Hybrid IRT-Neural Adaptive Engine for Big Five Personality Profiling.
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| Title: | Hybrid IRT-Neural Adaptive Engine for Big Five Personality Profiling. |
|---|---|
| Authors: | Vennelakati, S. Annamayya1, Kharche, S. P.2 shubhangi.kharche@gmail.com, Jillellamudi, Sravani3, Tiwari, Megha4 |
| Source: | Journal of Engineering Science & Technology Review. 2026, Vol. 19 Issue 1, p192-199. 8p. |
| Subjects: | Item response theory, Adaptive testing, Five-factor model of personality, Psychometrics, Personality assessment, Machine learning, Artificial neural networks |
| Abstract: | Personality assessment is central to clinical and organizational decision-making, yet standard Big Five questionnaires often require 50+ items, causing fatigue and limiting use in time-sensitive contexts. This work develops a shorter, precise, and interpretable adaptive alternative. We present a hybrid engine that combines item response theory (IRT) calibration with a neural ranker. A five-dimensional graded response model (GRM) was calibrated on over one million responses, and a lightweight multilayer perceptron (MLP, ∼45k parameters) was trained using two strategies: (i) direct EPVR training and (ii) EFI pretraining with EPVR fine-tuning. Runtime adaptivity was guided by a hybrid stopping rule targeting mean SE (average standard error across traits) ≤ 0.39 and worst-trait SE (maximum standard error across traits) ≤ 0.48. On 2,000 respondents, both strategies achieved Fisher-equivalent precision with a median of 37 items (IQR 34-40); Method II attained near-perfect teacher fidelity (accuracy >0.99, AUC ≈1.0), while Method I offered a simpler pipeline with comparable runtime outcomes. SHAP analyses confirmed reliance on psychometric features, providing transparent explanations. These results show that Fisher-level accuracy is achievable in ∼37 questions with real-time efficiency, making adaptive Big Five profiling practical for deployment. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering Science & Technology Review is the property of Technological Education Institute of Kavala and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192680617 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid IRT-Neural Adaptive Engine for Big Five Personality Profiling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vennelakati%2C+S%2E+Annamayya%22">Vennelakati, S. Annamayya</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kharche%2C+S%2E+P%2E%22">Kharche, S. P.</searchLink><relatesTo>2</relatesTo><i> shubhangi.kharche@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Jillellamudi%2C+Sravani%22">Jillellamudi, Sravani</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Tiwari%2C+Megha%22">Tiwari, Megha</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+Science+%26+Technology+Review%22">Journal of Engineering Science & Technology Review</searchLink>. 2026, Vol. 19 Issue 1, p192-199. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+testing%22">Adaptive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Five-factor+model+of+personality%22">Five-factor model of personality</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Personality+assessment%22">Personality assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Personality assessment is central to clinical and organizational decision-making, yet standard Big Five questionnaires often require 50+ items, causing fatigue and limiting use in time-sensitive contexts. This work develops a shorter, precise, and interpretable adaptive alternative. We present a hybrid engine that combines item response theory (IRT) calibration with a neural ranker. A five-dimensional graded response model (GRM) was calibrated on over one million responses, and a lightweight multilayer perceptron (MLP, ∼45k parameters) was trained using two strategies: (i) direct EPVR training and (ii) EFI pretraining with EPVR fine-tuning. Runtime adaptivity was guided by a hybrid stopping rule targeting mean SE (average standard error across traits) ≤ 0.39 and worst-trait SE (maximum standard error across traits) ≤ 0.48. On 2,000 respondents, both strategies achieved Fisher-equivalent precision with a median of 37 items (IQR 34-40); Method II attained near-perfect teacher fidelity (accuracy >0.99, AUC ≈1.0), while Method I offered a simpler pipeline with comparable runtime outcomes. SHAP analyses confirmed reliance on psychometric features, providing transparent explanations. These results show that Fisher-level accuracy is achievable in ∼37 questions with real-time efficiency, making adaptive Big Five profiling practical for deployment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering Science & Technology Review is the property of Technological Education Institute of Kavala and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.25103/jestr.191.19 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 192 Subjects: – SubjectFull: Item response theory Type: general – SubjectFull: Adaptive testing Type: general – SubjectFull: Five-factor model of personality Type: general – SubjectFull: Psychometrics Type: general – SubjectFull: Personality assessment Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Hybrid IRT-Neural Adaptive Engine for Big Five Personality Profiling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vennelakati, S. Annamayya – PersonEntity: Name: NameFull: Kharche, S. P. – PersonEntity: Name: NameFull: Jillellamudi, Sravani – PersonEntity: Name: NameFull: Tiwari, Megha IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 17912377 Numbering: – Type: volume Value: 19 – Type: issue Value: 1 Titles: – TitleFull: Journal of Engineering Science & Technology Review Type: main |
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