistants pedagogicalresourcerecommendation 1 heoutcome seekers 4 Systemsupporting consequentialist Perspective usingAI to support diagnosticor system wideplanning ethical decisions mustbe based system facing on achieving the mostbeneficial outcome Educationaldataminingfor resource allocation diagnosing even if it meansbending orbreakingrules Learningdifficulties guidanceservices How to applythis perspective certain ethicalprinciplesmay Ethical considerations be flexible or secondary if the AI's actions 1 HumanAgency andOversight Lead to greateroverallbenefits AI should beusedto support and empower teachers Teachermightprioritize the and learners not to replace them benefits that AI can providefor student keypoints learningandbendbreak somerules teachers mustremain in control of the learning studentscoulduse AI to enhance AI systemsshould enhance humandecisionmaking waysthatmightnotstrictlyadherenotdiminish it theirlearning to academicguidelines  topic: date: content 2 Transparency and Explainability 5PrivacyandDataProtection Educatorsstudents andparents AI systemsshouldcomplywithstrictdataprivacylawsand must understandhowAI systems makedecisions handlestudentdata responsibly Keypoints keypoints transparencybuilds trust in AI AI systemscollectvastamountsofpersonaldata usersmustbeable to questionand eg studentperformance behavior challenge AI decisions compliancewithdataprotectionregulations is essential Explainable AI AI systemsshould e g GDPRin Europe provideclear understandablereasonsfortheir Datashouldonlybeusedfor its intendedpurpose and outputs and decisions students shouldhavecontroloverhowtheirdata isused 3DiversityNonDiscriminationFairness BestPractices AI mustbedesignedandimplemented Anonymizationof data to protectstudent identities to ensurefairnessandavoidbiasthatcould clear data retentionpolicies harmanygroupofstudents 6 TechnicalRobustness andsafety Keypoints AI systems mustbe secureandresilient to externalthreats AI systems mustbeevaluated suchas hackingor unauthorized access regularly toensurethattheydonotperpetuate Keypoints or introducebiases protecting studentdatafromcyberattacks is crucial TypesofBiases AI systems shouldbe designed withbuilt in safeguards DataBias occurs if the data to ensure that datais not misused or accessedby unauthorized usedto train AI systems reflectsexisting individuals socialbiases e.g genderethnicity 7 Accountability AlgorithmicBiases arisesif AI clearaccountability must be establishedfor the development algorithms are designed or appliedinways deployment and outcomes of AI systems that unfairlyfavor or disadvantagecertain Keypoints groups AI providers andeducatorsusingthetoolsshouldbe 4 Societal Environmental wellbeing accountable fortheiractions includes sustainabilityandenvironment Continuousmonitoringandauditing of AIsystems are friendlinesssocialimpact societyanddemocracy required to ensuretheymeetethicalstandards Keypoints roles AI ineducationshouldaim tobridge whois responsible if an AI systemmakes a mistake socialdividesandfosterinclusivity ensuring Teachers AI developers or theschool equitable accessto quality educationfor allstudents regardless oftheirsocio economic background  topic: date: Week 4 content Emerging competences forEthicaluse of AI Area6FacilitatingLearner'sdigitalcompetence enablinglearners to creatively and responsibly usedigital Area 1 ProfessionalDevelopment technologies forinformation communication content creation usingdigitaltechnologiesfor comm wellbeingand problem solving collaboration and professional development competenceelement competenceElement theteacher AI and learninganalyticsethics is ableto criticallydescribe positive andnegativeimpactsof AI week617_ and data usein education understands the basicsof AI AIFoundations and Applications in ELT andlearning analytics Area2 Digital Resources Introduction to AI in ELT Sourcing creating and sharingdigital AI simulateshuman intelligence supporting bothteaching resources and learning competenceelement It includesstudentfacingtools like Data governance and AI governance teacher facing applications like Automated Wr ch.gg gt auain Area3 Teaching andlearning and administrative tools managing and orchestratingtheuse of digitaltechnologies NaturalLanguageProcessing NLP competenceelement Enables applications like machine translation Learner models of learning objectives of feedbacksystems andautomaticactivitygeneration education humanagencyfairnesshumanity Studies indicate that whileNLPtools are underused participation in thedevelopmentoflearning teachers holdpositiveattitudestowardtheir integration practices that use AI anddata Area 4 Assessment Automatedwriting Evaluation AWE usingdigitaltechnologies and strategies tools like Grammarlyprovidefeedbackon student to enhance assessment writing aiding autonomy and improvement competence element studiesshow combined teacher andAWEfeedback personaldifferences algorithmic Leads to betterlearningoutcomes biascognitivefocus newwaysto misuse Data DrivenLearning DDL technology focuses on Languagepatternsusingcorpora Area S EmpoweringLearners whileDDLshowsstrongbenefits teachersoftenlack usingdigitaltechnologies to enhance the skills to integrate it effectively inclusionpersonalisationLearner'sactive engagent Competenceelement AI addressinglearner'sdiverseneeds Justifiedchoice topic: date: Week 617 content ComputerizedDynamicAssessment CDA Conclusions provides graduated corrective Educators need to integrate AI effectivelywhile feedback helpinglearners self correct fostering critical human skills like collaboration and Language errors creativity in AI driven environments somesystemsrequirehuman intervention whenthey failto provide appropriate feedback IntelligentTutoringSystems ITSS deliverpersonalizedinstruction based on Learnerneeds enhancingskills like pronunciation vocabulary andgrammar found to improvereading comprehensioncomparedto traditional methods AutomaticSpeechRecognition ASR supports skills like pronunciation and listening reduces anxiety offeringan engaging flexiblelearningenvironment Chatbotsin LanguageLearning facilitateanxiety freepractice andpersonalized learning challenges includenovelty effects and reducedlongterm interest FutureDirections AI tools canmakelearners more autonomous andprovideflexibleinstant feedback Gapsremain in addressing human elements likeemotionsbodylanguage andcriticalthinking