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Norizan Mat Diah; Syahirul Riza; Suzana Ahmad; Norzilah Musa; Shakirah Hashim – Journal of Education and Learning (EduLearn), 2025
Sudoku is a puzzle that has a unique solution. No matter how many methods are used, the result will always be the same. The player thought that the number of givens or clues, the initial value on the Sudoku puzzles, would significantly determine the difficulty level, which is not necessarily correct. This research uses two search algorithms,…
Descriptors: Puzzles, Artificial Intelligence, Problem Solving, Algorithms
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Qiwei He; Qingzhou Shi; Elizabeth L. Tighe – Grantee Submission, 2023
Increased use of computer-based assessments has facilitated data collection processes that capture both response product data (i.e., correct and incorrect) and response process data (e.g., time-stamped action sequences). Evidence suggests a strong relationship between respondents' correct/incorrect responses and their problem-solving proficiency…
Descriptors: Artificial Intelligence, Problem Solving, Classification, Data Use
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Ouyang, Fan; Xu, Weiqi; Cukurova, Mutlu – International Journal of Computer-Supported Collaborative Learning, 2023
Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical…
Descriptors: Artificial Intelligence, Learning Analytics, Cooperative Learning, Problem Solving
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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
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Changyu Yang; Adam Stivers – Journal of Education for Business, 2024
The rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Comparative Analysis
Yihe Zhang – ProQuest LLC, 2024
Machine learning (ML) techniques have been successfully applied to a wide array of applications. This dissertation aims to take application data handling into account when developing ML-based solutions for real-world problems through a holistic framework. To demonstrate the generality of our framework, we consider two real-world applications: spam…
Descriptors: Artificial Intelligence, Problem Solving, Social Media, Computer Mediated Communication
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Skulmowski, Alexander – Educational Psychology Review, 2023
This review is aimed at synthesizing current findings concerning technology-based cognitive offloading and the associated effects on learning and memory. While cognitive externalization (i.e., using the environment to outsource mental computation) is a highly useful technique in various problem-solving tasks, a growing body of research suggests…
Descriptors: Mental Computation, Learning Processes, Memory, Problem Solving
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Michael E. Ellis; K. Mike Casey; Geoffrey Hill – Decision Sciences Journal of Innovative Education, 2024
Large Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high-level programming…
Descriptors: Artificial Intelligence, Programming Languages, Programming, Homework
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Stefan Depeweg; Contantin A. Rothkopf; Frank Jäkel – Cognitive Science, 2024
More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial…
Descriptors: Visual Learning, Problem Solving, Cognitive Science, Artificial Intelligence
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Bihao Hu; Longwei Zheng; Jiayi Zhu; Lishan Ding; Yilei Wang; Xiaoqing Gu – IEEE Transactions on Learning Technologies, 2024
This study explores and analyzes the specific performance of large language models (LLMs) in instructional design, aiming to unveil their potential strengths and possible weaknesses. Recently, the influence of LLMs has gradually increased in multiple fields, yet exploratory research on their application in education remains relatively scarce. In…
Descriptors: Artificial Intelligence, Natural Language Processing, Instructional Design, Prompting
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JiYeon Hong; Kwihoon Kim – Education and Information Technologies, 2025
The advent of the 4th Industrial Revolution era requires the creation of new value through convergence rather than piecemeal use of technology. In that sense, the convergence of AI's learning ability and IoT connectivity makes it possible to build a more intelligent and automated system. Therefore, AI education in preparation for the era of…
Descriptors: Artificial Intelligence, Technology Uses in Education, Internet, Digital Literacy
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Umar Alkafaween; Ibrahim Albluwi; Paul Denny – Journal of Computer Assisted Learning, 2025
Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some…
Descriptors: Automation, Grading, Introductory Courses, Programming
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Urtasun, Ainhoa – Industry and Higher Education, 2023
This report describes a teaching experience with undergraduates to approach, in a simple and practical way, artificial intelligence (AI) and machine learning (ML) -- general-purpose technologies that are highly demanded in any industry today. The article shows how business undergraduates with no prior experience in coding can use AI and ML to…
Descriptors: Undergraduate Students, Student Empowerment, Artificial Intelligence, Business Education
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Liang, Yicong; Zou, Di; Xie, Haoran; Wang, Fu Lee – Smart Learning Environments, 2023
The pretrained large language models have been widely tested for their performance on some challenging tasks including arithmetic, commonsense, and symbolic reasoning. Recently how to combine LLMs with prompting techniques has attracted lots of researchers to propose their models to automatically solve math word problems. However, most research…
Descriptors: Science Instruction, Physics, Artificial Intelligence, Computer Mediated Communication
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Lonneke Boels; Alex Lyford; Arthur Bakker; Paul Drijvers – Frontline Learning Research, 2023
Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how…
Descriptors: Data Interpretation, Interpretive Skills, Statistical Distributions, Graphs
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