{"id":"https://openalex.org/W7140077921","doi":"https://doi.org/10.48550/arxiv.2603.19280","title":"From Feature-Based Models to Generative AI: Validity Evidence for Constructed Response Scoring","display_name":"From Feature-Based Models to Generative AI: Validity Evidence for Constructed Response Scoring","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7140077921","doi":"https://doi.org/10.48550/arxiv.2603.19280"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19280","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":null,"license_id":null,"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.2603.19280","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019102552","display_name":"Jodi M. Casabianca","orcid":"https://orcid.org/0000-0002-1644-6731"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Casabianca, Jodi M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015876786","display_name":"Daniel F. McCaffrey","orcid":"https://orcid.org/0000-0003-1196-5273"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McCaffrey, Daniel F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101509874","display_name":"Matthew Johnson","orcid":"https://orcid.org/0000-0003-3157-4165"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Johnson, Matthew S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026932575","display_name":"Naim Alper","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alper, Naim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130365735","display_name":"Vladimir Zubenko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zubenko, Vladimir","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":0.2676999866962433,"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":0.2676999866962433,"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":0.07829999923706055,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.054999999701976776,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7861999869346619},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5444999933242798},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48989999294281006},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.37549999356269836},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.3747999966144562},{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.36410000920295715}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7861999869346619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6545000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6226999759674072},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5444999933242798},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4733000099658966},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4410000145435333},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.37549999356269836},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C2984538763","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring system","level":2,"score":0.2786000072956085}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19280","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19280","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8258395195007324}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,57,130],"rapid":[1],"advancements":[2],"in":[3,18,29,47,67,74,114,133,142],"large":[4,169],"language":[5,122],"models":[6],"and":[7,51,70,79,96,127,153,192,197],"generative":[8,27,71,106,128,135,158],"artificial":[9],"intelligence":[10],"(AI)":[11],"capabilities":[12],"are":[13],"making":[14,200],"their":[15],"broad":[16],"application":[17],"the":[19,30,41,65,68,87,94,110,134,143,149,181,194],"high-stakes":[20],"testing":[21],"context":[22,137,146],"more":[23,139],"likely.":[24],"Use":[25],"of":[26,32,59,83,89,98,148,151,171,183,189],"AI":[28,49,72,124,136,159],"scoring":[31,50,77,103,115,125,145,190],"constructed":[33,75,99],"responses":[34],"is":[35,62,138],"particularly":[36],"appealing":[37],"because":[38,147],"it":[39],"reduces":[40],"effort":[42],"required":[43],"for":[44,86,186,204],"handcrafting":[45],"features":[46],"traditional":[48],"might":[52],"even":[53],"outperform":[54],"those":[55],"methods.":[56],"purpose":[58],"this":[60],"paper":[61],"to":[63,92,157],"highlight":[64,193],"differences":[66],"feature-based":[69,120,144],"applications":[73],"response":[76,100,164],"systems":[78,104,116,191],"propose":[80],"a":[81,168,201],"set":[82],"best":[84],"practices":[85],"collection":[88,182],"validity":[90,111,184,202],"evidence":[91,112,131,185],"support":[93],"use":[95],"interpretation":[97],"scores":[101],"from":[102,167],"using":[105,117],"AI.":[107,129],"We":[108],"compare":[109],"needed":[113,132],"human":[118],"ratings,":[119],"natural":[121],"processing":[123],"engines,":[126],"extensive":[140],"than":[141],"lack":[150],"transparency":[152],"other":[154],"concerns":[155],"unique":[156],"such":[160],"as":[161],"consistency.":[162],"Constructed":[163],"score":[165],"data":[166],"corpus":[170],"independent":[172],"argumentative":[173],"essays":[174],"written":[175],"by":[176],"6-12th":[177],"grade":[178],"students":[179],"demonstrate":[180],"different":[187],"types":[188],"numerous":[195],"complexities":[196],"considerations":[198],"when":[199],"argument":[203],"these":[205],"scores.":[206]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-24T00:00:00"}