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Download full textGreenberg, Daphne; Miller, Christine; Graesser, Arthur C. – Adult Literacy Education, 2023
This article is written by two researchers and a teacher involved with the development and implementation of a web-based intelligent tutoring system for adults reading at elementary levels. A description of the tool is provided, followed by some of the challenges faced in designing, developing, and using the tool in adult literacy classrooms.
Descriptors: Intelligent Tutoring Systems, Adult Students, Adult Basic Education, Reading Comprehension
Direct linkLi, Haiying; Graesser, Arthur C. – Journal of Research on Technology in Education, 2021
This study investigated how computer agents' language style affects summary writing in an Intelligent Tutoring System, called CSAL AutoTutor. Participants interacted with two computer agents in one of three language styles: (1) a "formal" language style, (2) an "informal" language style, and (3) a "mixed" language…
Descriptors: Intelligent Tutoring Systems, Language Styles, Writing (Composition), Writing Improvement
Graesser, Arthur C.; Greenberg, Daphne; Frijters, Jan C.; Talwar, Amani – Grantee Submission, 2021
A large percentage of adults throughout the world have low reading skills. Computer technologies can potentially help these adults improve their literacy in addition to instructors at literacy centers. AutoTutor was designed to teach comprehension strategies by implementing conversational "trialogues" in which two computer agents (tutor…
Descriptors: Reading Achievement, Learner Engagement, Reading Comprehension, Intervention
Fang, Ying; Ren, Zhihong; Hu, Xiangen; Graesser, Arthur C. – Educational Psychology, 2019
Assessment and Learning in Knowledge Spaces (ALEKS) is one of the widely used online intelligent tutoring systems (ITS) in the USA, but it has rarely been included in meta-analyses of ITS efficacy to help students learn. We conducted a meta-analysis to assess the effectiveness of ALEKS on learning. A total of 15 empirical studies were conducted…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Meta Analysis, Instructional Effectiveness
Cai, 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
Shi, Genghu; Wang, Lijia; Zhang, Liang; Shubeck, Keith; Peng, Shun; Hu, Xiangen; Graesser, Arthur C. – Grantee Submission, 2021
Adult learners with low literacy skills compose a highly heterogeneous population in terms of demographic variables, educational backgrounds, knowledge and skills in reading, self-efficacy, motivation etc. They also face various difficulties in consistently attending offline literacy programs, such as unstable worktime, transportation…
Descriptors: Intelligent Tutoring Systems, Adult Literacy, Adult Students, Reading Comprehension
Fang, Ying; Lippert, Anne; Cai, Zhiqiang; Chen, Su; Frijters, Jan C.; Greenberg, Daphne; Graesser, Arthur C. – International Journal of Artificial Intelligence in Education, 2022
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an…
Descriptors: Intelligent Tutoring Systems, Classification, Reading Comprehension, Accuracy
Graesser, Arthur C.; Hu, Xiangen; Nye, Benjamin D.; VanLehn, Kurt; Kumar, Rohit; Heffernan, Cristina; Heffernan, Neil; Woolf, Beverly; Olney, Andrew M.; Rus, Vasile; Andrasik, Frank; Pavlik, Philip; Cai, Zhiqiang; Wetzel, Jon; Morgan, Brent; Hampton, Andrew J.; Lippert, Anne M.; Wang, Lijia; Cheng, Qinyu; Vinson, Joseph E.; Kelly, Craig N.; McGlown, Cadarrius; Majmudar, Charvi A.; Morshed, Bashir; Baer, Whitney – International Journal of STEM Education, 2018
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,…
Descriptors: Intelligent Tutoring Systems, STEM Education, Electronics, Integrated Curriculum
Chen, Su; Fang, Ying; Shi, Genghu; Sabatini, John; Greenberg, Daphne; Frijters, Jan; Graesser, Arthur C. – Grantee Submission, 2021
This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Attention, Adult Literacy
Hu, Xiangen; Cai, Zhiqiang; Hampton, Andrew J.; Cockroft, Jody L.; Graesser, Arthur C.; Copland, Cameron; Folsom-Kovarik, Jeremiah T. – Grantee Submission, 2019
In this paper, we consider a minimalistic and behavioristic view of AIS to enable a standardizable mapping of both the behavior of the system and of the learner. In this model, the "learners" interact with the learning "resources" in a given learning "environment" following preset steps of learning…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Metadata, Behavior Patterns
Chen, Su; Lippert, Anne; Shi, Genghu; Fang, Ying; Graesser, Arthur C. – Grantee Submission, 2018
This paper describes a novel automated disengagement tracing system (DTS) that detects mind wandering in students using AutoTutor, an Intelligent Tutoring System (ITS) with conversational agents. DTS is based on an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the reading time and response…
Descriptors: Learner Engagement, Intelligent Tutoring Systems, Reading Comprehension, Adult Literacy
Graesser, Arthur C. – International Journal of Artificial Intelligence in Education, 2016
AutoTutor helps students learn by holding a conversation in natural language. AutoTutor is adaptive to the learners' actions, verbal contributions, and in some systems their emotions. Many of AutoTutor's conversation patterns simulate human tutoring, but other patterns implement ideal pedagogies that open the door to computer tutors eclipsing…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Communication Strategies, Dialogs (Language)
Lippert, Anne; Gatewood, Jessica; Cai, Zhiqiang; Graesser, Arthur C. – Grantee Submission, 2019
One out of six adults in the United States possesses low literacy skills. Many advocates believe that technology can pave the way for these adults to gain the skills that they desire. This article describes an adaptive intelligent tutoring system called AutoTutor that is designed to teach adults comprehension strategies across different levels of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Adult Literacy, Skill Development
Fang, Ying; Lippert, Anne; Cai, Zhiqiang; Chen, Su; Frijters, Jan C.; Greenberg, Daphne; Graesser, Arthur C. – Grantee Submission, 2021
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Reading Comprehension
Shi, Genghu; Lippert, Anne M.; Shubeck, Keith; Fang, Ying; Chen, Su; Pavlik, Philip, Jr.; Greenberg, Daphne; Graesser, Arthur C. – Grantee Submission, 2018
Reading comprehension is often assessed by having students read passages and administering a test that assesses their understanding of the text. Shorter assessments may fail to give a full picture of comprehension ability while more thorough ones can be time consuming and costly. This study used data from a conversational intelligent tutoring…
Descriptors: Reading Comprehension, Intelligent Tutoring Systems, Adults, Accuracy
