International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
Building Intelligent Adaptive User Interfaces (IAUI) With Artificial Intelligence & LLM Models.
Leela Naga Sai Vamsi Krishna Dogiparthi
University Of Bridgeport, USA
Abstract – As the number of users leveraging online platforms over the internet is growing rapidly has created unmeasurable competition over the digital landscape demanding a need for a more personalized, intelligent and adaptive UI framework that can adjust itself based on the various factors such as user needs, environment, level of user understanding, historical usage patterns, age groups etc. to better serve the end user & provide more value for the time they are spending. All these needs demanded a revolutionary approach for building intelligent User interfaces known as Intelligent Adaptive User Interfaces (IAUI) powered by ArtificialIntelligence,LLMModels&Empathy.Thisarticlewill delve intotheconceptofIAUI,anovelframeworkforadjusting UI dynamically, increase user engagement, decrease the learning curve & increase accessibility in sectors such as Ecommerce, healthcare, financial services, educational platforms&entertainment byusingsomeofthepre-builtLLM models using Retrieval-Augmented Generation (RAG).
Key Words: Intelligent Adaptive User Interfaces (IAUI), Artificial Intelligence (AI), Large Language Models (LLM), Empathy,Accessibility,Adaptability,UserExperience(UX), Retrieval-Augmented Generation (RAG), unsupervised learning
1.INTRODUCTION
Over the past two decades the number of users over the internethasgrownexponentially,representingaparadigm shift in human-computer interactions In 2000, the user numberwereabout7%ofglobalpopulation(~413million users)[1]. In 2010, it was reported to be 29% of global population (~1.9 billion users) [1]. In 2020, it went up to 60%(~4.7billionusers)[1].In2025,itwasreportedtobeat 67.9% with whooping ~5.56 billion users [2], [3] across varioussectorssuchasHealthcare,E-commerce,E-learning, Entertainment&Socialmedia.
Chart -1:UserGrowthChart(2000–2025)
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
This huge flux of users over the internet has created immense opportunities and unending competition for businessestoattractmorecustomers, retainoldcustomers, providemorevaluetotheirtimespenthascreatedinevitable need for more intelligent UIs which can not only change itself based on the screen size of the device, but also representitselfwithmoreintuitiveandtailoredinformation basedonthekeyfactorsoftheuserinteractingwithit.
Ifthisinescapabledemandwasanaskdecadeago,itmight have been a difficult demand to meet. But over the last decade, the paradigm shifts in the capabilities of Artificial Intelligence (AI), Large Language Models (LLM), Machine Learningtechniques&ComputingpowerofServers.Some thegrowthrateshavebeenshowcasedbelow
Table-1:ComputingPowerGrowthRateoverthepast2 decades[4],[5]
Period
2005–2010 1.1petaFLOPs 1.5x BasicML models(e.g., NPLM)
2011–2015 470petaFLOPs →1exaFLOP 2.3x Image recognition error↓73%
2016–2020 1.9exaFLOPs→ 314exaFLOPs 4x GPT-3(175B params)
2021–2025 2.7zettaFLOPs →4zettaFLOPs 2.3x GPT-4(86% MMLU accuracy)
Along with the computing power of the AI machines, the improved capabilities of user devices have made it easier andpossibletoinjectIntelligentAUIsontothefaceofusers withoutcompromisingontheresponsetimesandthequality ofuserexperience.
InThisarticlewillfurtherdelveintovariousUserExperience Designconcepts(pre-stepstoIAUI),systemarchitectureof anIAUI,realtimeapplicationoftheIAUIincurrentmarket, Performancemetrics,Challengesandpossiblesolutions.
2. RELATED WORK
TheAdaptiveUserInterfaceconceptwasfirstdiscussedby DavidBenyoninhisarticleaboutdesigningadaptivesystems forsolvingusabilityproblems[6].Asthiswasanearlyphase ofresearchonadaptability.Thepaperdoesn’tpresentasolid implementationguideforcreatingaadaptiveuserinterface.
Accordingtoanoriginalresearchstudyon“SmartProductServiceSystems”anovelbusinessapproachemphasizesakey
techniquetodesignaAUIistofollowaclosed-loopdesignto allow both customers and providers to work together in variousstagesofdesignprocesstobuildamorecustomized userexperiencemeetinguserneeds[7].Thiscomprehensive studyhasexploredhowtheseadaptiveuserinterfacescanbe appliedtomultipleIOTDevices,Wendingmachines,Smart homedeviceswithuseofpowerfulcustomMachineLearning Models. Thispaperdoesn’ttalkabouthowwecanusesome prebuiltLLMModelswhichleverageunsupervisedlearning tobuildpersonalizedintelligentuserexperiences.
