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Lu, Yu; Wang, Deliang; Chen, Penghe; Meng, Qinggang; Yu, Shengquan – International Journal of Artificial Intelligence in Education, 2023
As a prominent aspect of modeling learners in the education domain, knowledge tracing attempts to model learner's cognitive process, and it has been studied for nearly 30 years. Driven by the rapid advancements in deep learning techniques, deep neural networks have been recently adopted for knowledge tracing and have exhibited unique advantages…
Descriptors: Learning Processes, Artificial Intelligence, Intelligent Tutoring Systems, Data Analysis
Pelánek, Radek; Effenberger, Tomáš; Cechák, Jaroslav – International Journal of Artificial Intelligence in Education, 2022
Complexity and difficulty are two closely related but distinct concepts. These concepts are important in the development of intelligent learning systems, e.g., for sequencing items, student modeling, or content management. We show how to use complexity and difficulty measures in the development of learning systems and provide guidance on how to…
Descriptors: Difficulty Level, Intelligent Tutoring Systems, Measurement Techniques, Computer System Design
Danielle S. McNamara – Grantee Submission, 2024
Our primary objective in this Special Issue was to respond to potential criticisms of AIED in potentially "perpetuating poor pedagogic practices, datafication, and introducing classroom surveillance" and to comment on the future of AIED in its coming of age. My overarching assumption in response to this line of critiques is that humans…
Descriptors: Educational Practices, Educational Quality, Intelligent Tutoring Systems, Artificial Intelligence
Kole Norberg; Husni Almoubayyed; Stephen E. Fancsali; Logan De Ley; Kyle Weldon; April Murphy; Steve Ritter – Grantee Submission, 2023
Large Language Models have recently achieved high performance on many writing tasks. In a recent study, math word problems in Carnegie Learning's MATHia adaptive learning software were rewritten by human authors to improve their clarity and specificity. The randomized experiment found that emerging readers who received the rewritten word problems…
Descriptors: Word Problems (Mathematics), Mathematics Instruction, Artificial Intelligence, Intelligent Tutoring Systems
A. N. Varnavsky – IEEE Transactions on Learning Technologies, 2024
The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The…
Descriptors: Hierarchical Linear Modeling, Intelligent Tutoring Systems, Individualized Instruction, Electronic Learning
Zhou, Guojing; Azizsoltani, Hamoon; Ausin, Markel Sanz; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2022
In interactive e-learning environments such as Intelligent Tutoring Systems, pedagogical decisions can be made at different levels of granularity. In this work, we focus on making decisions at "two levels": whole problems vs. single steps and explore three types of granularity: "problem-level only" ("Prob-Only"),…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Decision Making, Problem Solving
Nicolas J. Tanchuk; Rebecca M. Taylor – Educational Theory, 2025
AI tutors are promised to expand access to personalized learning, improving student achievement and addressing disparities in resources available to students across socioeconomic contexts. The rapid development and introduction of AI tutors raises fundamental questions of epistemic trust in education. What criteria should guide students' critical…
Descriptors: Individualized Instruction, Artificial Intelligence, Technology Uses in Education, Tutors
Micah Watanabe; Tracy Arner; Danielle McNamara – Reading Teacher, 2024
Students in the 3rd and 4th grade often encounter what has been called a reading "slump" when their class curriculums increasingly ask them to comprehend and learn from texts. Students are more likely to struggle if they have not been offered sufficient opportunities to build world and domain knowledge and engage in challenging…
Descriptors: Reading Instruction, Reading Strategies, Elementary School Students, Grade 3
Lodder, Josje; Heeren, Bastiaan; Jeuring, Johan; Neijenhuis, Wendy – International Journal of Artificial Intelligence in Education, 2021
This paper describes LOGAX, an interactive tutoring tool that gives hints and feedback to a student who stepwise constructs a Hilbert-style axiomatic proof in propositional logic. LOGAX generates proofs to calculate hints and feedback. We compare these generated proofs with expert proofs and student solutions, and conclude that the quality of the…
Descriptors: Intelligent Tutoring Systems, Cues, Feedback (Response), Mathematical Logic
Tsung-Ying Chen – Journal of Psycholinguistic Research, 2024
Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA (https://enigma-lang.org) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is…
Descriptors: Artificial Intelligence, Grammar, Computer Software, Handheld Devices
Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Fatma Outay; Nafaa Jabeur; Fahmi Bellalouna; Tasnim Al Hamzi – Smart Learning Environments, 2024
To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background…
Descriptors: Competence, Learning Activities, Individual Characteristics, Computer Simulation
Maria Y. Rodriguez; Lauri Goldkind; Bryan G. Victor; Barbara Hiltz; Brian E. Perron – Journal of Social Work Education, 2024
The most recent Council on Social Work Education's Educational Policy and Accreditation Standards (EPAS) demands that social workers develop competence in the ethical and professional deployment of technology. Arguably, artificial intelligence has become a critical element in the technological landscape, most recently with the advent of Generative…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Educational Policy, Social Work
Alberto Giretti; Dilan Durmus; Massimo Vaccarini; Matteo Zambelli; Andrea Guidi; Franco Ripa di Meana – International Association for Development of the Information Society, 2023
This paper provides a possible strategy for integrating large language artificial intelligence models (LLMs) in supporting students' education in artistic or design activities. We outline the methodological foundations concerning the integration of CHATGPT LLM in the educational approach aimed at enhancing artistic conception and design ideation.…
Descriptors: Art Education, Design, Artificial Intelligence, Computer Software
Minkyoung Kim; Lauren Adlof – TechTrends: Linking Research and Practice to Improve Learning, 2024
ChatGPT, an artificial intelligence (AI) language model, holds significant promise for improving the quality and efficiency of teaching and learning. However, its potential challenges and disruptions in education systems require further investigation for a deeper understanding and mitigation. Given that ChatGPT is already being utilized and…
Descriptors: Computer Software, Computational Linguistics, Intelligent Tutoring Systems, Teaching Methods