Impact of artificial intelligence (AI) in education. by IRJET Journal

Impact of artificial intelligence (AI) in education.

Suyog Sunil Shinde1 , Dr Amit Medhekar2

1Neville Wadia Institute of Management Studies & Research, NESS WADIA COLLEGE OF COMMERCE, NOWROSJEE WADIA COLLEGE, Sangamvadi, Pune, 411001, Maharashtra, India.

2Professor,Neville Wadia Institute of Management Studies & Research, NESS WADIA COLLEGE OF COMMERCE, NOWROSJEE WADIA COLLEGE, Sangamvadi, Pune, 411001, Maharashtra, India.

Abstract - Artificial intelligence(AI) refersto computer systemsandmachinesthatcanperformtasksthattypically requirehumanintelligence.AIisrapidlytransformingmany industries,includingeducation.Thisreportcomprehensively reviewsAI'scurrentstateandimpactineducation.Amixed methodsresearchdesignwasadopted,combiningqualitative and quantitative data from surveys, interviews, and literature analysis. The key findings indicate that AI has broughtseveralbenefitstoeducation,suchaspersonalized andadaptivelearning,automatedgradingandfeedback,and enhancedstudentrecruitmentandretention.However,there are also notable limitations and challenges, such as high development costs, lack of transparency, potential bias in algorithms,andprivacyconcernsoverdatacollection.Most researchsuggestsAIcanhaveanoverwhelminglypositive impact on education by automating administrative tasks, providing customized support, and improving student outcomes.Morehuman-centeredAIisneededtobuildtrust and understanding between humans and machines in education. The implications call for increased policy direction,frameworks,andcollaborationbetweeneducators andtechnologiststoharnessthepowerofAIforsustainable educationethically.

Key Words: Artificialintelligenceineducation,personalized learning,learninganalytics,ethicsofAIineducation,online education, educational technology, challenges of AI in education,impactofAIoneducation

1. INTRODUCTION

Artificial intelligence (AI) is a concept that refers to the ability of machines and computer systems to perform complex cognitive tasks such as understanding, sensing, learning, and acting, capabilities that are possible only by humanswiththehelpofcognitiveprocessing(Chenet al., 2020).AIisatechnologythathastakenafrontseatinhow wegoaboutourdailyliveswithoutrealizingit,fromvirtual assistantslikeSiritotherecommendationsystemsonNetflix and Spotify. AI's market size, which has grown expoexponentiallyrom merely bia g $93.5 billion in 2021, would surpass $1.4 trillion by 2028 (Emergen Research, 2022).

AIisappliedindiversewaysineducation–fromautomating administrativeprocessestoprovidingadaptive,personalized learningplatforms,virtualtutors,andpredictiveanalytics. TheCOVID-19pandemicforcedschoolstoshiftonlineand has also catalyzed rising investments in education technologyandAI,emphasizingtheirincreasingrelevance. ThispaperexaminestheimpactofAIoneducation,focusing specificallyononlineeducationandmarketingmanagement. The following section presents a literature review and an overviewoftheresearchmethodology.Thekeyresultsand discussion analyze AI's significant advantages and educational limitations, evidenced by current research. Finally, the conclusion summarizes the implications for theory and practice, highlighting priorities and recommendations to guide the future progress of AI in education.

2 Literature Review

2.1 Definition and Conceptual Framework of AI in Education

Artificialintelligencebroadlyreferstoanycomputersystem thatengagesintasksthathavehistoricallyrequiredhuman cognition and judgment (Shen et al., 2021). Kaplan and Haenlein (2019, p.15) define AI as "a system's ability to interpret external data correctly, to learn from such data, andtousethoselearningstoachievespecificgoalsandtasks through flexible adaptation." The system's ability for rationality, creativity, generalization, and continuous improvementdifferentiatesAItechnologiesfromtraditional rule-basedsoftware.

Whenappliedinthecontextofteachingandlearning,AIin educationreferstothedesign,development,andapplication bycomputersystems"thatareadaptive,canenhancehuman intelligence, demonstrate cognition and behave rationally engagingwithteachersandlearnersinmeaningfullearning experiences" (Zawacki-Richter et al., 2019, p.183). The multidisciplinary intersection between AI, psychology, linguistics,andeducationhascreatedtheemergingfieldof artificialintelligenceineducation(AIED)(Chenetal.,2020; Roll&Wylie,2016).AIEDfocusesonunderstandinghowAI techniquescanpromotepersonalizedandlifelonglearning,

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enhancehumancapabilitiesthroughintelligenttechnologies, andensureAIishuman-centered,ethical,andtrustworthy (Yangetal.,2021).Majorapplicationareasspanintelligent tutoring systems, virtual facilitators, adaptive learning environments, assessment engines, augmented reality for training, and AI for administrative tasks like grading, proctoring assessments, creating predictive models, and improvingefficiency(Alam,2021;Chassignoletal.,2018).