According to another original research study on various design methodologies or techniques for creating more meaningful “User Experiences” is to use Empathic design principles powered by a powerful human emotion called empathy.Anabilitytofeelandunderstandother’semotions [8].Thisresearchemphasisthefundamentalimportanceof empathy in design process and how to implement this techniquetodesignmorepersonalized“Userinterfaces”.
We will be discussing this concept in depth in upcoming sectionsofthearticle.
2.1 Intelligent Adaptive User Interface (IAUI)
Intelligent Adaptive User Interfaces (AUI) is a powerful frameworkallowinguserexperiencewhichisself-reliant,& canchangetheirappearance,content,sizebasedonvarious key user metrics, environment conditions, location of the user/device, device attributes etc. in real-time. Along with their self-reliance, they are also self-taught. Which means theyevolveeveryday,everyminute,everysecondwithevery userinteraction.Theykeeplearningfromthedataitcollects, byfeedingthembacktothedesignprocessformingacyclic process.
ThereweremanytechniquestodevelopIntelligentAdaptive userinterfaces(IAUI)fromdesignphasestoinferencing.In this paper weare concentrating on using pre-trained LLM models to perform various operations in our proposed systemarchitecture.Whichwewillbegoingoverindetailin thebelowsections.
3. PROPOSED SYSTEM
The Proposed Intelligent Adaptive user interfaces (IAUI) systemhasthreemajorcomponents.
1. UXDesignEngineering
2. AIEngine.
3. InferenceEngine.
3.1 UX DESIGN ENGINEERING
UX Design Engineering plays a crucial role in designing adaptive user interfaces, which can solve a real user problem. No matter how intelligent your interfaces are, if they are not designed based on real user problems. They won’tbeadhered,acceptedbytheusers.So,havingagood
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
design system is a crucial part of the Intelligent Adaptive UserInterface(IAUI)framework.
Twoofthebesttechniques/methodologiestodevelopUser Centric,personalizeduserexperiencesarebeingdiscussed inthefurthersections
1. Closed-LoopDesign.
2. EmpathicDesign.
3.2 CLOSED-LOOP DESIGN.
Closed-Loop Design emphasizes on active participation of the end user & business directly or indirectly in different stagesofUserExperiencedesignprocess[7].Someexamples ofthosestagesare
1. RequirementGathering:Inthisstagetheenduser can be the source of requirements, providing us detailed input on how a user’s experience can resolvetheirneeds.
2. DesignValidation:Duringthisstage,thedesignsare validated with users before any development can start
3. POCValidation:Duringthisstage,theendusercan beourfirstvalidatorprovidinguswithfeedbackon howwellthedesignsadheretotheirrequirements orhowtheydeviatefromthem.ThisPOCvalidation stagewillallowustoquicklycorrectanymistakes, beforeitgoestothenextphase.
4. FinalProductValidation:Duringthisstage,theend userorcustomercanbeavalidatorofourdeployed version of the user designs. This stage will allow themtoconsumethemasanenduser,playaround withitandprovideusbackwithmoreconstructive feedback.
Followingthisclosed-loopdesignprocesshelpsustobetter connect with users and develop more meaningful experiencesforthem.
3.3 EMPATHIC DESIGN
Empathic Design framework has been one of the best frameworksfordesigningUserExperienceswhicharecloser to what a user needs. The research and importance for “Empathic Design” was emphasized in a book “Empathic Design”byKoskinen&Battarbee[9].Accordingtoitthebest waytodesignauserexperienceisbyunderstandingtheuser needs, having empathy towards his or her feelings, will enableuswithmorepowerfulthoughtstodesignsomething whichwilltakeusclosertotheuser’sheart.
Overthepasttwodecades,Empathyhasbecomeanessential component in the “User Experience Design” process for producing more human centric design bases on the real people perspectives. It also enabled designer to design solutionstoproblemsbasesonrealuserexperiences[8]
Themajorcharacteristicsof“EmpathicDesign”are
1. Finding equilibrium between Rationality and emotion
2. Defining end users as the source of requirements and partners in the design process along with stakeholders.
Someoftheadoptionandsuccessratesof“EmpathicDesign” overthepasttwodecadesareshownbelow.
