{"id":"https://openalex.org/W7160515609","doi":"https://doi.org/10.48550/arxiv.2605.04761","title":"Cognitive Twins: Investigating Personalized Thinking Model Building and Its Performance Enhancement with Human-in-the-Loop","display_name":"Cognitive Twins: Investigating Personalized Thinking Model Building and Its Performance Enhancement with Human-in-the-Loop","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160515609","doi":"https://doi.org/10.48550/arxiv.2605.04761"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04761","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002047082","display_name":"Wu\u2010Yuin Hwang","orcid":"https://orcid.org/0000-0001-5684-3590"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hwang, Wu-Yuin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099126348","display_name":"Nur Alif Ilyasa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ilyasa, Nur Alif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006473675","display_name":"Muhammad Irfan Luthfi","orcid":"https://orcid.org/0000-0001-8806-9704"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luthfi, Muhammad Irfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5048199701","display_name":"Yuniar Indrihapsari","orcid":"https://orcid.org/0000-0002-0950-6513"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Indrihapsari, Yuniar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.7597000002861023,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.7597000002861023,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.02669999934732914,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10636","display_name":"Innovative Teaching and Learning Methods","score":0.022700000554323196,"subfield":{"id":"https://openalex.org/subfields/3204","display_name":"Developmental and Educational Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.6305999755859375},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5493999719619751},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.49070000648498535},{"id":"https://openalex.org/keywords/metacognition","display_name":"Metacognition","score":0.4618000090122223},{"id":"https://openalex.org/keywords/likert-scale","display_name":"Likert scale","score":0.43939998745918274},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4140999913215637},{"id":"https://openalex.org/keywords/cognitive-model","display_name":"Cognitive model","score":0.39070001244544983},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.3889000117778778},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.37459999322891235},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.3671000003814697}],"concepts":[{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.6305999755859375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6128000020980835},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5493999719619751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49549999833106995},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.49070000648498535},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.483599990606308},{"id":"https://openalex.org/C118147538","wikidata":"https://www.wikidata.org/wiki/Q1126970","display_name":"Metacognition","level":3,"score":0.4618000090122223},{"id":"https://openalex.org/C105776082","wikidata":"https://www.wikidata.org/wiki/Q617473","display_name":"Likert scale","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.39070001244544983},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C150681269","wikidata":"https://www.wikidata.org/wiki/Q2380771","display_name":"Cognitive style","level":3,"score":0.35019999742507935},{"id":"https://openalex.org/C61641136","wikidata":"https://www.wikidata.org/wiki/Q1107019","display_name":"Cognitive load","level":3,"score":0.3450999855995178},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3447999954223633},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.30410000681877136},{"id":"https://openalex.org/C20854674","wikidata":"https://www.wikidata.org/wiki/Q4386060","display_name":"Cognitive architecture","level":3,"score":0.3034000098705292},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C187179951","wikidata":"https://www.wikidata.org/wiki/Q7784616","display_name":"Thinking processes","level":3,"score":0.29989999532699585},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C2777096784","wikidata":"https://www.wikidata.org/wiki/Q3826351","display_name":"Referent","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26919999718666077},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8687179684638977,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,84],"paper":[1,85],"presents":[2],"the":[3,159,165,188],"Personalized":[4],"Thinking":[5],"Model":[6],"(PTM),":[7],"a":[8,26,66,97,127,138,206],"hierarchical":[9],"and":[10,38,51,58,81,119,135,143,204],"interpretable":[11],"learner":[12,23],"representation":[13],"designed":[14],"for":[15,141],"AI":[16],"supported":[17],"education.":[18],"PTM":[19,41,87,189],"organizes":[20],"evidence":[21],"from":[22,156,176],"journals":[24],"into":[25],"five-layer":[27],"structure":[28],"covering":[29],"behavioral":[30,32,160],"instances,":[31],"patterns,":[33],"cognitive":[34,60],"routines,":[35],"metacognitive":[36],"tendencies,":[37],"self-system":[39],"values.":[40],"is":[42],"grounded":[43],"in":[44,96],"Marzano's":[45],"New":[46],"Taxonomy":[47],"of":[48,113,133],"Educational":[49],"Objectives":[50],"tries":[52],"to":[53,93,162,178],"clone":[54],"learner's":[55],"thinking":[56],"model":[57,72],"build":[59],"twin.":[61],"It":[62],"was":[63,195],"constructed":[64],"using":[65,103,126],"pipeline":[67],"that":[68,152,187],"combines":[69],"large":[70],"language":[71],"inference":[73],"(Gemini":[74],"2.5":[75],"Pro),":[76],"sentence":[77],"embeddings,":[78],"dimensionality":[79],"reduction,":[80],"consensus":[82],"clustering.":[83],"evaluates":[86],"fidelity":[88],"through":[89],"three":[90],"methods":[91],"applied":[92],"40":[94],"participants":[95],"seven-week":[98],"study.":[99],"First,":[100],"automatic":[101],"evaluation":[102,125],"atomic":[104],"information":[105],"point":[106],"matching":[107],"yielded":[108],"an":[109],"overall":[110],"F1":[111],"score":[112],"74.57%":[114],"before":[115],"human-in-the-loop":[116],"(HITL)":[117],"refinement":[118],"75.48%":[120],"after":[121],"refinement.":[122],"Second,":[123],"user":[124],"Likert":[128],"scale":[129,140],"produced":[130],"mean":[131],"ratings":[132],"4.26":[134],"4.30":[136],"on":[137],"five-point":[139],"pre":[142],"post-HITL":[144],"conditions":[145],"respectively.":[146],"Third,":[147],"semantic":[148,210],"alignment":[149],"verification":[150],"showed":[151,205],"topic":[153],"coherence":[154],"increased":[155],"0.436":[157],"at":[158,164],"layer":[161],"0.626":[163],"core":[166],"value":[167],"layer,":[168],"while":[169],"lexical":[170],"overlap":[171],"with":[172,192,209],"journal":[173],"vocabulary":[174],"decreased":[175],"0.114":[177],"0.007":[179],"across":[180,212],"those":[181],"same":[182],"layers.":[183,213],"These":[184],"results":[185],"suggest":[186],"produces":[190],"outputs":[191],"acceptable":[193],"fidelity,":[194],"generally":[196],"perceived":[197],"by":[198],"users":[199],"as":[200],"reflecting":[201],"their":[202],"thinking,":[203],"pattern":[207],"consistent":[208],"abstraction":[211]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-08T00:00:00"}