{"id":"https://openalex.org/W7201869030","doi":"https://doi.org/10.1109/aic65131.2026.11633682","title":"A Multi-Level Assessment Model for Generative AI's Occupational Impact","display_name":"A Multi-Level Assessment Model for Generative AI's Occupational Impact","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7201869030","doi":"https://doi.org/10.1109/aic65131.2026.11633682"},"language":"en","primary_location":{"id":"doi:10.1109/aic65131.2026.11633682","doi":"https://doi.org/10.1109/aic65131.2026.11633682","is_oa":true,"landing_page_url":"https://doi.org/10.1109/aic65131.2026.11633682","pdf_url":null,"source":{"id":"https://openalex.org/S7407105921","display_name":"International Conference on Artificial Intelligence and Computing (AIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 1st International Conference on Artificial Intelligence and Computing (AIC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/aic65131.2026.11633682","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5145951227","display_name":"Lingyu Ji","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyu Ji","raw_affiliation_strings":["Nankai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103019707","display_name":"Yutong Zhou","orcid":"https://orcid.org/0000-0002-4175-3100","observed_orcids":["https://orcid.org/0000-0002-4175-3100"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yutong Zhou","raw_affiliation_strings":["Nankai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5145951731","display_name":"Zhuohan Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuohan Wang","raw_affiliation_strings":["Nankai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132551322","display_name":"Jian-Yu Li","orcid":"https://orcid.org/0000-0002-6143-9207","observed_orcids":["https://orcid.org/0000-0002-6143-9207"]},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian-Yu Li","raw_affiliation_strings":["Nankai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nankai University","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56747275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"296","last_page":"303"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.3522999882698059,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.3522999882698059,"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/T10208","display_name":"Labor market dynamics and wage inequality","score":0.2046000063419342,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13812","display_name":"AI and HR Technologies","score":0.0812000036239624,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-ai","display_name":"generative AI","score":0.984000027179718},{"id":"https://openalex.org/keywords/occupational-impact","display_name":"occupational impact","score":0.9760000109672546},{"id":"https://openalex.org/keywords/job-displacement","display_name":"job displacement","score":0.47600001096725464},{"id":"https://openalex.org/keywords/workplace-automation","display_name":"workplace automation","score":0.3880000114440918},{"id":"https://openalex.org/keywords/human-ai-interaction","display_name":"human-AI interaction","score":0.4819999933242798}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4250999987125397},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3544999957084656},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.34220001101493835},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.24619999527931213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aic65131.2026.11633682","doi":"https://doi.org/10.1109/aic65131.2026.11633682","is_oa":true,"landing_page_url":"https://doi.org/10.1109/aic65131.2026.11633682","pdf_url":null,"source":{"id":"https://openalex.org/S7407105921","display_name":"International Conference on Artificial Intelligence and Computing (AIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 1st International Conference on Artificial Intelligence and Computing (AIC)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/aic65131.2026.11633682","doi":"https://doi.org/10.1109/aic65131.2026.11633682","is_oa":true,"landing_page_url":"https://doi.org/10.1109/aic65131.2026.11633682","pdf_url":null,"source":{"id":"https://openalex.org/S7407105921","display_name":"International