Table-2:Adoptionandsuccessratesof“EmpathicDesign” overthepasttwo.[10],[11]
Period Adoption rates Success rates
2005–2010 ~40%adoptionin healthcaresectors -20–30%ofreduction inredesigncosts -25%higheruser satisfaction
2011–2015 50-60% -30%fasterdecision making -40%reductionin designbias
2016–2020 65–70% -55%increasein innovation
2021–2025 75–80% 60%increaseinuser satisfactionrates
Fig - 1: UXDesignLifeCycle
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
3.3 AI ENGINE
AIEngineformsthebrainoftheIntelligentAdaptiveUser interfaceswhichactasanAdapterbetweentheLLMModels, andinternalsystems.
AI Engine is primarily built on “Unsupervised pre-trained LLM Models” to avoid “Training costs for customized models” increase availability and decrease maintenance costsforthecompanies.
AI Engine user data that it receives from the “Inference Engine” and system database using RAG, with system prompts to instruct LLM to perform some unsupervised learning of user key metrics to perform “Profile Scoring”, “Geo-location identification”, “Time Detection”, “Device Information”&BuiltRAGDatatorespondtotheinference engineonwhichUIComponentto beusedforthespecific user, theme to be used based on the location and time, “Content Cultures” to be used to display more localized content.
Wehaveusedthreepopularmodels(gpt-4.o,gpt-4.5,gpt-4oturbo)tocomparetheirperformanceintermsofaccuracyof “Profile Scoring”, “Theme detections”, “UI Component Classification”.
3.4 INFERENCE ENGINE
InferenceEngineformsthemaininteractionlayerbetween users,userinterface&AIengine.Itsmainresponsibilityisto collectuserdatafromouruserstores,componentlibraries, location&deviceinformation,applydatatransformations, aggregate the data and pass it back to the AI Engine for contextawarenessforfurtherevaluation
Itisalsothemainlayerwhichcollectsdataagainstourusers etc.topersistinourdatastores.
Finally,beforesendingbacktheresultsitperforms“Output processing”totransformstheresultsbacktosomethingthat UIcanparse&understand
5. SYSTEM ARCHITECTURE
6. METHODOLOGIES
Ourproposed“SystemArchitecture”implementssomeofthe below methodologies to explore the use of pre-built LLM capabilitiesfordesigningmoreintelligentuserexperiences.
1. Retrieval-AugmentedRetrieval(RAG).
2. PromptEngineering.
3. Plugins&Filters.
4. ProfileScoring.
5. Un-supervisedlearning.
6. Natural Language Processing & Natural Language Understanding.
“Retrieval-AugmentedRetrieval (RAG)”isused toprovide contextualinformationaboutthesystemspecificattributes astheLLMsaretrainedwithnon-systemspecificattributes andtheinformationithasmightbeoutdated.So,usingRAG you can provide context to it which is only specific to a systemandexpecta“Grounded”responsefromit.Withoutit the model might hallucinate and provide unrealistic response.
“Prompt Engineering” is used to provide some critical instructionstotheLLM,suchasitsbehavioralcontext,what itsupposedtodo,whatitisnotsupposedtodo,directionsto how & from where it can retrieve contextual information usingspecific plugins&filters,formatofinput&outputit supposedexpectandproduce.Toachievethedesiredresults fromanLLMmodel,havingagoodpromptstrategyisvery crucial.Wehavefollowed3Sprinciplesfordesigningthe prompts.
“Plugins”arethewayLLMmodelwillreactwithoursystem toperformcertaincustomoperations,gettingsystemdata forRAGetc.
Fig - 2 IAUI System Architecture.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
“Filters”areusedtofilteroutanybadinputsorpromptsto be fed into LLM and to filter any unexpected data coming fromtheLLMtosaveguardthesystem.
“ProfileScoring”–Wearescoringprofilebasedontheuser featuressuchasloginfrequency,loginactivetimes,age.This scoreiscalculatedbytheLLMmodel,whichisthenusedto correlatetheusertorespectiveUIcomponents,categorized basedontheirlevelofusabilityandscorebands.Allthiswas doneusingpretrainedLLMmodelswithoutneedingtotrain anycustommodels.