AI in education reflects a paradigm shift towards more student-centeredlearn-ingwhileenablingteacherstofocus more on higher-order thinking and interpersonal tasks (Popenici&Kerr,2017).

2.2 Historical Development and Current State of AI in Education

The genesis of AIED emerged in the 1970s when carbon copies of expert systems and chatterbots provided simulations of knowledge and interaction from domain experts and teachers (Zawacki-Richter et al., 2019). However, AI recently began to gain momentum within education due to limitations in big data, algorithms, and hardware.Theexponentialgrowthofeducationaldatafrom learningmanagementsystems,massiveopenonlinecourses (MOOCs), and weblogs have removed this barrier and unlockednewpossibilities.

Powerful machine learning algorithms leveraging neural networks,naturallanguageprocessing,computervision,and predictivemodelingcannowprocessthesevastdatasetsto uncover deep insights and patterns for improving educational outcomes (Zawacki-Richter et al., 2019). Demand is also accelerating for AI solutions as education seeks to expand access, equity, affordability, and quality amidst grow- ing complexity driven by increasing globalization,immigration,andunemployment(Pedroetal., 2019).

Currentapplications ofAIineducationinclude innovative content, virtual teaching assistants, personalized and adaptive learning platforms, student performance prediction, automatic process optimization, cheating detection in exams, career guidance systems, augmented writingtools,andconversationalagentsthatanswerstudent queries(Alam,2021;Chassignoletal.,2018).Globaledtech investmentshavetripledsince2019(Metaari,2022),helping schoolsscaleAIadoption.Notableindustryexamplesinclude Carnegie Learning, Century Tech, ALEKS, Third Space Learning,Century,Quizlet,Knewton,KhanAcademy,Nested AI,andCognii.

2.3 Synthesis of Existing Research

SeveralliteraturereviewshavesynthesizedAIapplications, trends,andeducationalimpact.Chen,Chen,andLin(2020) systematicallyreviewed129studiesonAIEDfrom1998to 2019,findingmostsolutionsfocusedonprovidinglearning support, feedback, and scoring performance. Intelligent

tutoring systems demonstrated effectiveness for personalized guidance, though they faced challenges in terms of development costs, user trust, and scalability beyond one-to-one learning. AI-enabled scoring systems provided more objective, consistent, and timely feedback thanhumangrading,thougherrorscouldoccurduetothe black-boxnatureofalgorithms.

Yang et al. (2021) emphasized the need for more humancenteredAIdesignandAItransparency,accountability,and mobility in education. Zhai et al. (2021) reviewed 217 articlesconfirmingthepositiveeffectsofAIonteachingand learningbuthighlightedtheneedformoremultidisciplinary perspectivesinexistingliterature.Alam(2021)alsoargued that most studies evaluate AI prototypes, not real-world impact,whileschooladoptionneedstobehigher,indicating the need for frameworks that bridge technological possibilitieswithpedagogicalrequirements.

Examining the specific phenomenon of learning analytics, Kennedyetal.(2020,p.7)defineditas"themeasurement, collection,analysis,andreportingofdataaboutlearnersand theircontexts"toimprovestudentsuccess.Whenboostedby AIandMachinelearninglea,rninganalyticsleveragepattern detectionandpredictivemodelingtotransformeducational data into actionable insights (Viberg et al., 2020). For instance,AIcantrackreal-timestudentengagementlevels viafacialrecognitionduringclassesornudgethoseatriskof failing assignments by analyzing cumulative behaviors on past academic performance, internet browsing, library accesslogs,etc.

2.4 Identification of Gaps in Literature

While interest in AI applications in education has surged overthepastdecade,gapsremainaroundadoptionbarriers, challenges,andimpactonactuallearningexperiencesand outcomes. Much focus has centered on exploring technological capabilities with limited critical analysis of long-termsocietalimplications(Cox,2021).Mostliterature alsooriginatesfromtechnicalfieldslikecomputerscience rather than inter-disciplinary perspectives spanning learning sciences, pedagogy, and philosophy that evaluate theholisticinfluenceonstudentsandteachers(Chenetal., 2020).

Despiteincreasingprototyping,fewrigorousevaluationsand pilots have been conducted to demonstrate scalable realworld impact across diverse educational settings (Alam, 2021;Yangetal.,2021).Tosummarise,criticalgapsinclude:

1. Shortageofstudiesevidencingactualimprovements instudentlearningoutcomes

2. Limitedcriticalanalysisoflong-termsocietalimpact andrisks

3. Need for conceptual frameworks and adoption roadmapstailoredtoeducation.

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4. Lack of interpretation from an interdisciplinary learningsciencesperspective

5. Scarcityoflarge-scaleimpactevaluationsandpilots forAIEDsolutions.

3 Methodology

Amixedmethodsresearchparadigmwas adoptedforthis study, combining qualitative data from literature analysis and expert interviews with quantitative survey data. This pragmatic approach harnesses the strengths of both methodologiestoenableadeeperexaminationofAI'simpact on online education and marketing management from pedagogical,technological,andethicalviewpoints.

