Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called . This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources.
Results: A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research.
Conclusions: The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.
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http://dx.doi.org/10.1186/s40594-018-0110-y | DOI Listing |
Int Dent J
January 2025
Department of Basic Sciences, Faculty of Dental Sciences, University of Peradeniya, Peradeniya, 20400 Sri Lanka. Electronic address:
Objective: This study evaluated the effectiveness of an AI-based tool (ChatGPT-4) (AIT) vs a human tutor (HT) in providing feedback on dental students' assignments.
Methods: A total of 194 answers to two histology questions were assessed by both tutors using the same rubric. Students compared feedback from both tutors and evaluated its accuracy against a standard rubric.
Med Teach
January 2025
Department of Medical Education, Dartmouth College Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
Health Professions Education (HPE) assessment is being increasingly impacted by Artificial Intelligence (AI), and institutions, educators, and learners are grappling with AI's ever-evolving complexities, dangers, and potential. This AMEE Guide aims to assist all HPE stakeholders by helping them navigate the assessment uncertainty before them. Although the impetus is AI, the Guide grounds its path in pedagogical theory, considers the range of human responses, and then deals with assessment types, challenges, AI roles as tutor and learner, and required competencies.
View Article and Find Full Text PDFNPJ Sci Learn
January 2025
Department of Educational Sciences, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476, Potsdam, Germany.
Rising interest in artificial intelligence in education reinforces the demand for evidence-based implementation. This study investigates how tutor agents' physical embodiment and anthropomorphism (student-reported sociability, animacy, agency, and disturbance) relate to affective (on-task enjoyment) and cognitive (task performance) learning within an intelligent tutoring system (ITS). Data from 56 students (M = 17.
View Article and Find Full Text PDFFront Artif Intell
December 2024
IN3-Department of Computer Science, Multimedia and Telecommunications, Open University of Catalonia, Barcelona, Spain.
BMC Bioinformatics
December 2024
Synthetic Biology and Biotechnology Unit, Department of Biology, University of Padua, Padua, Italy.
Background: Vaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills.
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