“Un-supervised learning” - we are using pre-trained LLM models, which are more generalized Machine learning models trained not specifically for our system. This can significantlysavethecustommodeltraining,maintenance andinferencecosts.Thisoutcomecanboostmanysmallto medium businesses which don’t want a headache of incurringalltheabovecosts.
“Natural Language Processing (NLP) & Natural Language Understanding”–weareusingnaturallanguagepromptsto instructLLMtobehaveasweareexpectingittowork.This techniqueutilizesNLP,NLUcapabilitiesofLLM,todefineits characteristics.
WewillbeprocessingouroutputfromLLMinJSONformats.
7. RESULTS
We have performed some comprehensive testing for a sample size of 100 users, 10 sample UI components for differentlevelsofusers.Theuser’ssamplewasdistributed across 5 different locations with different Time Zones, different times of the day for verifying environmental awareness of the system. Below are some of the comprehensivetestresultsacrosstheLLMmodelsusedfor thesame“TestData”.
7.1 Profile Scoring
Wehaveimplemented“ProfileScoringModule”ontheabove testdata,tocategorizeusersinto5categories
- 0-20:VeryLow - 21-40:Low - 41-60:Medium - 61-80:High - 81-100:VeryHigh
Table–3:“ProfileScoring”Moduleresultsusing“Random ForestRegression”.
Although the precision of the result matters, sometimes whenyouareworkingbands,theresponsetimeoftheLLM becomesakeyfactoraswell.Belowaretheresponsetimes oftheLLMmodelswehaveusedsofar.
Table-4:LLMModelsResponseTimes
Model Response Times GPT4.o 2.896s GPT4o-mini 1.89s GPT4.1 1.98s GPT4.1-mini 1.78s
7.2 Environmental awareness:
We have tested our LLM models to recognize the user application theme based on the environmental awareness such as setting theme to “light” during daytime & “dark” duringnighttime.WehavefedtheLLMpromptswithuser location,timezone,customtimeplugintofetchthecurrent userlocationtimebasedonthe“IANATimeZone”.
We have tested the LLMs using 100 user samples, spread across5differentTimeZones.Wehaveexecutedthetestsat different“America/New_York”timezonetimestodetectthe themedetectionbehavior
Time zones used: - America/New_York. - Europe/London - Asia/Tokyo - Australia/Sydney - Asia/Kolkata
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
Test Case Execution Times:
Table-5:TimeZoneandTestCaseExecutionTimes
Time Zone Used Test Case Execution Time
America/New_York 9AM
America/New_York 12PM
America/New_York 4PM
America/New_York 9PM
BelowarethetestresultsbyLLMmodel,TimeZone,Theme detected.
Table-6:Model,TimeZone&Themedetectionresults
Model Time Zone(s) Test Result
GPT4.o
America/New_York, Europe/London, Asia/Tokyo, Australia/Sydney, Asia/Kolkata Passed
GPT4o-mini America/New_York, Europe/London, Asia/Tokyo, Australia/Sydney, Asia/Kolkata Passed
GPT4.1
America/New_York, Europe/London, Asia/Tokyo, Australia/Sydney, Asia/Kolkata Passed
GPT4.1mini America/New_York, Europe/London, Asia/Tokyo, Australia/Sydney, Asia/Kolkata Passed
7.3 Localization awareness:
We have also tested the LLM models to provide the right localizationinformationforshowingtheUIcomponentsin thenativelocallanguages.TheresponsefromtheLLMwas expectedtoberespondedwithISO639-1format.Wehave used5differentcountriesfortestingthis.
- Australia.
- Japan.
- India.
- UnitedStates.
- UnitedKingdom.
Table-7:ISO639-1LanguageCodedetectiontestresults.
Model Time Zone(s) Test Result
GPT4.o Australia, Japan, India, UnitedStates, UnitedKingdom Passed
GPT4o-mini Australia, Japan, India, UnitedStates, UnitedKingdom Passed
GPT4.1 Australia, Japan, India, UnitedStates, UnitedKingdom Passed
GPT4.1mini Australia, Japan, India, UnitedStates, UnitedKingdom Passed
7.4 Component Profile Mapping:
WehavetestedtheLLMsabilitytocorrelatecalculateduser profilescores with the UIcomponentsforloadingvarious skinsoftheUItomakeitmoreusable,easytounderstand andnavigatefortheenduser.WeproactivelyratedtheUI componentusabilityscoreinto5categories.