3.1 Research Design

Qualitative content analysis methodology was applied to code and synthesize data from literature and interviews, interpreting expert opinions. Quantitative descriptive analysis of survey data provided supporting numerical statisticsevidencingactualadoptionrates,effectiveness,and challenges of AI in education; by triangulating multiple sources, the mixed methods design aimed to bolster reliability and achieve comprehensive, multidimensional insightsonAI'simpacts.

3.2 Data Collection Methods

 Literatureanalysis: RecentempiricalstudiesonAI in education over the past five years were examined, applying inclusion criteria of being publishedinpeer-reviewedscholarlyjournalsand relevancetothescopeofAIcapabilitiesandimpact.

 Expert interviews: Semi-structured interviews wereconductedwithtenindustrythoughtleaders acrosseducationtechnology,policy,andacademia tocaptureinformedperspectivesonAI.

 Onlinesurveys: Questionnairesweredistributedto 500 educators across K-12 and higher education institutestoquantifyAIadoptionratesanddiscern keyfactorsinfluencingintegration.

3.3 Data Analysis Methods

The methodology for this project was a combination of contentandstatisticalanalysis.Contentanalysisintheform of hermeneutic reading of literature and interviews using coding was used to derive themes and, ultimately, conclusions from the material. Statistics was the form of movingfromthegeneraltospecificsingeneratingthemes, thatis,movingfromthepercentageoftimethepopulation spent in AI discussion to higher and higher percentages spent in that discussion, for example, 90%. General or descriptive statistics included percentages, means, and standard deviations for survey data. The constant

comparativemethodwasemployedtocompareandcontrast thequalitativeandquantitativefindings.Resultsgatheredby thesemeansprovidedarobustargumentgiventhestudy's goal of providing policymakers with practical, evidencebasedguidanceforhowAImightimpactandcouldbeused infutureeducationstrategy.

4 Results and Discussion

4.1 Advantages and Possibilities of AI in Education

IntegratingAIineducationhasintroducedvariousbenefits spanningoperationalefficiency,personalizedlearning,and enhancedstudentoutcomes.

4.1.1 Improving Administrative Efficiency

Automationofmanual processes: AIassistsmuchexciting workandcostsvidingautomationofdocumentprocessing, applicant screening, chatbots answering admission and enrollmentFAQ,etc,whereCollegescanallocatethesavings tohigh-valuework(Huangetal.,2021).

Optimizing predictive decisions and resource allocation: AIisagreathelptooptimizepredictivedecisions and is an excellent assistance in planning intelligent decisions such as AI algorithms, data following of student performancethroughLearningManagementSystems(LMS) or Big data, institutional data warehouse through which institutecantakeimportantdecisionforupcomingstudents demand,whichsubjectsshouldberun,whatbudgetshould beprovidedonscholarships,etc.(Popenici&Kerr,2017).

4.1.2 Fostering Personalized and Adaptive Learning Experiences

Matching user interests and needs, this strategy can also provide customized content recommendations, which empowersindividuallearnerstolearnautonomously(Zhai. etal.,2021).Thisstrategyallowslearnerstokeeppacewith therightlevelofinstructionbyidentifyingknowledgegaps and misconceptions in an ongoing way and providing the requiredmaterialtofillthegapsindividually(VanderVlies & Vincent-Lancrin, 2020). Unlimited on-demand virtual tutorstohelpunderstandtheproblems(Yangetal.,2021). For example, gamification and immersive learning environments such as virtual reality also can enhance learningmotivation,studentengagement,etc.(Alam,2021)

4.1.3 Enhancing Learning Outcomes and Employability

 IntelligentorderingoflessonswhereAIcanidentify skill gaps and reduce repetition Grading that is rapid, reliable, efficient, and involves less scorer variabilitythroughtheuseofAI(Chenetal.,2020)

 Scaling feedback that personally aids student masteryandconceptcomprehension(Sharmaetal., 2021)

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 Systemsforcareerdirectionsthatmatchstudent's strengthsandattributestotheworkforce(ZawackiRichteretal.,2019).

4.2 Limitations and Risks of AI in Education

However,multiplestudiesalsoevidencenotablelimitations andconcernsoverAIadoption.

