Publication Date
| In 2026 | 0 |
| Since 2025 | 3 |
| Since 2022 (last 5 years) | 22 |
| Since 2017 (last 10 years) | 47 |
| Since 2007 (last 20 years) | 87 |
Descriptor
| Cognitive Processes | 109 |
| Intelligent Tutoring Systems | 109 |
| Difficulty Level | 36 |
| Educational Technology | 31 |
| Models | 27 |
| Foreign Countries | 26 |
| Problem Solving | 22 |
| Computer Assisted Instruction | 19 |
| Instructional Design | 19 |
| Teaching Methods | 19 |
| Technology Uses in Education | 19 |
| More ▼ | |
Source
Author
| Lajoie, Susanne P. | 5 |
| Graesser, Arthur C. | 4 |
| Cai, Zhiqiang | 3 |
| Wang, Tingting | 3 |
| Butler, Heather | 2 |
| Chang, Te-Jeng | 2 |
| Forsyth, Carol | 2 |
| Hampton, Andrew J. | 2 |
| Hsu, Pi-Shan | 2 |
| Hu, Xiangen | 2 |
| Huang, Xiaoshan | 2 |
| More ▼ | |
Publication Type
Education Level
| Higher Education | 33 |
| Postsecondary Education | 30 |
| Secondary Education | 14 |
| Middle Schools | 13 |
| Junior High Schools | 10 |
| Elementary Education | 9 |
| Adult Education | 4 |
| Grade 6 | 4 |
| High Schools | 4 |
| Intermediate Grades | 4 |
| Grade 10 | 3 |
| More ▼ | |
Audience
Location
| Brazil | 4 |
| China | 4 |
| South Africa | 3 |
| Algeria | 2 |
| France | 2 |
| Germany | 2 |
| Massachusetts | 2 |
| Pennsylvania | 2 |
| Taiwan | 2 |
| Thailand | 2 |
| Turkey | 2 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
| Program for International… | 2 |
| ACT Assessment | 1 |
| Gates MacGinitie Reading Tests | 1 |
| Massachusetts Comprehensive… | 1 |
| Motivated Strategies for… | 1 |
What Works Clearinghouse Rating
Hu, Yuanyuan; Donald, Claire; Giacaman, Nasser – International Journal of Artificial Intelligence in Education, 2023
This paper investigates using multi-label deep learning approach to extending the understanding of cognitive presence in MOOC discussions. Previous studies demonstrate the challenges of subjectivity in manual categorisation methods. Training automatic single-label classifiers may preserve this subjectivity. Using a triangulation approach, we…
Descriptors: Classification, MOOCs, Artificial Intelligence, Intelligent Tutoring Systems
Ziyi Kuang; Xiaxia Jiang; Keith T. Shubeck; Xiaoxue Leng; Yahong Li; Rui Zhang; Zhen Wang; Shun Peng; Xiangen Hu – Educational Psychology, 2024
This study explored the role of question types and prior knowledge in vicarious learning with an intelligent tutoring system. In experiment 1, the participants were assigned to three conditions (deep questions, shallow questions, control), the results showed that participants in the deep questions condition had higher retention test scores than…
Descriptors: Questioning Techniques, Intelligent Tutoring Systems, Cognitive Processes, College Students
Helene Ackermann; Anja Henke; Johann Chevalère; Hae Seon Yun; Verena V. Hafner; Niels Pinkwart; Rebecca Lazarides – npj Science of Learning, 2025
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…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Animals, Human Body
Daryn A. Dever; Megan D. Wiedbusch; Sarah M. Romero; Roger Azevedo – British Journal of Educational Technology, 2024
Intelligent tutoring systems (ITSs) incorporate pedagogical agents (PAs) to scaffold learners' self-regulated learning (SRL) via prompts and feedback to promote learners' monitoring and regulation of their cognitive, affective, metacognitive and motivational processes to achieve their (sub)goals. This study examines PAs' effectiveness in…
Descriptors: Intelligent Tutoring Systems, Scaffolding (Teaching Technique), Independent Study, Prompting
Mark Abdelshiheed; Tiffany Barnes; Min Chi – International Journal of Artificial Intelligence in Education, 2024
Two metacognitive knowledge types in deductive domains are procedural and conditional. This work presents a preliminary study on the impact of metacognitive knowledge and motivation on transfer across two Intelligent Tutoring Systems (ITSs), then two experiments on metacognitive knowledge instruction. Throughout this work, we trained students on a…
Descriptors: Metacognition, Intelligent Tutoring Systems, Cognitive Processes, Learning Strategies
Wang, Tingting; Li, Shan; Huang, Xiaoshan; Pan, Zexuan; Lajoie, Susanne P. – Education and Information Technologies, 2023
Students process qualitatively and quantitatively different information during the dynamic self-regulated learning (SRL) process, and thus they may experience varying cognitive load in different SRL behaviors. However, there is limited research on the role of cognitive load in SRL. This study examined students' cognitive load in micro-level SRL…
Descriptors: Cognitive Processes, Difficulty Level, Learning Strategies, Self Efficacy
