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AI AS A TUTOR

Elda Mariya Joy1 , Anania Santhosh2 , Ancila Ansari3 , Ardra Suresh4 , Ashna Suresh5

1Asst.Professor,Dept.of Computer Science and Engineering,Sree Narayana Gurukulam College of Engineering,Kadayiruppu,Kerala,India

2,3,4,5 Dept.of Computer Science and Engineering,Sree Narayana Gurukulam College of Engineering,Kadayiruppu,Kerala,India

Abstract - This survey paper presents a comprehensive explorationofArtificialIntelligence(AI)systemsfunctioning as tutors, encompassing related works, comparisons, and future scope. The review delves into various methodologies, algorithms, datasets, and applications employed in AI tutor systems.Itsynthesizesexistingliteraturetoshedlightonthe evolutionofthesesystems,providinginsightsintochallenges faced and advancements achieved. Through comparative analyses, the paper discusses and contrasts different approaches, offering a critical perspective on their effectiveness. The survey also outlines potential future directions and areas for improvement in AI-based tutoring. Aimed at scholars, professionals, and enthusiasts, the paper servesasavaluableresourcetostimulatefurtherinnovation andunderstandinginthedynamicfieldofAItutoring.

Key Words: AI Tutoring, Intelligent Tutoring Systems (ITS), Personalized Learning,Online Learning, Tutoring Systems Evaluation, Student Engagement.

1. INTRODUCTION

Inthecontemporarylandscapeofeducation,theinfusionof ArtificialIntelligence(AI)hasusheredinaparadigmshift, revolutionizing the way we perceive and engage with learning. One of the remarkable manifestations of this transformation is the emergence of AI tutors, intelligent systems designed to guide and support learners in their educational journey. As traditional teaching methods undergo a digital metamorphosis, AI tutors stand at the forefront,offeringpersonalized,adaptive,anddata-driven approaches to education. This review delves into the multifaceted realm of AI as a tutor, exploring the diverse methodologies, applications, and implications of these intelligent systems. By examining related works, drawing comparisons, and outlining future prospects, we aim to provide a holistic understanding of the current state and potentialadvancementsinAI-driveneducationaltutoring.

2. LITERATURE SURVEY

Inthepaper[1]“TheResearchontheRoleofOnlineTutor

andtheLearningActivityOrganizationStrategies" byNan

Wang andAi-ling Qiao examinesstrategiesfor developing effectiveonlinelearningmodels,withafocusonthecrucial role of online tutors. Given the emphasis on utilizing informationtechnologytoimproveeducationinChina,the authorsanalyzecommonchallengesinweb-basedlearning. Theyproposethatonlinetutorsarecriticaltodrivestudent engagement and learning outcomes. The paper suggests online tutors should facilitate structured learning experiencesbydesigningmeaningfultasksandmoderating collaborativediscussionforums.Additionally,tutorsshould provide customized support based on the specific curriculum and needs of individual learners. Overall, the authors recommend that online instructors embrace their roleasguidesandenablersofself-directedonlinelearning. With proper tutor participation, web-based education can become more impactful. The paper aims to encourage teachers to actively support students in online environments, in order to enhance the effectiveness of technology-enabled instruction. Further research could continueinvestigatingoptimalonlineteachingstrategies.

The research paper [2]"Understanding the Factors InfluencingHigherEducationStudents”IntentiontoAdopt Artificial Intelligence-Based Robots" by Mohammed A. M. Algerafi, Yueliang Zhou, Hind Alfadda, and Tommy Tanu Wijaya, investigates Chinese higher education students' willingness to adopt AI-based robots for educational purposes.ApplyingtheTechnologyAcceptanceModel(TAM) 3,thestudyproposes14hypotheses,revealingthat12were accepted.Theresultssuggestapositiveinclinationamong studentstoembraceAI-basedrobotsineducation.However, job relevance and robot anxiety were found to have insignificant impacts on perceived usefulness and ease of use, respectively. The study offers valuable insights for universityadministrations,robotdevelopers,policymakers, andadministratorsindesigningandimplementingAI-based robotstomeetcontemporaryeducationalneeds.

