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Sheng Bi; Zeyi Miao; Qizhi Min – IEEE Transactions on Learning Technologies, 2025
The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder-decoder architectures have advanced traditional KGQG approaches. However, these…
Descriptors: Grammar, Models, Questioning Techniques, Graphs
Andrew Kwok-Fai Lui; Sin-Chun Ng; Stella Wing-Nga Cheung – Interactive Learning Environments, 2024
The technology of automated short answer grading (ASAG) can efficiently process answers according to human-prepared grading examples. Computer-assisted acquisition of grading examples uses a computer algorithm to sample real student responses for potentially good examples. The process is critical for optimizing the grading accuracy of machine…
Descriptors: Grading, Computer Uses in Education, Educational Technology, Artificial Intelligence
Rahm, Lina; Rahm-Skågeby, Jörgen – British Journal of Educational Technology, 2023
This paper suggests that artificial intelligence in education (AIEd) can be fruitfully analysed as 'policies frozen in silicon'. This means that they exist as both materialised and proposed problematisations (problem representations with corresponding solutions). As a theoretical and analytical response, this paper puts forward a heuristic lens…
Descriptors: Artificial Intelligence, Technology Uses in Education, Heuristics, Problem Solving
Alexis Lebis; Jérémie Humeau; Anthony Fleury; Flavien Lucas; Mathieu Vermeulen – International Journal of Artificial Intelligence in Education, 2024
The personalization of curriculum plays a pivotal role in supporting students in achieving their unique learning goals. In recent years, researchers have dedicated efforts to address the challenge of personalizing curriculum through diverse techniques and approaches. However, it is crucial to acknowledge the phenomenon of student forgetting, as…
Descriptors: Individualized Instruction, Curriculum Development, Curriculum Implementation, Memory
Chunpeng Zhai; Santoso Wibowo; Lily D. Li – Smart Learning Environments, 2024
The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance of learning aids. Despite examination of the ethical concerns associated with these technologies, there is a noticeable gap in investigations on how these ethical issues of AI contribute to students'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Cognitive Ability, Decision Making
Clarivando Francisco Belizário Júnior; Fabiano Azevedo Dorça; Luciana Pereira de Assis; Alessandro Vivas Andrade – International Journal of Learning Technology, 2024
Loop-based intelligent tutoring systems (ITSs) support the learning process using a step-by-step problem-solving approach. A limitation of ITSs is that few contents are compatible with this approach. On the other hand, recommendation systems can recommend different types of content but ignore the fine-grained concepts typical of the step-by-step…
Descriptors: Artificial Intelligence, Educational Technology, Individualized Instruction, Cognitive Style
Akhrif, Ouidad; Benfaress, Chaymae; EL Jai, Mostapha; El Bouzekri El Idrissi, Youness; Hmina, Nabil – Interactive Technology and Smart Education, 2022
Purpose: The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict…
Descriptors: Artificial Intelligence, Cooperative Learning, Interdisciplinary Approach, Universities
Using GPT and Authentic Contextual Recognition to Generate Math Word Problems with Difficulty Levels
Wu-Yuin Hwang; Ika Qutsiati Utami – Education and Information Technologies, 2024
Automatic generation of math word problems (MWPs) is a challenging task in Natural Language Processing (NLP), particularly connecting it to real-life problems because it can benefit students in developing a higher level of mathematical thinking. However, most of the MWPs are presented within a scholastic setting in a decontextualized way. This…
Descriptors: Artificial Intelligence, Technology Uses in Education, Mathematics Education, Word Problems (Mathematics)
Codebook Co-Development to Understand Fidelity and Initiate Artificial Intelligence in Serious Games
Ravyse, Werner Siegfried; Blugnaut, A. Seugnet; Botha-Ravyse, Chrisna R. – International Journal of Game-Based Learning, 2020
This study aimed to identify and rank the serious game fidelity themes that should be considered for retaining both the learning potential and predicted market growth of serious games. The authors also investigated existing links between fidelity and AI. The methodology unraveled serious game fidelity through the co-development of a theory- and…
Descriptors: Fidelity, Artificial Intelligence, Educational Games, Computer Games
Lamb, Richard; Hand, Brian; Kavner, Amanda – Journal of Science Education and Technology, 2021
This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test. The Student…
Descriptors: Models, Computation, Content Area Writing, Science Education
Dasgupta, Ishita; Guo, Demi; Gershman, Samuel J.; Goodman, Noah D. – Cognitive Science, 2020
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present…
Descriptors: Natural Language Processing, Man Machine Systems, Heuristics, Sentences
Choffin, Benoît; Popineau, Fabrice; Bourda, Yolaine – Journal of Educational Data Mining, 2021
Adaptive spacing algorithms are powerful tools for helping learners manage their study time efficiently. By personalizing the temporal distribution of retrieval practice of a given piece of knowledge, they improve learners' long-term memory retention compared to fixed review schedules. However, such algorithms are generally designed for the pure…
Descriptors: Heuristics, Time Factors (Learning), Memorization, Time Management
Chen, Zhanwen; Li, Shiyao; Rashedi, Roxanne; Zi, Xiaoman; Elrod-Erickson, Morgan; Hollis, Bryan; Maliakal, Angela; Shen, Xinyu; Zhao, Simeng; Kunda, Maithilee – Grantee Submission, 2020
Modern social intelligence includes the ability to watch videos and answer questions about social and theory-of-mind-related content, e.g., for a scene in "Harry Potter," "Is the father really upset about the boys flying the car?" Social visual question answering (social VQA) is emerging as a valuable methodology for studying…
Descriptors: Visual Stimuli, Questioning Techniques, Social Cognition, Video Technology
Verma, Mudit – Online Submission, 2018
In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial intelligence is becoming a popular field in computer science as it has enhanced the human life in many areas. Artificial…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Computer Software
Mandel, Travis Scott – ProQuest LLC, 2017
When a new student comes to play an educational game, how can we determine what content to give them such that they learn as much as possible? When a frustrated customer calls in to a helpline, how can we determine what to say to best assist them? When an ill patient comes in to the clinic, how do we determine what tests to run and treatments to…
Descriptors: Reinforcement, Learning Processes, Student Evaluation, Data Collection