{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113136"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113136","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Artificial intelligence powered personality assessment: A multidimensional psychometric natural language processing perspective","abstract":"Recent technological advances have allowed researchers to apply automated, language-based machine learning models as alternatives to self-reports for assessing personality. However, previous work has largely overlooked the multidimensional nature of personality and lacked in-depth exploration of validity issues. In this dissertation, I examined novel methods for leveraging artificial intelligence (AI), natural language processing (NLP), machine learning, and automation to systematically glean personality-related information from textual data which offers rich information and reflects various aspects of personality but has been severely underutilized. In two studies, I connected the five-factor (or Big Five) model (comprised of openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism) with NLP from two angles: 1) a construct validity perspective (i.e., the degree to which information extracted from textual data reflects personality constructs), and 2) an applicability perspective (i.e., the ability to elicit personality-relevant information from text in line with psychological and organizational principles). In Study 1, I meta-analytically reviewed the multidimensional psychometric evidence of AI-supported language-based personality assessment. Results showed that measurement reliability is often not addressed in past AI personality assessment research, and that construct validity evidence is lacking. In Study 2, I built an interactive tool to automatically and adaptively prompt for, collect, and analyze personality-relevant topic-based (i.e., honoring the Big Five factorial structure) narrative data through conversations conducted by an AI chatbot. Results showed some improvements in various validities of the new personality assessment tool. Potential reasons for the improvement magnitudes, limitations of the current methods, and future directions are discussed.","abstract_html":"Recent technological advances have allowed researchers to apply automated, language-based machine learning models as alternatives to self-reports for assessing personality. However, previous work has largely overlooked the multidimensional nature of personality and lacked in-depth exploration of validity issues. In this dissertation, I examined novel methods for leveraging artificial intelligence (AI), natural language processing (NLP), machine learning, and automation to systematically glean personality-related information from textual data which offers rich information and reflects various aspects of personality but has been severely underutilized. In two studies, I connected the five-factor (or Big Five) model (comprised of openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism) with NLP from two angles: 1) a construct validity perspective (i.e., the degree to which information extracted from textual data reflects personality constructs), and 2) an applicability perspective (i.e., the ability to elicit personality-relevant information from text in line with psychological and organizational principles). In Study 1, I meta-analytically reviewed the multidimensional psychometric evidence of AI-supported language-based personality assessment. Results showed that measurement reliability is often not addressed in past AI personality assessment research, and that construct validity evidence is lacking. In Study 2, I built an interactive tool to automatically and adaptively prompt for, collect, and analyze personality-relevant topic-based (i.e., honoring the Big Five factorial structure) narrative data through conversations conducted by an AI chatbot. Results showed some improvements in various validities of the new personality assessment tool. Potential reasons for the improvement magnitudes, limitations of the current methods, and future directions are discussed.","abstract_has_math":false,"creators":["Sun, Tianjun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Drasgow, Fritz","Roberts, Brent W","Rounds, James","Guenole, Nigel","Jiang, Ge"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:34:50Z","date_published":"2022-01-12T22:34:50Z","updated_at":"2026-07-22T22:24:53Z","subjects":["personality","psychometrics","machine learning","natural language processing","artificial intelligence"],"languages":["en"],"rights":["Copyright 2021 Tianjun Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113136","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Drasgow, Fritz","Roberts, Brent W","Rounds, James","Guenole, Nigel","Jiang, Ge"]},{"key":"dc:creator","label":"Author","values":["Sun, Tianjun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:34:50Z","2024-01-12T22:35:30Z","2021-07-06","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["personality","psychometrics","machine learning","natural language processing","artificial intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Tianjun Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113136"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Recent technological advances have allowed researchers to apply automated, language-based machine learning models as alternatives to self-reports for assessing personality. However, previous work has largely overlooked the multidimensional nature of personality and lacked in-depth exploration of validity issues. In this dissertation, I examined novel methods for leveraging artificial intelligence (AI), natural language processing (NLP), machine learning, and automation to systematically glean personality-related information from textual data which offers rich information and reflects various aspects of personality but has been severely underutilized. In two studies, I connected the five-factor (or Big Five) model (comprised of openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism) with NLP from two angles: 1) a construct validity perspective (i.e., the degree to which information extracted from textual data reflects personality constructs), and 2) an applicability perspective (i.e., the ability to elicit personality-relevant information from text in line with psychological and organizational principles). In Study 1, I meta-analytically reviewed the multidimensional psychometric evidence of AI-supported language-based personality assessment. Results showed that measurement reliability is often not addressed in past AI personality assessment research, and that construct validity evidence is lacking. In Study 2, I built an interactive tool to automatically and adaptively prompt for, collect, and analyze personality-relevant topic-based (i.e., honoring the Big Five factorial structure) narrative data through conversations conducted by an AI chatbot. Results showed some improvements in various validities of the new personality assessment tool. Potential reasons for the improvement magnitudes, limitations of the current methods, and future directions are discussed.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Tianjun Sun, accepted the attached license on 2021-07-01 at 14:23.","The student, Tianjun Sun, submitted this Dissertation for approval on 2021-07-01 at 14:27.","This Dissertation was approved for publication on 2021-07-06 at 07:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16744 on 2022-01-12 at 12:52:49","Made available in DSpace on 2022-01-12T22:34:50Z (GMT). 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However, previous work has largely overlooked the multidimensional nature of personality and lacked in-depth exploration of validity issues. In this dissertation, I examined novel methods for leveraging artificial intelligence (AI), natural language processing (NLP), machine learning, and automation to systematically glean personality-related information from textual data which offers rich information and reflects various aspects of personality but has been severely underutilized. In two studies, I connected the five-factor (or Big Five) model (comprised of openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism) with NLP from two angles: 1) a construct validity perspective (i.e., the degree to which information extracted from textual data reflects personality constructs), and 2) an applicability perspective (i.e., the ability to elicit personality-relevant information from text in line with psychological and organizational principles). In Study 1, I meta-analytically reviewed the multidimensional psychometric evidence of AI-supported language-based personality assessment. Results showed that measurement reliability is often not addressed in past AI personality assessment research, and that construct validity evidence is lacking. In Study 2, I built an interactive tool to automatically and adaptively prompt for, collect, and analyze personality-relevant topic-based (i.e., honoring the Big Five factorial structure) narrative data through conversations conducted by an AI chatbot. Results showed some improvements in various validities of the new personality assessment tool. Potential reasons for the improvement magnitudes, limitations of the current methods, and future directions are discussed.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Tianjun Sun, accepted the attached license on 2021-07-01 at 14:23.","The student, Tianjun Sun, submitted this Dissertation for approval on 2021-07-01 at 14:27.","This Dissertation was approved for publication on 2021-07-06 at 07:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16744 on 2022-01-12 at 12:52:49","Made available in DSpace on 2022-01-12T22:34:50Z (GMT). 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