- 0-20:VeryLow
- 21-40:Low
- 41-60:Medium
- 61-80:High
- 81-100:VeryHigh
LLMs has performed relatively well in mapping the UI componentstotherightsetofusers.Belowaresomeofthe test results. With fine tweaks to the prompt, it will be possibletoachieve100%resultswithLLMs.
Table8:LLMProfile-UIcomponentMappingResults
Model UI Components Test Result (%)
GPT4.o <basic-navigatio> <advanced-navigation> <mid-level-card> <advanced-card> <basic–card> 95%
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
GPT4o-mini <basic-navigatio> <advanced-navigation> <mid-level-card> <advanced-card> <basic–card>
GPT4.1
<basic-navigatio> <advanced-navigation> <mid-level-card> <advanced-card> <basic–card>
GPT4.1mini <basic-navigatio> <advanced-navigation> <mid-level-card> <advanced-card> <basic–card>
8. DISCUSSION
This section of the document will discuss more about our findingsbyusingpre-trainedLLMmodelstosolvesomeof the challenges related to using custom trained models, limitations of using LLM models, challenges of using LLM Models&possiblesolutionstosomeofthosechallenges.We will be also discussing about the future of Intelligent Adaptiveuserinterface(IAUI)tobuild“GenerativeUI”
PerformingacomprehensiveDesignStudyforUXdesignis outof scopeof thisarticle. We have providedsome of the concepts we recommend for designing User Experiences. Testingthemisoutofscopeofthisarticle.
8.1 Challenges & Possible Solutions
“Hallucinations” - LLM models are known to hallucinate, whenprovidingresponses.Thepossiblesolutiontoitisto reduce “temperature”, “top” parameters. Along with it providing strict guidelines with “System Prompts”, “ContextualData”withRAGwillhelpittonothallucinate.
“Bias Removal” - As the LLM models are trained with generalizeddata,toavoidbiasfromyoursystem.Youshould bemakingsureimplementingcustombiastechniquesusing preandpostprocessingfilterstoremove“Bias”completely fromthesystem.
“TokenLimitations” -althoughLLMscanworkonprompts& RAG.Theyhavetokenlimits,whichdoesn’tallowyoutosend too much data to it. This limitation can create inaccurate results.Someofthetechniquestoovercometheseproblems are
“Truncation”–Truncatingfromstartortheendof the sentences to meet the token limitations. The problemwiththisisthatitcanlosecontextandcan stillcreateinaccurateresults
“Chunking”–Breakingdowntheinformationinto smaller chunks, which will help to be processed independently and within the token limitation. “Chunking” can be done semantically to not loose contextualinformation.
“Summarize”–Summarizethelengthyinformation intomoreprecisesentenceswithoutloosingtheir contextualinformation.Thiscandrasticallyreduce theamountofinformationyouaresendingtoLLM.
“RemoveRedundantTerms”–Removingredundant terms such as stop words, will also reduce the amountofinformationbeingfedtoLLM.
Dependingontheusecaseyoumightendupimplementing one or more above techniques to keep the information shared within the token limitation boundaries and yet achievemorepreciseresults.
“Scaling”–Astheuserbaseincreases,thenumberoftimes weperformthese“ProfileScoring”,“ComponentMapping”, “Environment awareness” using LLM models increases as well.So,soonwewillgetintoapositionwherethesystem needs to be scaled. One solution, As the LLM models are managed by the third-party providers on their cloud systems, they can easily scale and we can buy more dedicated equipment to run the models from the cloud providers such as Azure, AWS etc. Another solution is to acquire powerful machines on prem or on cloud (VM) & deploy LLM models locally.This allows us to control over whentoscaleupandscaledown,staggeringmodelupdates etc.
“Dataprivacy”–LLMmodelsareusedbymanyapplications acrosstheworld.Toavoiditsniffingintoyourdatafor its training.Youshouldbeimplementingsafetymeasuresatthe modelproviderlevelaswellasintheapplicationtoredact PIIinformation&anyotherpersonalinformationfromthe databeingsharedtoLLM.Thismightimpacttheaccuracyof themodel,buttonoteffecttheaccuracyoftheresponse,we canshareitas“contextualinformation”uponwhentheLLM modelneedsitbeforeprovidingfinalresponse.