Theresearchpaper[3]"ArtificialIntelligenceinEducation:A Review" by Lijia Chen, Pingping Chen, and Zhijian Lin, explores the impact of Artificial Intelligence (AI) on education. Focusing on administration, instruction, and learning, the study utilizes a qualitative approach and literature review to assess AI applications. It reveals the

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extensive adoption of AI in education, evolving from computertechnologiestoweb-basedsystemsandhumanoid robots. AI systems, including web-based chatbots, independently perform administrative and instructionalStasks, enhancing efficiency in grading and teaching quality. Notably, machine learning enables personalizedcurriculumandcontent,aligningwithstudents' needs, thereby improving uptake, retention, and overall learningexperiences.Thestudyprovidesvaluableinsights into the transformative effects of AI on various facets of education.

The paper titled [4] "Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to IdentifyLearningStyles:ASystematicLiteratureReview"by SaadiaGuttaEssa,TurgayCelik,andNadiaEmeliaHumanHendricks delves into the use of artificial intelligence (AI) and machine learning (ML) in personalized adaptive education systems, aiming to overcome the limitations of statically determined learning styles (LSs). Conducting a systematicliteraturereviewfrom2015to2022,theauthors analyzeinfluentialstudiestoidentifytrendsandgapsinthe literature. The paper highlights the application of ML techniques to dynamically map students' behavioral attributes to specific LSs, optimizing individual learning processes.ThereviewexploresLSmodels,MLtechniques, platforms stimulating research, evaluation methods, and learningsupport.Thefindingsindicateagrowinginterestin employing artificial neural network approaches for LS identification,withanotablegapinthecomparisonofdeep learning methods. The study underscores the need for further empirical investigation and documentation of the adoption and comparison of deep learning algorithms in classifying LSs to enhance adaptability in e-learning environments.Overall,thepaperprovidesinsightsintothe evolving landscape of personalized adaptive learning technologies and the role of ML in identifying and accommodatingdiverselearningstyles.

The research paper [5] "Research on the Design and ImplementationofIntelligentTutoringSystemBasedonAI Big Model" byWenjing Shi,Zhuming Nie and Yuhan Shi presents research on developing an intelligent tutoring

systemusinglargeAImodelstoprovidepersonalizedand adaptivelearningsupport.Thesystemutilizesdeeplearning algorithms and big models to analyze each student's data andlearningpatternsinordertobuildacustomizedlearning profile.Basedonthisprofile,thesystemcantailortutoring toindividualstudentsbygeneratingpersonalizedlearning paths,recommendingappropriateteachingresources,and providingreal-timefeedbackandassessment.Thegoalisto simulateanactualteacher'sguidancetohelpstudentsbetter comprehendandapplyknowledgeacrossvarioussubjects. The researchers adopt advanced AI techniques and computing platforms to create an efficient, scalable, and user-friendly system interface. Overall, this intelligent

Inpapertitled[6]"AIandMachineLearningTechniquesin theDevelopmentofIntelligentTutoringSystem:AReview" by Fatema AlShaikh and Nabil Hewahi provides a comprehensive review of research on intelligent tutoring systems (ITS) that leverage artificial intelligence and machine learning techniques. ITS aim to provide personalized and adaptive learning support by emulating humantutors.TheauthorsdescribethearchitectureofITS, whichincorporates student modeling, domainknowledge, pedagogical modules, and user interfaces. Key AI and ML approaches used in ITS include reinforcement learning, artificial neural networks, clustering algorithms, Bayesian networks, and fuzzy logic. These techniques enable capabilitiessuchascustomizedfeedback,intelligentproblem solving support, and knowledge assessment. The authors summarize current ITS implementations and ongoing researcheffortstoenhancethesesystemsusingthelatestAI innovations. Challenges include developing more accurate studentmodels,scalableplatforms,andcapabilitiesfornew subjectdomains.Overall,thereviewhighlightsthevitalrole ofAIinbuildingITSthatcandynamicallyadapttoindividual students and improve outcomes through one-on-one intelligenttutoring.FurtheradvancementsinAIandMLwill continueexpandingthesesystems.

Inthepaper[7]“RehearsingNavalTacticalSituationsUsing SimulatedTeammatesandanAutomatedTutor”byEmilio Remolina,SowmyaRamachandran,RichardStottlerandAlex DavisdescribesanIntelligentTutoringSystemthattrains

tutoringsystemleveragesthecapabilitiesofAIbigmodelsto offer individualized tutoring and has the potential to enhance student learning outcomes and engagement. Further research could focus on optimizing the system to accommodate different learner needs and educational objectives. TacticalActionOfficers(TAOs)intheNavy.Thesystemuses simulation and artificial intelligence to provide a virtual trainingenvironment.TAOslearn"commandbynegation," supervising watchstanders who perform duties autonomously,whiletheTAOintervenestocorrectmistakes. ThesystemfeaturesAutomatedRolePlayersrepresenting watchstanders and a natural language interface for communicating with them. An adaptive coaching strategy providespersonalizedfeedbackduringexercises.Thepaper outlinestheinstructionaldesign,systemarchitecture,andAI techniques, including simulations, automated teammates, natural language processing, and adaptive coaching algorithms that make this specialized training system possiblewithinthepracticalconstraintsofdeploymenton Navyships.

