{"id":"https://openalex.org/W7148254663","doi":"https://doi.org/10.48550/arxiv.2604.00259","title":"LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias","display_name":"LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7148254663","doi":"https://doi.org/10.48550/arxiv.2604.00259"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00259","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120301557","display_name":"Filip J. Kucia","orcid":"https://orcid.org/0009-0005-8473-1402"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kucia, Filip J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132824579","display_name":"Anirban Chakraborty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Anirban","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021096406","display_name":"Anna Wr\u00f3blewska","orcid":"https://orcid.org/0000-0003-3728-1216"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wr\u00f3blewska, Anna","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120301557"],"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/T10467","display_name":"Psychometric Methodologies and Testing","score":0.1451999992132187,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.1451999992132187,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.12139999866485596,"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/T10028","display_name":"Topic Modeling","score":0.08030000329017639,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/raw-score","display_name":"Raw score","score":0.5486000180244446},{"id":"https://openalex.org/keywords/meaning","display_name":"Meaning (existential)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4864000082015991},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.447299987077713},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.43880000710487366},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.38999998569488525},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.3684000074863434},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.35260000824928284},{"id":"https://openalex.org/keywords/grammar","display_name":"Grammar","score":0.33799999952316284}],"concepts":[{"id":"https://openalex.org/C173633133","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw score","level":3,"score":0.5486000180244446},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.5134999752044678},{"id":"https://openalex.org/C2780876879","wikidata":"https://www.wikidata.org/wiki/Q3054749","display_name":"Meaning (existential)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4641000032424927},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.42100000381469727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41749998927116394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4133000075817108},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.41200000047683716},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C95190672","wikidata":"https://www.wikidata.org/wiki/Q815382","display_name":"Meta-analysis","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.32100000977516174},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31610000133514404},{"id":"https://openalex.org/C79585631","wikidata":"https://www.wikidata.org/wiki/Q431498","display_name":"Confirmation bias","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2718999981880188},{"id":"https://openalex.org/C2778724333","wikidata":"https://www.wikidata.org/wiki/Q14401","display_name":"Kappa","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00259","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00259","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8587820529937744,"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":{"Despite":[0],"growing":[1],"interest":[2],"in":[3,137],"using":[4,218],"Large":[5],"Language":[6],"Models":[7],"(LLMs)":[8],"for":[9,166],"educational":[10],"assessment,":[11],"it":[12],"remains":[13],"unclear":[14],"how":[15],"closely":[16],"they":[17],"align":[18],"with":[19,50,74,181],"human":[20,51,123],"scoring.":[21,46,92,140],"We":[22,47,125],"present":[23],"a":[24,161,199],"systematic":[25,151,210],"evaluation":[26],"of":[27,59,145,204],"instruction-tuned":[28],"LLMs":[29],"across":[30],"three":[31],"open":[32],"essay-scoring":[33],"datasets":[34],"(ASAP":[35],"2.0,":[36],"ELLIPSE,":[37],"and":[38,44,56,98,111,216],"DREsS)":[39],"that":[40,65,114,128,175],"cover":[41],"both":[42],"holistic":[43,77],"analytic":[45,91,139],"analyze":[48],"agreement":[49,73],"consensus":[52],"scores,":[53,209],"directional":[54,101],"bias,":[55],"the":[57,143,155,167],"stability":[58],"bias":[60,102,169,177],"estimates.":[61],"Our":[62],"results":[63],"show":[64],"strong":[66],"open-weight":[67],"models":[68,115],"achieve":[69],"moderate":[70],"to":[71,90,148],"high":[72],"humans":[75],"on":[76,103,206],"scoring":[78],"(Quadratic":[79],"Weighted":[80],"Kappa":[81],"about":[82],"0.6),":[83],"but":[84],"this":[85],"does":[86],"not":[87],"transfer":[88],"uniformly":[89],"In":[93],"particular,":[94],"we":[95,153],"observe":[96],"large":[97],"stable":[99],"negative":[100],"Lower-Order":[104],"Concern":[105,188],"(LOC)":[106],"traits,":[107],"such":[108],"as":[109],"Grammar":[110],"Conventions,":[112],"meaning":[113],"often":[116,179],"score":[117,211],"these":[118,150],"traits":[119,190],"more":[120],"harshly":[121],"than":[122],"raters.":[124],"also":[126],"find":[127],"concise":[129],"keyword-based":[130],"prompts":[131,136],"generally":[132],"outperform":[133],"longer":[134],"rubric-style":[135],"multi-trait":[138],"To":[141],"quantify":[142],"amount":[144],"data":[146],"needed":[147],"detect":[149],"deviations,":[152],"compute":[154],"minimum":[156],"sample":[157],"size":[158],"at":[159],"which":[160],"95%":[162],"bootstrap":[163],"confidence":[164],"interval":[165],"mean":[168],"excludes":[170],"zero.":[171],"This":[172],"analysis":[173],"shows":[174],"LOC":[176],"is":[178],"detectable":[180],"very":[182],"small":[183,219],"validation":[184],"sets,":[185,222],"whereas":[186],"Higher-Order":[187],"(HOC)":[189],"typically":[191],"require":[192],"much":[193],"larger":[194],"samples.":[195],"These":[196],"findings":[197],"support":[198],"bias-correction-first":[200],"deployment":[201],"strategy:":[202],"instead":[203],"relying":[205],"raw":[207],"zero-shot":[208],"offsets":[212],"can":[213],"be":[214],"estimated":[215],"corrected":[217],"human-labeled":[220],"bias-estimation":[221],"without":[223],"requiring":[224],"large-scale":[225],"fine-tuning.":[226]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}