“Prompt Creation” – Producing a right prompt is a very crucialstepforintegratingwithLLMmodelandgettheright informationoutofit.CreatingPromptisaniterationprocess andmighttakealotoftimetoproducetherightpromptthat will work for your usecase. There assomegreat“Prompt Engineering”principlestohelpuscreateapowerfulprompt. Oneofthemostusedandgreatprincipleis“3SPrinciple”to craftbetterprompt.[12]
8.2 Advantages of using prebuilt LLM Models
Asweknow,howpowerfultheLLMmodelscanbeandhow they can help us achieve building more modular and adaptiveinterfaces.Tocreateortrainacustommodel,we
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
needalotofmanpowerandcomputepower.Belowaresome oftheTrainingcostsofsomeofthepopularLLMmodels.
Table-9:LLMmodeltrainingcosts [13]
Model Parameters Training Cost Key Factors
GPT3 1.75B $4.6 milion
NVIDIAV100 GPUs
GPT4 1.8T+ 78-100 million Specialized hardware (H100GPUs)
Falcon7B 7B 30K–450K
GPUfine tuning
Table10:Maintenance&ServingCosts.[14],[15]
Model Deployment Monthly Cost Components
Falcon7B On-demandGPU 1.006/ho ur(724/ month)
Llama3-8B
AWS (g5.2xLarge)
Llama3-70B AWS (ml.p4d.24xlarg e)
872-1745
SingleNVDIA V100
Single-node GPU, autoscaling
$27,360 Specialized hardware (H100GPUs)
Manysmallerandmedium-scaleorganizationswouldfindit hardtotrain&maintainLLMmodelsontheirownbecause ofthecostassociated. Whichtakesoutanopportunityfor themtobuildIntelligentUserexperience.
ButwiththeuseofprebuiltLLMmodels,theycandrastically reducethecoststoonlyinferencecosts,&cloudproviders areprovidingvariouspaymentplanstofittheorganization needs,whichcanenableeveryorganizationtoleveragethe beautyofartificialintelligenceintotheiruserexperience.
8.3 Real Time Applications:
Building Intelligent Adaptive User interfaces has already taken its shape by many major organizations building variousintelligentmodulesusingCustomMachineLearning Models.Beloware someoftheinstances of those by their functionality.
Table11:RealTimeIAUIimplementations.[16],[17],[18], [19]
Functionali ty Improvement (%) Organization
Recommend ation systems 40%ofuserretentions fromamazon, 75%ofuserengagement increasesbyNetflix. 30%
Amazon,Flipkart, Shopify, Netflix
ProfileLevel &Paced learning 14%userretention, Duolingo
Environmen tal Awareness 30%userengagement increase GoogleMaps
Intelligent ChatBot 40%decreaseincustomer carecases Amazon
Personalize dPlaylists 40%increaseinUser engagement Spotify
9. CONCLUSIONS
Intelligent Adaptive user interfaces (IAUI) provide a new paradigmformorepersonalizeduserinterfacesincreasing, userengagement,improvingaccessibility&efficiencyofthe systems. Although they provide great opportunities for businesses to take them closer to the user’s needs. They come with their own challenges related to data privacy, development&maintenancecosts,designcomplexities,high computerequipment&datacollection.WiththeuseofpretrainedLLMModels.Theremightbeachanceofnotmeeting customers’needsastheyarevastandhavebeentrainedfor multipleusecases.Onepossiblesolutiontothischallengeis tousecustomtrained“SmallLanguageModels”.Whichare trainedforspecificusecasestonot
InmanycasesthepotentialbenefitsofimplementingIAUI outshineitschallenges,especiallyformanywell-established companies,whichhaveenoughresourcestospendonthem.
Finally,IAUIpavesthewayformorepowerful“Generative UI”toachievecompleteautonomyondesigning,building& maintaining personalized UIs, blurring the line between design&UI[20]
REFERENCES
[1] T. Pangarkar, “Internet Usage Statistics and Facts (2025),” Market.us Scoop. Accessed: May 01, 2025.