The paper titled [8] “Individualized AI Tutor Based on DevelopmentalLearningNetworks”byWoo-HyunKimand Jong-HwanKimproposesanindividualizedAItutorsystem toprovidepersonalizededucationalservicestolearners.The

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

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systemusesthreedevelopmentallearningnetworksbased onadeepadaptiveresonancetheoryneuralnetworkcapable ofincrementallearning.Thefirstnetworkmonitorslearner status from various inputs to understand the learner's current state. The second categorizes learner preferences basedonpatternsintheirchoices.Thethirdreflectsupdated assessmentsofeducationaleffectiveness.Combined,these networks allow the AI tutor to dynamically recommend appropriate personalized content for each learner to help them achieve academic success. The system was implemented in a commercial mobile application for teachingKoreantochildren.Experimentsdemonstratethe application'sefficiencyinassistingchildrentolearnKorean. The proposed AI tutor architecture could be extended to othersubjectsbytrainingthenetworksonrelevantdata.

Thepaper[9]“StudentModelingandAnalysisinAdaptive Instructional Systems” by Jing Liang,Ryan Hare, Tianyu Chang,FangliXu,YingTang,Fei-YueWang,ShimengPengand MingyuLei presentsareviewofresearchfrom2010-2021 onstudentmodelingandanalysisinadaptiveinstructional systems.Thegoal ofstudentmodelingisto representand trace an individual student's knowledge state to inform adaptation.Thepaperdiscussesemergingstudentmodels based on overlay modeling and data-driven approaches, including variations that address limitations. It also examinestheimportanceofmultidimensionallearnerdata, categorized as learning data, physiological data, psychometric data, and environmental data, to improve prediction accuracy. Challenges implementing multimodal learninganalyticsinreal-worldclassroomsaresummarized. An industry case study demonstrates increased model prediction accuracy from 53.3% to 69% by adding modalities. However, uncontrolled data collection led to issues like noise. The paper concludes by offering perspectivesonlimitations,challenges,andpromisingfuture directionsforresearch.

NAME ADVANTAGES DISADVANTAGES

[1]The Research on the Role of Online Tutor and the Learning Activity Organization Strategies

[2]Understand ingtheFactors Influencing Higher Education Students

Analyzes Effective strategies foronlinetutorsto support and engagestudents

Providesguidance on organizing learning activities in a remote environment

Highlights the unique role and best practices for onlinetutors.

Informs training and professional development for onlinetutors

Provides insight into elements impacting student success beyond justacademics.

Canidentifyat-risk students to target support and preventdropouts

Findings may not generalize across different subjects, student age groups andtutoringformats

Practical implementation challenges may be overlooked.

Technological capabilities and constraintsmaynot befullyaddressed.

Difficult to capture full range of factors influencing each individualstudent.

Findings may not generalize across diverse student demographics and institutions

Informs improvements to curriculum, advising, campus resourcestobetter servestudents. Self-reported data from students can containbiases

Quantitative correlations do not determinecausation Focuses specifically on higher education context vs. Potentialissueswith sampling methods providing

Table -1: COMPARISON OF LITERATURE SURVEY

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

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broader education research.

Holistic view of student experience using a mixed methodsapproach. representativedata.

[3]Artificial Intelligencein Education:A Review

Provides a comprehensive overview of the state of AI technology in educational contexts.

Identifies key areas and applications where AI is making an impactonlearning andinstruction.

Analyzes the capabilities and constraints of existing AI systems in education.

Highlights major trends and future directions for the field.

Encouragescritical analysis of the challenges and opportunities associatedwithAI ineducation.

Promotes discussionaround the theoretical foundations and ethical implications of using AI for teaching and learning.

Ethical concerns around collection/use of studentdata.

Hard to account for allcontextualfactors inanalysis.

A review may lack sufficient critical assessment of the quality of existing researchstudies.

The breadth of the review could miss depth in analyzing specific AI methods andtechnologies.

Education is a complex domain, difficult to fully capture through a broad AI literature review.