Conference on Artificial Intelligence and Computing (AIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 1st International Conference on Artificial Intelligence and Computing (AIC)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.833299994468689}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1821977771","https://openalex.org/W2526781987","https://openalex.org/W2891186800","https://openalex.org/W2892380133","https://openalex.org/W2897697024","https://openalex.org/W3007698149","https://openalex.org/W3012446350","https://openalex.org/W3124124240","https://openalex.org/W4247680473","https://openalex.org/W4317581943","https://openalex.org/W4386170955","https://openalex.org/W4399848789","https://openalex.org/W4403511218","https://openalex.org/W4404469638","https://openalex.org/W7149025121"],"related_works":["https://openalex.org/W7130571780","https://openalex.org/W7105689388","https://openalex.org/W6959975333","https://openalex.org/W7160870045","https://openalex.org/W7221554646","https://openalex.org/W7167789132","https://openalex.org/W7161159261","https://openalex.org/W4386539791","https://openalex.org/W7106677234","https://openalex.org/W4410593539"],"abstract_inverted_index":{"Generative":[0],"artificial":[1],"intelligence":[2],"is":[3,86],"rapidly":[4],"reshaping":[5],"the":[6,49,62,125,136,139,142,182,190,233],"global":[7],"labor":[8],"market":[9],"and":[10,34,52,95,123,138,153,208,228],"exerting":[11],"highly":[12],"heterogeneous":[13],"impacts":[14,21],"on":[15,32,212],"different":[16,168],"occupations.":[17],"Quantifying":[18],"these":[19,68],"differentiated":[20],"has":[22],"therefore":[23],"become":[24],"an":[25],"urgent":[26],"task.":[27],"Existing":[28],"methods":[29,223],"mostly":[30],"rely":[31],"static":[33,89],"single-dimensional":[35],"evaluation.":[36],"They":[37],"fail":[38],"to":[39,87,129,158,180,203],"capture":[40,159],"how":[41,160],"augmentation":[42,161],"effects":[43,47,162,166],"shift":[44],"into":[45,99,164],"substitution":[46,131,165],"as":[48],"technology":[50],"evolves,":[51],"they":[53],"rarely":[54],"explain":[55],"what":[56],"drives":[57],"occupational":[58,149,186],"transformation,":[59,187],"which":[60,133],"leaves":[61],"analytical":[63,102],"view":[64],"incomplete.":[65],"To":[66],"address":[67],"limitations,":[69],"this":[70],"paper":[71],"proposes":[72],"a":[73,113,147,155,175],"progressive":[74],"Multi-Level":[75],"Gen-AI":[76],"Occupational":[77],"Impact":[78,116],"Assessment":[79],"Model,":[80],"named":[81],"ML-OIAM.":[82],"The":[83,196],"core":[84],"idea":[85],"link":[88],"impact":[90],"quantification,":[91],"dynamic":[92],"trend":[93,150],"projection,":[94],"causal":[96],"driver":[97],"attribution":[98,178,191],"one":[100],"complete":[101],"chain.":[103],"ML-OIAM":[104,202,218],"consists":[105],"of":[106,141,235],"three":[107,197,213,220],"key":[108,183],"components.":[109],"First,":[110],"we":[111,145,173],"build":[112],"Weighted":[114],"Multi-dimensional":[115],"Index":[117],"Model":[118],"that":[119,217],"uses":[120],"task-level":[121],"data":[122],"applies":[124],"Analytic":[126],"Hierarchy":[127],"Process":[128],"quantify":[130],"risk,":[132],"improves":[134],"both":[135,205],"accuracy":[137],"interpretability":[140],"assessment.":[143],"Second,":[144],"design":[146],"scenario-based":[148],"projection":[151],"model":[152,179],"introduce":[154],"time-varying":[156],"coefficient":[157],"transition":[163],"under":[167,193],"AI":[169],"development":[170],"scenarios.":[171],"Third,":[172],"apply":[174],"grey":[176],"relational":[177],"identify":[181],"factors":[184],"driving":[185],"thereby":[188],"filling":[189],"gap":[192],"small-sample":[194],"conditions.":[195],"components":[198],"work":[199],"synergistically,":[200],"enabling":[201],"achieve":[204],"comprehensive":[206],"evaluation":[207,226],"robust":[209],"prediction.":[210],"Experiments":[211],"representative":[214],"occupations":[215],"demonstrate":[216],"outperforms":[219],"mainstream":[221],"baseline":[222],"across":[224],"all":[225],"dimensions,":[227],"ablation":[229],"experiments":[230],"further":[231],"verify":[232],"indispensability":[234],"each":[236],"component.":[237]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-08-08T00:00:00"}