Publication Date
| In 2026 | 0 |
| Since 2025 | 11 |
| Since 2022 (last 5 years) | 28 |
| Since 2017 (last 10 years) | 31 |
| Since 2007 (last 20 years) | 31 |
Descriptor
| Artificial Intelligence | 31 |
| Feedback (Response) | 31 |
| Learning Analytics | 31 |
| Student Evaluation | 10 |
| Technology Uses in Education | 10 |
| Intelligent Tutoring Systems | 8 |
| Natural Language Processing | 8 |
| Automation | 7 |
| Educational Technology | 7 |
| Higher Education | 7 |
| Computer Software | 6 |
| More ▼ | |
Source
Author
| Dragan Gaševic | 2 |
| Ahmad Faza | 1 |
| Akinpelu A. Oyekunle | 1 |
| Anu Mary Chacko | 1 |
| Avraamidou, Lucy | 1 |
| Barbara Šlibar | 1 |
| Bearman, Margaret | 1 |
| Bhagya Maheshi | 1 |
| Bouchet, Francois, Ed. | 1 |
| Boud, David | 1 |
| Buckingham Shum, Simon | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 25 |
| Reports - Research | 22 |
| Information Analyses | 6 |
| Speeches/Meeting Papers | 4 |
| Collected Works - Proceedings | 2 |
| Reports - Descriptive | 2 |
| Reports - Evaluative | 2 |
| Books | 1 |
Education Level
| Higher Education | 13 |
| Postsecondary Education | 13 |
| Elementary Secondary Education | 3 |
| Elementary Education | 1 |
| Grade 9 | 1 |
| High Schools | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
| National Assessment of… | 1 |
What Works Clearinghouse Rating
Wannapon Suraworachet; Qi Zhou; Mutlu Cukurova – Journal of Computer Assisted Learning, 2025
Background: Many researchers work on the design and development of multimodal collaboration support systems with AI, yet very few of these systems are mature enough to provide actionable feedback to students in real-world settings. Therefore, a notable gap exists in the literature regarding students' perceptions of such systems and the feedback…
Descriptors: Graduate Students, Student Attitudes, Artificial Intelligence, Cooperative Learning
Buckingham Shum, Simon; Lim, Lisa-Angelique; Boud, David; Bearman, Margaret; Dawson, Phillip – International Journal of Educational Technology in Higher Education, 2023
Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed "feedback literacy." A previously published teacher "feedback literacy competency framework" has identified what is needed by teachers to implement…
Descriptors: Automation, Feedback (Response), Learning Analytics, Artificial Intelligence
Flora Ji-Yoon Jin; Bhagya Maheshi; Wenhua Lai; Yuheng Li; Danijela Gasevic; Guanliang Chen; Nicola Charwat; Philip Wing Keung Chan; Roberto Martinez-Maldonado; Dragan Gaševic; Yi-Shan Tsai – Journal of Learning Analytics, 2025
This paper explores the integration of generative AI (GenAI) in the feedback process in higher education through a learning analytics (LA) tool, examined from a feedback literacy perspective. Feedback literacy refers to students' ability to understand, evaluate, and apply feedback effectively to improve their learning, which is crucial for…
Descriptors: College Students, Student Attitudes, Artificial Intelligence, Learning Analytics
Ramaswami, Gomathy; Susnjak, Teo; Mathrani, Anuradha; Umer, Rahila – Technology, Knowledge and Learning, 2023
Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing…
Descriptors: Prediction, Learning Analytics, Learning Management Systems, Identification
Darvishi, Ali; Khosravi, Hassan; Sadiq, Shazia; Gaševic, Dragan – British Journal of Educational Technology, 2022
Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher-order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high-quality feedback from peer…
Descriptors: Artificial Intelligence, Learning Analytics, Peer Evaluation, Student Evaluation
Hongwen Guo; Matthew S. Johnson; Kadriye Ercikan; Luis Saldivia; Michelle Worthington – Journal of Learning Analytics, 2024
Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough--educators need to know why students got low scores, how students engaged…
Descriptors: Artificial Intelligence, Learning Analytics, Learning Management Systems, Measurement
Kasra Lekan; Zachary A. Pardos – Journal of Learning Analytics, 2025
Choosing an undergraduate major is an important decision that impacts academic and career outcomes. In this work, we investigate augmenting personalized human advising for major selection using a large language model (LLM), GPT-4. Through a three-phase survey, we compare GPT suggestions and responses for undeclared first- and second-year students…