Hardtokeepcurrent asthefieldisrapidly evolving. Findings mayquicklybecome outdated.

Trends identified may be speculative rather than evidenced-based.

The review may emphasize technological capabilities over practical implementation challenges.

d Adaptive Learning Technologies Based on Machine Learning Techniquesto Identify Learning Styles: A Systematic Literature Review

comprehensive overview of researchonusing machine learning to identify and adapttodifferent learningstyles.

Analyzes the capabilities and limitations of current machine learning techniques for personalization.

Assesses the validity and reliability of proposedlearning stylemodels.

Identifies key trends and future directions for adaptive learning technologies.

Encourages critical thinking on using learningstylesand personalizationin education.

[5]Research on the Design and Implementatio nofIntelligent Tutoring System Based on AI Big Model

Leverages state-ofthe-art AI techniques like deep learning for advanced tutoring capabilities.

Big models can provide more accurate analysis of student responses and personalized feedback.

Can dynamically adjust instruction to each student’s knowledge levels andneeds.

learning styles as a concept is still debated among researchers.

Review may lack indepth critical analysis on the qualityofstudies.

Findings could emphasize technological capabilities over practicalchallenges.

Difficult to draw definitive conclusionsgiventhe breadth of the review.

Research landscape is rapidly evolving, so review findings mayquicklybecome outdated.

Ethical implications ofdatacollectionand analytics could be exploredmore.

Require extensive computational resources and expertisetodevelop andmaintain.

Student motivation and emotion are difficult for AI to evaluateeffectively.

Adaptability of system is limited by the training data parameters.

Student privacy concerns around data collection and surveillance.

[4]Personalize

Provides a The reliability of

Large training datasets enable

Tutor interactions mayfeelimpersonal

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

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the system to coverawiderange of topics and scenarios.

[6]AI and Machine Learning Techniquesin the Development of Intelligent Tutoring System: A Review

[7]Rehearsing NavalTactical Situations Using Simulated Teammates and an Automated Tutor

AI-powered tutors are more scalable and cost-effective than human tutoring. or lack nuance without human involvement.

Provides a comprehensive overview of the state of AI/ML in ITSdevelopment.

Analyzes capabilities and constraints of various AI/ML methods for modeling students and personalizing instruction.

Identifies key trends and advancements that show promise for enhancing ITS effectiveness.

Encourages critical analysis of the challenges in implementing AIpowered adaptive learningatscale.

Highlights gaps in research that still need to be addressed.

Enables training for difficult-torecreatescenarios (valuable experience) Provides automated feedback for improvingskills

Testing for fairness, transparency and ethics of the AI tutoring is critical butchallenging.

The breadth of the review results in limited depth/critique of specific AI/ML techniques.

Practical implementation challenges may be overlooked.

Findings could overemphasize technological capabilities rather than real-world limitations.

Hard to provide concreteevaluations and recommendations given the broad scope.

The AI landscape evolves rapidly, so findingsmaysoonbe outdated.

Discussionofethical implications is limited.

Lacks realism compared to realworldexercises

Effectiveness depends on simulation/AI capabilities

Could lead to anticipation rather

[8]Individualiz ed AI Tutor

Based on Developmenta l Learning Networks

Morecost-effective than real-world exercises

[9]Student

Cannot fully replace human team members

Builds teamwork skills with simulated teammates thancriticalthinking

Adaptive learning customizedtoeach student’s needs andpace

Provides real-time feedback and corrections during learning

Allowslearnersto progress at their ownpace

Reduces need for constant human teacheroversight

Analyzes strengths/weakne ssesoflearnersto focustraining

Requiressubstantial programming and maintenance

Significant programming requiredforeffective AItutor

Potentialissueswith accurately assessing studentperformance

Lack of human element in teaching/communica tion

Overreliance could reducedevelopment ofcriticalthinking

Studentfrustrationif systeminaccurateor ineffective

Large amounts of data required for tutor to be truly personalized

Modeling and Analysis in Adaptive Instructional Systems Creates customized learning experiences by adapting to each student’s level, needs and interests

Provides more targeted and efficient instruction by focusingonareas where students needthemosthelp

Reduces chances ofstudentsgetting bored or frustrated by

Requires extensive student data collection and complex analysis to build accurate models

Potential for inaccurate student modeling if algorithms are flawed or data is insufficient

Overreliance on systemcouldreduce developmentofselfdirected learning skills

Privacy concerns

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

receiving instruction unsuited to their abilities around data collection and analyticsonstudents

Analyzes student weaknesses and misconceptionsto improve instructional content

Models student knowledge, skills and affective states to personalize learning

Difficult to scale system across large student populations andcontentareas

Does not account well for social, emotional and environmental factors influencing learning

REFERENCES

[1] N.WangandA.-l.Qiao,"TheResearchontheRoleof OnlineTutorandtheLearningActivityOrganization Strategies," 2011FourthInternationalConferenceon IntelligentComputationTechnologyandAutomation, Shenzhen,China,2011,pp.12331235,doi:10.1109/ICI CTA.2011.619.