Schulz, Sandra; McLaren, Bruce M.; Pinkwart, Niels – International Journal of Artificial Intelligence in Education, 2023
This paper develops a method for the construction and evaluation of cognitive models to support students in their problem-solving skills during robotics in school, aiming to build a basis for an implementation of a tutoring system in the future. Two Wizard-of-Oz studies were conducted, one in the classroom and one in the lab. Based on the…
Descriptors: Cognitive Processes, Models, Intelligent Tutoring Systems, Robotics
Carlon, May Kristine Jonson; Cross, Jeffrey S. – Open Education Studies, 2022
Adaptive learning is provided in intelligent tutoring systems (ITS) to enable learners with varying abilities to meet their expected learning outcomes. Despite the personalized learning afforded by ITSes using adaptive learning, learners are still susceptible to shallow learning. Introducing metacognitive tutoring to teach learners how to be aware…
Descriptors: Intelligent Tutoring Systems, Metacognition, Cognitive Processes, Difficulty Level
Lajoie, Susanne P.; Poitras, Eric G.; Doleck, Tenzin; Huang, Lingyun – Education and Information Technologies, 2023
The present paper builds on the literature that emphasizes the importance of self-regulation for academic learning or self-regulated learning (SRL). SRL research has traditionally focused on count measures of SRL processing events, however, another important measure of SRL is being recognized: time-on-task. The current study captures the influence…
Descriptors: Intelligent Tutoring Systems, Self Management, Time on Task, Correlation
Wang, Tingting; Lajoie, Susanne P. – Educational Psychology Review, 2023
Although cognitive load (CL) and self-regulated learning (SRL) have been widely recognized as two determinant factors of students' performance, the integration of these two factors is still in its infancy. To further specify why and how CL links with SRL, we first conducted an overview to describe the multiple dimensions of cognitive load (i.e.,…
Descriptors: Cognitive Ability, Metacognition, Cognitive Processes, Correlation
Soonri Choi; Soomin Kang; Kyungmin Lee; Hongjoo Ju; Jihoon Song – Contemporary Educational Technology, 2024
This study proposes that the gestures of an agent tutor in a multimedia learning environment can generate positive and negative emotions in learners and influence their cognitive processes. To achieve this, we developed and integrated positive and negative agent tutor gestures in a multimedia learning environment directed by cognitive gestures.…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Cognitive Processes, Difficulty Level
Wang, Yue; Eysink, Tessa H. S.; Qu, Zhili; Yang, Zhijiao; Shan, Huaming; Zhang, Nan; Zhang, Hai; Wang, Yining – Journal of Educational Computing Research, 2022
This research used a comparative quasi-experimental design to investigate the impacts of an IRS in the ILE on students' academic performance, cognitive load, and satisfaction with the lesson. A total of 31 middle school students were divided into the experimental group and the control group. Mann-Whitney U tests yielded three major results. (1)…
Descriptors: Intelligent Tutoring Systems, Active Learning, Academic Achievement, Cognitive Processes
Xuanyan Zhong; Zehui Zhan – Interactive Technology and Smart Education, 2025
Purpose: The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners' computational thinking. Design/methodology/approach: By…
Descriptors: Intelligent Tutoring Systems, Computer Science Education, Programming, Independent Study
Xiaoqing Hong; Li Guo – Education and Information Technologies, 2025
The study investigates the effects of AI-enhanced multi-display language teaching systems on English as a Foreign Language (EFL) learners. Utilizing a pretest-posttest random assignment experimental design, the research involved 302 EFL students aged 19 to 28 in a higher education setting. The study examines the effects of AI-powered virtual…
Descriptors: Artificial Intelligence, Technology Uses in Education, Learning Motivation, Cognitive Processes
Sharma, Kshitij; Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Journal of Computer Assisted Learning, 2021
When students are working collaboratively and communicating verbally in a technology-enhanced environment, the system cannot track what collaboration is happening outside of the technology, making it difficult to fully assess the collaboration of the students and adapt accordingly. In this article, we propose using gaze measures as a proxy for…
Descriptors: Cooperative Learning, Interpersonal Communication, Eye Movements, Problem Solving

Peer reviewed
Direct link