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072 © 2025, IRJET | Impact Factor value: 8.315 | ISO 9001:2008
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
[Online].Available:https://scoop.market.us/internetusage-statistics/
[2] “Digital2025:GlobalOverviewReport,”DataReportal–Global Digital Insights. Accessed: May 01, 2025. [Online]. Available: https://datareportal.com/reports/digital-2025-globaloverview-report
[3] “Digital Around the World,” DataReportal – Global Digital Insights. Accessed: May 01, 2025. [Online]. Available: https://datareportal.com/global-digitaloverview
[4] J. Sevilla, “Can AI Scaling Continue Through 2030?,” EpochAI.Accessed:May01,2025.[Online].Available: https://epoch.ai/blog/can-ai-scaling-continue-through2030
[5] C. Giattino, E. Mathieu, V. Samborska, and M. Roser, “Artificial Intelligence,” Our World Data, Sep. 2023, Accessed: May 01, 2025. [Online]. Available: https://ourworldindata.org/artificial-intelligence
[6] David Benyon and D. Benyon, “Adaptive systems: A solutiontousabilityproblems,” UserModel. User-Adapt. Interact., vol. 3, no. 1, pp. 65–87, Mar. 1993, doi: 10.1007/bf01099425.
[7] A. Carrera-Rivera, F. Larrinaga, G. Lasa, G. MartinezArellano,andG.Unamuno,“AdaptUI:AFrameworkfor thedevelopmentofAdaptiveUserInterfacesinSmart Product-Service Systems,” User Model. User-Adapt. Interact.,vol.34,no.5,pp.1929–1980,Nov.2024,doi: 10.1007/s11257-024-09414-0.
[8] A.TellezF.andJ.Gonzalez-Tobon,“EmpathicDesignas a Framework for Creating Meaningful Experiences,” Conf. Proc. Acad. Des. Innov. Manag.,vol.2,no.1, Nov. 2019,doi:10.33114/adim.2019.03.408.
[9] “EmpathicDesign-kirja0.997.indd”.
[10]“Challenges of Doing Empathic Design: Experiences fromIndustry,”InternationalJournalofDsign.Accessed: May 03, 2025. [Online]. Available: https://ijdesign.org/index.php/IJDesign/article/view/1 008/403
[11]“Empathicdesign,” Wikipedia.Mar.08,2024.Accessed: May 03, 2025. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Empathic_ design&oldid=1212488763
[12]“3SPrincipleforPromptEngineering(GitHubCopilot)Peter Miľovčík - Obsidian Publish,” Peter Miľovčík. Accessed: May 12, 2025. [Online]. Available: https://publish.obsidian.md/petermilovcik/Knowledge
/3S+Principle+for+Prompt+Engineering+(GitHub+Copil ot)
[13]E.O.PooleRichard,“Whatisthecostoftraininglarge languagemodels?,”CUDOCompute.Accessed:May13, 2025. [Online]. Available: https://www.cudocompute.com/blog/what-is-the-costof-training-large-language-models
[14]“Breaking Down the Cost of Large Language Models | JFrogML.”Accessed:May13,2025.[Online].Available: https://www.qwak.com/post/llm-cost
[15]“WhatistheCostofTrainingLLMModels?KeyFactors Explained.”Accessed:May13,2025.[Online].Available: https://botpenguin.com/blogs/what-is-the-cost-oftraining-llm-models
[16]R. E. -, “Enhancing User Experience through Recommendation Systems: A Case Study in the EcommerceSector,” Int. J. Multidiscip. Res.,vol.6,no.4,p. 24598, Jul. 2024, doi: 10.36948/ijfmr.2024.v06i04.24598.
[17]“Enhancing User Experience Through AI-Powered Personalization in UI Design,” Int. J. Adv. Res. Sci. Commun. Technol..
[18]N. T. Blog, “Recommending for Long-Term Member Satisfaction at Netflix,” Medium. Accessed: May 13, 2025. [Online]. Available: https://netflixtechblog.com/recommending-for-longterm-member-satisfaction-at-netflix-ac15cada49ef
[19]“Research-Duolingo.”Accessed:May13,2025.[Online]. Available:https://ai.duolingo.com
[20]“GenAIUI Whitepaper 2024.pdf.” Accessed: May 01, 2025. [Online]. Available: https://6082761.fs1.hubspotusercontentna1.net/hubfs/6082761/2024%20Whitepaper/GenAIU I%20Whitepaper%202024.pdf
BIOGRAPHIES
2025, IRJET | Impact Factor value: 8.315 | ISO 9001:2008
InnovativeEnterpriseApplications architect | Microsoft certified professional (MCP) | AI Engineer tryingtocreatenovelsolutionsto hard problems using Artificial Intelligence.