[2] M. A. M. Algerafi, Y. Zhou, H. Alfadda and T. T. Wijaya, "Understanding the Factors Influencing Higher Education Students’ Intention to Adopt ArtificialIntelligence-BasedRobots,"in IEEEAccess, vol.11,pp.9975299764,2023,doi:10.1109/ACCESS.2 023.3314499.

[3] L.Chen,P.ChenandZ.Lin,"ArtificialIntelligencein Education: A Review," in IEEE Access, vol. 8, pp. 7526475278,2020,doi:10.1109/ACCESS.2020.2988 510.

FUTURE SCOPE

ThefuturescopeofAIasatutorisexpansive,revolutionizing thelandscapeofeducation.AI-poweredtutoringsystemsare poised to provide personalized learning experiences, adapting to individual needs and learning styles. These systemsarelikelytoevolveintoadaptivelearningplatforms thatcontinuouslyassessandadjustthecurriculumbasedon student progress. Global accessibility will be enhanced, breakingdowngeographicalbarriersandofferingeducation in diverse languages and cultural contexts. AI tutors are expected to engage in more natural and interactive conversations with students, incorporating multimodal learning approaches. Continuous assessment, immediate feedback,andtheintegrationofemotionalintelligencewill create supportive and empathetic learning environments. Lifelong learning initiatives will benefit from AI tutors, assistingindividualsinacquiringnewskillsthroughouttheir careers. As technology advances, the integration of augmentedreality,virtualreality,andethicalconsiderations will play a crucial role in shaping the responsible and effectiveuseofAIineducation.

A comprehensive examination of related works in AI tutoring reveals a promising landscape for further enhancements.Integratingadditionalfeatures,particularly visualaids,cansignificantlyaugmenttheefficacyofAIasa tutor. The synthesis of existing research underscores the importanceofevolvingtutoringsystemstocatertodiverse learningstylesandoptimizeeducationaloutcomes.Aswe progress, the incorporation of innovative tools and multimediaelementsstandsasakeyavenueforrefiningAI tutoring platforms, ultimately contributing to a more personalizedandeffectivelearningexperience.

[4] S. G. Essa, T. Celik and N. E. Human-Hendricks, "Personalized Adaptive Learning Technologies BasedonMachineLearningTechniquestoIdentify Learning Styles:A Systematic Literature Review,"inIEEEAccess,vol.11,pp.4839248409,2023, doi:10.1109/ACCESS.2023.3276439.

[5] W.Shi,Z.NieandY.Shi,"ResearchontheDesign andImplementationofIntelligentTutoringSystem Based on AI Big Model," 2023 IEEE International Conference on Unmanned Systems (ICUS), Hefei, China,2023,pp.16,doi:10.1109/ICUS58632.2023.10 318499.

[6] F. AlShaikh and N. Hewahi, "AI and Machine Learning Techniques in the Development of Intelligent Tutoring System: A Review," 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies(3ICT),Zallaq,Bahrain,2021,pp.403410,doi:10.1109/3ICT53449.2021.9582029.

[7] E. Remolina, S. Ramachandran, R. Stottler and A. Davis,"RehearsingNavalTacticalSituationsUsing Simulated Teammates and an Automated Tutor," in IEEETransactionsonLearningTechnologies,vol. 2, no. 2, pp. 148-156, April-June 2009, doi: 10.1109/TLT.2009.24.

[8] W.-H.KimandJ.-H.Kim,"IndividualizedAITutor

CONCLUSIONS Based on Developmental Learning Networks," in IEEE Access, vol. 8, pp. 2792727937,2020,doi:10.1109/ACCESS.2020.2972167

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 p-ISSN: 2395-0072

[9] J. Liang et al., "Student Modeling and Analysis in Adaptive Instructional Systems," in IEEE Access, vol.10,pp.5935959372,2022,doi:10.1109/ACCESS.2 022.3178744.