{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T18:08:26Z","timestamp":1775153306073,"version":"3.50.1"},"reference-count":129,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,23]],"date-time":"2019-01-23T00:00:00Z","timestamp":1548201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner\u2019s emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems.<\/jats:p>","DOI":"10.3390\/s19030460","type":"journal-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T03:52:32Z","timestamp":1548301952000},"page":"460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9564-6653","authenticated-orcid":false,"given":"Fehaid","family":"Alqahtani","sequence":"first","affiliation":[{"name":"School of Computing, Engineering, and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK"},{"name":"Computer Science Department, King Fahad Naval Academy, Jubail 35512, Kingdom of Saudi Arabia"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering, and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1002\/jclp.20243","article-title":"Mechanisms of change in dialectical behavior therapy: Theoretical and empirical observations","volume":"62","author":"Lynch","year":"2006","journal-title":"J. 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