DEVELOPMENT AND VALIDATION OF EMOTIONBRIDGE: A GPT-BASED DIAGNOSTIC SYSTEM FOR EMPATHIC COMMUNICATION SKILLS.
This study developed and validated EmotionBridge, a GPT-based diagnostic tool for assessing empathic communication through real-time, scenario-based interactions. Using a Design and Development Research (DDR) approach, the system was grounded in a theoretical framework with two domains: empathic exp...
| Publicado en: | Scientific Culture Vol. 12; no. 5 Part 1; pp. 526 - 544 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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University of the Aegean
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=193975213&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 193975213 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 5 Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 193975213 10.5281/zenodo.12511043 ppf: 526 ppct: 18 formats: tig: atl: DEVELOPMENT AND VALIDATION OF EMOTIONBRIDGE: A GPT-BASED DIAGNOSTIC SYSTEM FOR EMPATHIC COMMUNICATION SKILLS. aug: au: Lee, Yuna Xiao, AiJin Lee, HyeRan Lee, Sang-Soo affil: Institute of Educational Policy Research, Future Education Institute, Uiryeong, South Korea Department of Education, Pusan National University, Busan, South Korea su: Generative pre-trained transformers Empathy Social emotional learning Test validity Scoring rubrics User-centered system design Design research Emotional competence sug: subj: Generative pre-trained transformers Empathy Social emotional learning Test validity Scoring rubrics User-centered system design Design research Emotional competence keyword: AI-assisted assessment Empathic Communication GPT-based system social-emotional learning usability evaluation ab: This study developed and validated EmotionBridge, a GPT-based diagnostic tool for assessing empathic communication through real-time, scenario-based interactions. Using a Design and Development Research (DDR) approach, the system was grounded in a theoretical framework with two domains: empathic expression and empathic response. The prototype, built via GPT Builder, incorporated rubric-based evaluations. Expert reviews demonstrated high content validity (CVI = .95 and .97), and concurrent validity was supported by correlations between expert and self-report scores. Usability testing with 35 participants yielded a strong SUS score (79.64), and qualitative feedback informed iterative refinements. The results demonstrate the promise of generative AI in delivering scalable and personalized diagnostics in social-emotional learning, offering a replicable design model that links large language models with structured assessment principles. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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