{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T12:24:18Z","timestamp":1788351858652,"version":"build-2803163510"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Behav. Sci.","Behavioral Sciences"],"abstract":"<jats:p>This study examined whether AI-powered chatbot-based training was associated with changes in university students\u2019 Facial and Vocal Emotional Reaction Time Proportions during simulated job interviews. A one-group pretest\u2013posttest design was conducted with 54 third- and fourth-year students enrolled in a Human Talent Management course at a private Latin American university. This study was implemented in an Applied Neuroscience Laboratory using iMotions-supported facial-expression recognition and vocal analysis technologies. Participants first completed a baseline simulated interview, followed by three chatbot-based training sessions using HR-expert-validated questions, end-of-session scoring, and qualitative feedback. A final simulated interview was then conducted to compare pre- and post-training indicators. Facial emotional reaction time was analyzed through aggregate indicators\u2014positive, negative, neutral, confusion, and sentimentality\u2014and specific facial-expression categories, including joy, surprise, anger, sadness, disgust, fear, and contempt. Vocal emotional reaction time was examined through happiness, sadness, anger, and neutrality. Pre\u2013post differences were assessed using paired-samples t-tests and complementary Wilcoxon signed-rank tests. Positive facial emotional reaction time increased significantly from 3.52% to 14.75%, with a mean increase of 11.23 percentage points, 95% CI [4.79, 17.67]. Facial joy increased significantly from 2.38% to 10.10%, with a mean increase of 7.72 percentage points, 95% CI [3.30, 12.14], while vocal happiness increased significantly from 2.79% to 10.71%, with a mean increase of 7.92 percentage points, 95% CI [3.38, 12.46]. Each of these principal outcomes showed a standardized paired effect of dz = 0.48, 95% CI [0.19, 0.76], with corresponding Wilcoxon effect-size estimates ranging from r = 0.40 to r = 0.44. Several negative and neutral indicators also decreased after training; however, their mean-based standardized effects were generally smaller and some statistically significant findings were supported primarily by the Wilcoxon signed-rank test. Overall, chatbot-based interview training was associated with changes in algorithmically classified facial and vocal-expression patterns and may provide a complementary tool for structured interview practice in higher education.<\/jats:p>","DOI":"10.3390\/bs16091554","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T12:04:16Z","timestamp":1788350656000},"page":"1554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Changes in Emotional and Vocal Expression in Job Interview Simulations with an AI-Enhanced Chatbot for University Students"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2515-6944","authenticated-orcid":false,"given":"Alberto","family":"Grajeda","sequence":"first","affiliation":[{"name":"Research Center for Innovation in Information Technology for Education, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8644-3157","authenticated-orcid":false,"given":"Pamela","family":"Cordova","sequence":"additional","affiliation":[{"name":"Center for Research in Economics and Entrepreneurial Sciences, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0366-5525","authenticated-orcid":false,"given":"Juan Pablo","family":"Cordova","sequence":"additional","affiliation":[{"name":"Applied Neuroscience Laboratory, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4550-2025","authenticated-orcid":false,"given":"Mar\u00eda Isabel","family":"Pueyo","sequence":"additional","affiliation":[{"name":"Olave School of Business and Faculty of Business Sciences and Law, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9203-0897","authenticated-orcid":false,"given":"Patricia","family":"Gasser","sequence":"additional","affiliation":[{"name":"Olave School of Business and Faculty of Business Sciences and Law, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0948-3649","authenticated-orcid":false,"given":"Isabel","family":"La Fuente","sequence":"additional","affiliation":[{"name":"Olave School of Business and Faculty of Business Sciences and Law, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9819-8418","authenticated-orcid":false,"given":"Hern\u00e1n","family":"Naranjo","sequence":"additional","affiliation":[{"name":"Olave School of Business and Faculty of Business Sciences and Law, Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e02089","DOI":"10.26668\/businessreview\/2023.v8i6.2089","article-title":"The power of artificial intelligence in recruitment: An analytical review of current AI-based recruitment strategies","volume":"8","author":"Albassam","year":"2023","journal-title":"International Journal of Professional Business Review"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/17470919.2022.2064910","article-title":"\u201cWe will be in touch\u201d: A neuroscientific assessment of remote vs. face-to-face job interviews via EEG hyperscanning","volume":"17","author":"Balconi","year":"2022","journal-title":"Social Neuroscience"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/978-3-642-40415-3_10","article-title":"Cicero\u2014Towards a multimodal virtual audience platform for public speaking training","volume":"Vol. 8108","author":"Aylett","year":"2013","journal-title":"Intelligent virtual agents: 13th international conference, IVA 2013, Edinburgh, UK, August 29\u201331, 2013, proceedings"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e70048","DOI":"10.1002\/acp.70048","article-title":"Experts\u2019 views on artificial intelligence-based child chatbots to train investigative interviewing skills","volume":"39","author":"Brubacher","year":"2025","journal-title":"Applied Cognitive Psychology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11024-023-09518-8","article-title":"The feeling rules of peer review: Defining, displaying, and managing emotions in evaluation for research funding","volume":"62","author":"Brunet","year":"2024","journal-title":"Minerva"},{"key":"ref_6","unstructured":"Calzolari, N., Choukri, K., Declerck, T., Loftsson, H., Maegaard, B., Mariani, J., Moreno, A., Odijk, J., and Piperidis, S. 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