4.2.1

High Development and Training Costs

 Huge initial investments are required in order to producedetaileddatasets,infrastructure,aswellas specialized artificial intelligence and machine learning knowledge and skills, which might not providethedesiredimpact(Pedroetal.,2019)

 Continual costs of maintaining the system and updating it as newer data becomes available and trainingnewmodels(Popenici&Kerr,2017)

 Internal competence by reskilling the staffing in artificial intelligence or dependent on external EdTechcompanies(Lee,2020)

4.2.2 Lack of Transparency and Explainability

 TheinabilityofAItoexplainwhyitcameupwitha specific prediction orconclusionisalsoknownas theblackboxeffect(Rajasingham,2009).

 HumanscanoverseeandaccountforAI'sautonomic decision-making(Cox,2021).

 AI is only as good as the data it has been fed- for example,AIcouldacquireracialandsocialbiasesby learningfromhistoricaldataand,asaresult,explain VanderVliesandVincent-Lancrin.

4.2.3 Negative Implications for Employment and Inequality

 Faculty members see the promotion of massificationanditsthreattofacultyjobsandtask control in automated teaching and assessment (Zawacki-Richteretal.,2019).

 Thereareseveralreferencestopossibledigital(dis) advantages to students who have access to/capabilities in using the systems (Pedro et al., 2019).

 Bysomeaccounts,thepotentialblurringandeven increase of the gap between sound and poorlyequippedinstitutions(Sharmaetal.,2021).

4.3 Impact of AI on Online Education and Marketing Management

The analysis reveals that AI profoundly enhances operationalefficiency,learningpersonalizationandstudent outcomes within online education. Automated administrationofadmissions,enrolment,tuitionpayments, etc,tacklerisingcomplexityfromsurgingglobalenrolments inflexiblelearning(Rajasingham,2009).

Virtual teaching assistants, intelligent course sequencing, and adaptive learning content boost retention and completion rates, while predictive analytics strengthen enrollmentmanagementanddropoutidentification(Pedro etal.,2019).Inmarketingmanagement,AIoptimizesdigital campaignsandfunnelanalysiswhilelearninganalyticsgives unparalleledinsightintostudentbehaviorsandinfluencers, guidingrecruitmentandpositioningdecisions(Kennedyet al.,2020).

However,alackoftransparencyarounddatausageandthe tendency to see students as metrics risks undermining human relationships that are foundations of positive learning environments (Lewis, 2021). Striking a balance betweenscaleefficienciesfromautomationandpreserving meaningful human interaction is critical for ethical AI adoption.

5 Conclusion

AI/ML enabling technologies are transforming online educationintheautomationofrepetitivetasks,data-driven decision optimization, personalized learning at scale, and enhanced student outcome(s). Intelligent systems can expandaccess,affordability,equity,andqualityofeducation tounlockthehumanpotentialglobally(Zawacki-Richteret al., 2019). However, AI technologies also raise several significantrisks,therebynecessitatingthedevelopmentof moretrustworthy,human-centeredsystemsthataugment, rather than replace, humans. These risks include transparency, bias, inequality, employment, ethics, and humanwell-being(Hwangetal.,2020).

Achieving the potential of AI depends on partners from a widerangeoftechnological,learning,andpolicydisciplines who work together to develop appropriate governance, includingframeworksthatbalanceinnovationopportunities with ethical obligations (Shen et al., 2021). Similarly, educationinstitutionsmustbalancescaleefficienciesfrom automationandpreservehumanrelationshipsfundamental forpositivelearningcultures(Lewis,2021).Furtherpilots thatcandemonstratethescaledimpactonjobs,skills,and educationaloutcomeswillbuildconfidenceforthebroader adoption of AI and also show where to focus. The tool is always in service of societal progress; human-centered systemsthatsupportratherthandisrupthumangrowthare critical.

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6 Future Scope

Multiple promising applications warrant deeper ongoing explorationasAIineducationmatures,including:

 Hybrid AI-human teacher models blending automated personalization with emotional connection

 Leveraging extended reality for experiential learning, simulation training, Ethics frameworks, andpoliciesguidingresponsibleAIadoption.

 ToolsdemocratizingAIforeaseofusebyeducators with low technical skills Solutions enhancing inclusion for disabled, remote, or economically marginalizedstudents

 Impact on 21st-century skills and Employability fromemergingjobsandIndustriesImplicationsfor Organizationalculture,workforceproductivity,and Professionalidentities.

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BIOGRAPHIES

Suyog Sunil Shinde is a research scholar at Neville Wadia Institute of Management Studies & Research,Pune.Hefocusesonthe integrationofartificialintelligence in education, aiming to enhance learningexperiencesandoutcomes throughinnovativetechnologies.

Dr. Amit Medhekar is a professor at Neville Wadia Institute of Management Studies & Research, Pune.Withextensiveexperiencein educational technology and management, Dr. Medhekar's research interests include the application of AI in education, strategic management, and organizationaldevelopment.