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Peer reviewedPriti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
Direct linkAustin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
Peer reviewedArun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Vasile Rus – Grantee Submission, 2024
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide…
Descriptors: Coding, Computer Science Education, Computational Linguistics, Artificial Intelligence
Download full textCai, Zhiqiang; Hu, Xiangen; Graesser, Arthur C. – Grantee Submission, 2019
Conversational Intelligent Tutoring Systems (ITSs) are expensive to develop. While simple online courseware could be easily authored by teachers, the authoring of conversational ITSs usually involves a team of experts with different expertise, including domain experts, linguists, instruction designers, programmers, artists, computer scientists,…
Descriptors: Programming, Intelligent Tutoring Systems, Courseware, Educational Technology
Aleven, Vincent; McLaren, Bruce M.; Sewall, Jonathan; van Velsen, Martin; Popescu, Octav; Demi, Sandra; Ringenberg, Michael; Koedinger, Kenneth R. – Grantee Submission, 2016
In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing tutors was shown to be 4-8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its…
Descriptors: Intelligent Tutoring Systems, Programming, Artificial Intelligence, Visual Aids
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – Grantee Submission, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence
