{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T09:10:48Z","timestamp":1788772248800,"version":"build-2803163510"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Sciences Research Project of Ministry of Education, China","award":["23YJA880022"],"award-info":[{"award-number":["23YJA880022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Intell."],"abstract":"<jats:p>The accurate measurement of individual cognitive abilities in educational settings increasingly relies on personalized assessment systems that adapt to the learner\u2019s current standing on the measured ability. These systems generate and use assessment items continuously and in real-time, bypassing the field-test cycle that traditional calibration requires. Item banks therefore contain a growing share of items whose parameters must be predicted first, and each test form must be assembled in real time to match the learner\u2019s current ability level and content needs for effective personalized instruction. This study proposes an Ising framework that addresses both requirements by predicting item parameters from text with calibrated uncertainty estimates and assembling personalized test forms within the time budget of the assessment loop. This framework is validated through a factorial simulation and an empirical study on state-level English Language Arts items across Grades 3 through 12. In the simulation, the proposed uncertainty-aware method achieves a mean test information function (TIF) gap of 0.226, compared with 1.171 for the conventional baseline that ignores prediction uncertainty. The proposed method also produces feasible forms across the full operational region, whereas the chance-constrained and robust baselines become infeasible at high prediction error combined with a high share of predicted items. In the empirical study, predictive intervals achieve coverage within ten percentage points of the nominal level in the pooled condition, and the uncertainty-aware assembly reduces the TIF gap relative to the baseline, with the largest gains at the extremes of the ability distribution where accurate measurement matters most for instructional placement decisions. By separating prediction uncertainty from true individual differences in ability, this framework preserves the interpretability of cognitive ability estimates and provides a foundation for personalized assessment systems that support differentiated instruction under continuous item bank growth.<\/jats:p>","DOI":"10.3390\/jintelligence14090216","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T08:07:06Z","timestamp":1788768426000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Dynamic Adaptive Test Assembly for Personalized Measurement of Cognitive Abilities Under Continuous Item Bank Growth"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2069-8720","authenticated-orcid":false,"given":"Jiawei","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Educational Psychology, Mary Frances Early College of Education, University of Georgia, Athens, GA 30605, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6556-7144","authenticated-orcid":false,"given":"Cheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Educational Psychology, Mary Frances Early College of Education, University of Georgia, Athens, GA 30605, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6797-4163","authenticated-orcid":false,"given":"Qidi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Educational Psychology, Mary Frances Early College of Education, University of Georgia, Athens, GA 30605, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6986-2213","authenticated-orcid":false,"given":"Feiming","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, College of Education, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1007\/s40593-023-00362-1","article-title":"Text-based question difficulty prediction: A systematic review of automatic approaches","volume":"34","author":"AlKhuzaey","year":"2024","journal-title":"International Journal of Artificial Intelligence in Education"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aramon, M., Rosenberg, G., Valiante, E., Miyazawa, T., Tamura, H., and Katzgraber, H. 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