Publication Date
| In 2026 | 0 |
| Since 2025 | 5 |
| Since 2022 (last 5 years) | 14 |
| Since 2017 (last 10 years) | 18 |
| Since 2007 (last 20 years) | 23 |
Descriptor
Source
Author
Publication Type
Education Level
| Postsecondary Education | 5 |
| Higher Education | 4 |
| Secondary Education | 3 |
| Adult Education | 2 |
| Elementary Secondary Education | 2 |
| High Schools | 2 |
| Elementary Education | 1 |
Audience
| Researchers | 14 |
| Practitioners | 13 |
| Teachers | 6 |
| Administrators | 2 |
Location
| France | 1 |
| New Jersey | 1 |
| Pennsylvania | 1 |
| Portugal | 1 |
| Taiwan | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Hsiao-Ping Hsu – TechTrends: Linking Research and Practice to Improve Learning, 2025
The advancement of large language model-based generative artificial intelligence (LLM-based GenAI) has sparked significant interest in its potential to address challenges in computational thinking (CT) education. CT, a critical problem-solving approach in the digital age, encompasses elements such as abstraction, iteration, and generalisation.…
Descriptors: Programming, Prompting, Computation, Thinking Skills
William McGalliard; Samuel Otten – Digital Experiences in Mathematics Education, 2025
This article considers the rise of generative Artificial Intelligence (GenAI) in the context of secondary mathematics education, focusing on its responses to cognitively demanding tasks and the pedagogical implications of these interactions. Using tools such as ChatGPT (OpenAI) and Gemini (Google), we investigate how GenAI engages in complex…
Descriptors: Artificial Intelligence, Secondary School Mathematics, Computer Uses in Education, Mathematics Education
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
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
Betty Exintaris; Nilushi Karunaratne; Elizabeth Yuriev – Journal of Chemical Education, 2023
Successful problem solving is a complex process that requires content knowledge, process skills, developed critical thinking, metacognitive awareness, and deep conceptual reasoning. Teaching approaches to support students developing problem-solving skills include worked examples, metacognitive and instructional scaffolding, and variations of these…
Descriptors: College Bound Students, Problem Solving, Metacognition, Scaffolding (Teaching Technique)
Murphy, Michelle Pauley; Hung, Woei – TechTrends: Linking Research and Practice to Improve Learning, 2023
One hundred years ago, Paul Weiss and Ludwig von Bertalanffy independently proposed that living organisms interact with their environment through systems. In the century that has followed, systems thinking and modeling have grown in tandem with discovery of the vast complexity of the universe at microscopic through astronomic levels. As our…
Descriptors: Systems Approach, Cognitive Processes, Artificial Intelligence, Learning Processes
Gaskins, Nettrice – TechTrends: Linking Research and Practice to Improve Learning, 2023
This paper reviews algorithmic or artificial intelligence (AI) bias in education technology, especially through the lenses of speculative fiction, speculative and liberatory design. It discusses the causes of the bias and reviews literature on various ways that algorithmic/AI bias manifests in education and in communities that are underrepresented…
Descriptors: Algorithms, Bias, Artificial Intelligence, Educational Technology
Anita Pásztor-Kovács; Attila Pásztor; Gyöngyvér Molnár – Interactive Learning Environments, 2023
In this paper, we present an agenda for the research directions we recommend in addressing the issues of realizing and evaluating communication in CPS instruments. We outline our ideas on potential ways to improve: (1) generalizability in Human-Human assessment tools and ecological validity in Human-Agent ones; (2) flexible and convenient use of…
Descriptors: Cooperation, Problem Solving, Evaluation Methods, Teamwork
Jaime Carvalho e Silva – International Journal of Mathematical Education in Science and Technology, 2025
The use of technologies in mathematics education at all levels has been discussed extensively for a number of years. It is one of the few themes that was the object of two ICMI studies, the most recent being published in 2010. Two new approaches, emerging lately in the teaching and learning of Mathematics at all levels, will be discussed:…
Descriptors: Computation, Thinking Skills, Artificial Intelligence, Mathematics Instruction
Tamara Broderick; Andrew Gelman; Rachael Meager; Anna L. Smith; Tian Zheng – Grantee Submission, 2022
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of training data, (2) in the…
Descriptors: Taxonomy, Trust (Psychology), Algorithms, Probability
Xiaoni Zhang – Journal of Information Systems Education, 2025
This teaching tip explores the integration of AI tools into database education. The author describes how instructors can use AI tools to prepare teaching materials and how students can use AI to facilitate database development. The teaching tips provided encompass both course-level objectives and assignment-specific strategies. The inclusion of AI…
Descriptors: Databases, Technology Integration, Critical Thinking, Thinking Skills
Didactic Strategies for the Understanding of the Kalman Filter in Industrial Instrumentation Systems
Flórez C., Oscar D.; Camargo L., Julián R.; Hurtado, Orlando García – Journal of Language and Linguistic Studies, 2022
This paper presents an application of the Kalman filter in signal processing in instrumentation systems when the conditions of the environment generate a large amount of interference for the acquisition of signals from measurement systems. The unwanted interferences make important use of the instrumentation system resources and do not represent…
Descriptors: Measurement, Accuracy, Simulation, Computer Software
Rudy Baez; Henry Sanchez; Neril Sandeep; Duli Pllana – International Journal of Research in Education and Science, 2025
Educational institutions around the world have been integrating AI into their educational practices. Many studies and reports highlight both the advantages and disadvantages of this integration. This paper focuses on the positive aspects of AI in education, specifically through the lens of a high school geometry project at a technology-focused…
Descriptors: Creativity, Critical Thinking, High School Students, Urban Schools
Reed, Stephen K. – Oxford University Press, 2020
In "Cognitive Skills You Need for the 21st Century," Stephen Reed discusses a Future of Jobs report that contrasts trending and declining skills required by the workforce in the year 2022. Trending skills include analytical thinking and innovation, active learning strategies, creativity, reasoning, and complex problem solving. Part One…
Descriptors: Thinking Skills, 21st Century Skills, Labor Force Development, Creative Thinking
How, Meng-Leong; Hung, Wei Loong David – Education Sciences, 2019
Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved…
Descriptors: Stakeholders, Artificial Intelligence, Bayesian Statistics, Probability

Peer reviewed
Direct link
