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Ziyan Yang; Jia Hu; Shaochun Zhong; Lan Yang; Geyong Min – Education and Information Technologies, 2025
Intelligent technology plays a pivotal role in revolutionizing learning assessments, overcoming the constraints of traditional assessment methods and driving educational innovation. Knowledge tracing (KT) emerges as a critical component for assessing students' learning states and forecasting their future performance. However, existing graph-based…
Descriptors: Learning Processes, Artificial Intelligence, Graphs, Concept Mapping
Laneshia Conner; Kristel Scoresby; Keith J. Watts – Journal of Faculty Development, 2025
Academic mentorship is essential for faculty development, yet mid-career researchers have limited resources. This study explores AI-assisted mentorship models using biosketch analysis, AI mapping, and human expertise to enhance mentor selection. Tools like Google NotebookLM and Mural streamlined coding, visualization, and theme identification,…
Descriptors: Artificial Intelligence, Faculty Development, Researchers, Mentors
Bao Wang; Philippe J. Giabbanelli – International Journal of Artificial Intelligence in Education, 2024
Knowledge maps have been widely used in knowledge elicitation and representation to evaluate and guide students' learning. To effectively evaluate maps, instructors must select the most informative map features that capture students' knowledge constructs. However, there is currently no clear and consistent criteria to select such features, as…
Descriptors: Concept Mapping, Evaluation Methods, Student Evaluation, Algorithms
Reese Butterfuss; Harold Doran – Educational Measurement: Issues and Practice, 2025
Large language models are increasingly used in educational and psychological measurement activities. Their rapidly evolving sophistication and ability to detect language semantics make them viable tools to supplement subject matter experts and their reviews of large amounts of text statements, such as educational content standards. This paper…
Descriptors: Alignment (Education), Academic Standards, Content Analysis, Concept Mapping
Gi Woong Choi; Soo Hyeon Kim; Daeyeoul Lee; Jewoong Moon – TechTrends: Linking Research and Practice to Improve Learning, 2024
Recently, generative AI has been at the center of disruptive innovation in various settings, including educational sectors. This article investigates ChatGPT, which is one of the most prominent generative AI in the market, to explore its usefulness and potential for instructional design. Four researchers used a set of prompts to generate a course…
Descriptors: Artificial Intelligence, Instructional Design, Information Technology, Course Content
Li Chen; Gen Li; Boxuan Ma; Cheng Tang; Masanori Yamada – International Association for Development of the Information Society, 2024
This paper proposes a three-step approach to develop knowledge graphs that integrate textbook-based target knowledge graph with student dialogue-based knowledge graphs. The study was conducted in seventh-grade STEM classes, following a collaborative problem solving process. First, the proposed approach generates a comprehensive target knowledge…
Descriptors: Concept Mapping, Graphs, Cooperative Learning, Problem Solving
Qian Wang; Shiwang Hou; Sixian Wan; Xin Feng; Hao Feng – European Journal of Education, 2025
To prepare undergraduates for complex careers, interdisciplinary higher education is gaining popularity. However, implementing interdisciplinary learning in engineering management is challenging due to the complex and intertwined knowledge structures. While smart education platforms provide access to extensive knowledge bases, the intricate web of…
Descriptors: Undergraduate Students, Higher Education, Interdisciplinary Approach, Engineering Education
Karumbaiah, Shamya; Zhang, Jiayi; Baker, Ryan S.; Scruggs, Richard; Cade, Whitney; Clements, Margaret; Lin, Shuqiong – International Educational Data Mining Society, 2022
Considerable amount of research in educational data mining has focused on developing efficient algorithms for Knowledge Tracing (KT). However, in practice, many real-world learning systems used at scale struggle to implement KT capabilities, especially if they weren't originally designed for it. One key challenge is to accurately label existing…
Descriptors: Artificial Intelligence, Middle School Students, Models, Concept Mapping
Bin Meng; Fan Yang – International Journal of Web-Based Learning and Teaching Technologies, 2025
This paper proposes a computer-aided teaching model using knowledge graph construction and learning path recommendation. It first creates a multimodal knowledge graph to illustrate complex relationships among knowledge. Learning elements and sequences are then used to form time sequences stored as directed graphs, supporting flexible path…
Descriptors: Students, Teachers, Computer Assisted Instruction, Knowledge Representation
Xinya Chen; Baiyi Jia; Xiaoyang Peng; Huichen Zhao; Jiajia Yao; Zhen Wang; Shuhui Zhu – Education and Information Technologies, 2025
Although online argumentation provides students with sufficient time to think, they often lack a clear argument structure and timely guidance during the process. This may negatively affect students' abilities to support or rebut arguments and to develop their critical thinking skills. In this study, 64 sophomores were evenly assigned to either a…
Descriptors: Artificial Intelligence, Technology Uses in Education, Persuasive Discourse, Concept Mapping
Liu, Chunhong; Zhang, Haoyang; Zhang, Jieyu; Zhang, Zhengling; Yuan, Peiyan – International Journal of Information and Communication Technology Education, 2023
Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally…
Descriptors: Educational Technology, Artificial Intelligence, Electronic Learning, Concept Mapping
Sri Wahyuni; Nur Hidayanto Pancoro Setyo Putro; Anwar Efendi – Advanced Education, 2024
Notwithstanding the increase in research on artificial intelligence-infused English language learning, several issues remain inadequately addressed. Thus, this paper provides a systematic review and analyzes previous studies to pinpoint fruitful knowledge gaps and outline approaches for future research directions. Two approaches, bibliometric and…
Descriptors: Artificial Intelligence, Educational Trends, Technology Uses in Education, Handheld Devices
Rugh, Michael Sze-hon; Capraro, Mary Margaret; Capraro, Robert Michael – Electronic Journal of e-Learning, 2023
The Dynamic and Interactive Mathematical Expressions (DIME) Map system automatically generates DIME maps, which are personalizable and manipulable concept maps that allow students to interact with the mathematical concepts contained in any portable document format (PDF) textbook or document. A teacher can automatically upload a PDF textbook…
Descriptors: Self Efficacy, Artificial Intelligence, Concept Mapping, Technology Integration
Lee, Chia-An; Huang, Nen-Fu; Tzeng, Jian-Wei; Tsai, Pin-Han – IEEE Transactions on Learning Technologies, 2023
Massive open online courses offer a valuable platform for efficient and flexible learning. They can improve teaching and learning effectiveness by enabling the evaluation of learning behaviors and the collection of feedback from students. The knowledge map approach constitutes a suitable tool for evaluating and presenting students' learning…
Descriptors: Artificial Intelligence, MOOCs, Concept Mapping, Student Evaluation
Baran, Evrim; AlZoubi, Dana; Morales, Anasilvia Salazar – TechTrends: Linking Research and Practice to Improve Learning, 2023
Computational analysis methods and machine learning techniques introduce innovative ways to capture classroom interactions and display data on analytics dashboards. Automated classroom analytics employ advanced data analysis, providing educators with comprehensive insights into student participation, engagement, and behavioral trends within…
Descriptors: Automation, Learning Analytics, Stakeholders, Computation

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