Descriptors: Technology Uses in Education, Artificial Intelligence, Academic Advising, Majors (Students)
Xiaona Xia; Wanxue Qi – European Journal of Education, 2025
Massive Open Online Courses (MOOCs) effectively support online learning behaviour; while constructing a sustainable learning process, MOOCs have also formed the social network. In addition, learners' burnout state has become a serious obstacle to the development and promotion of MOOCs. This study analyzes the potential social behaviour associated…
Descriptors: MOOCs, Burnout, Social Behavior, Feedback (Response)
Stanislav Pozdniakov; Jonathan Brazil; Mehrnoush Mohammadi; Mollie Dollinger; Shazia Sadiq; Hassan Khosravi – Journal of Learning Analytics, 2025
Engaging students in creating high-quality novel content, such as educational resources, promotes deep and higher-order learning. However, students often lack the necessary training or knowledge to produce such content. To address this gap, this paper explores the potential of incorporating generative AI (GenAI) to review students' work and…
Descriptors: Student Evaluation, Artificial Intelligence, Student Developed Materials, Feedback (Response)
Wongvorachan, Tarid; Lai, Ka Wing; Bulut, Okan; Tsai, Yi-Shan; Chen, Guanliang – Journal of Applied Testing Technology, 2022
Feedback is a crucial component of student learning. As advancements in technology have enabled the adoption of digital learning environments with assessment capabilities, the frequency, delivery format, and timeliness of feedback derived from educational assessments have also changed progressively. Advanced technologies powered by Artificial…
Descriptors: Artificial Intelligence, Feedback (Response), Learning Analytics, Natural Language Processing
Nesra Yannier; Scott E. Hudson; Henry Chang; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2024
Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a…
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education
Ling Wang; Shen Zhan – Education Research and Perspectives, 2024
Generative Artificial Intelligence (GenAI) is transforming education, with assessment design emerging as a crucial area of innovation, particularly in computer science (CS) education. Effective assessment is critical for evaluating student competencies and guiding learning processes, yet traditional practices face significant challenges in CS…
Descriptors: Artificial Intelligence, Computer Science Education, Technology Uses in Education, Student Evaluation
Cleon Xavier; Luiz Rodrigues; Newarney Costa; Rodrigues Neto; Gabriel Alves; Taciana Pontual Falcao; Dragan Gasevic; Rafael Ferreira Mello – IEEE Transactions on Learning Technologies, 2025
Providing timely and personalized feedback on open-ended student responses is a challenge in education due to the increased workloads and time constraints educators face. While existing research has explored how learning analytic approaches can support feedback provision, previous studies have not sufficiently investigated educators' perspectives…
Descriptors: Teacher Empowerment, Learning Analytics, Artificial Intelligence, Computer Software
Diana Šimic; Barbara Šlibar; Jelena Gusic Mundar; Sabina Rako – Technology, Knowledge and Learning, 2025
Researchers and practitioners from different disciplines (e.g., educational science, computer science, statistics) continuously enter the rapidly developing research field of learning analytics (LA) and bring along different perspectives and experiences in research design and methodology. Scientific communities share common problems, concepts,…
Descriptors: Learning Analytics, Higher Education, Science Education, Publications
Okan Bulut; Tarid Wongvorachan – OTESSA Conference Proceedings, 2022
Feedback is an essential part of the educational assessment that improves student learning. As education changes with the advancement of technology, educational assessment has also adapted to the advent of Artificial Intelligence (AI). Despite the increasing use of online assessments during the last decade, a limited number of studies have…
Descriptors: Feedback (Response), Artificial Intelligence, Technology Uses in Education, Natural Language Processing

Peer reviewed
Direct link
