Publication Date
| In 2026 | 0 |
| Since 2025 | 7 |
| Since 2022 (last 5 years) | 12 |
| Since 2017 (last 10 years) | 28 |
| Since 2007 (last 20 years) | 46 |
Descriptor
| Feedback (Response) | 48 |
| Intelligent Tutoring Systems | 48 |
| Natural Language Processing | 48 |
| Artificial Intelligence | 18 |
| Educational Technology | 14 |
| Essays | 14 |
| Automation | 12 |
| Technology Uses in Education | 12 |
| Models | 11 |
| Reading Comprehension | 11 |
| Prediction | 10 |
| More ▼ | |
Source
Author
Publication Type
| Reports - Research | 28 |
| Journal Articles | 20 |
| Speeches/Meeting Papers | 19 |
| Reports - Evaluative | 8 |
| Collected Works - Proceedings | 6 |
| Reports - Descriptive | 5 |
| Books | 1 |
| Opinion Papers | 1 |
Education Level
Audience
Location
| Brazil | 2 |
| Arizona (Phoenix) | 1 |
| Australia | 1 |
| California | 1 |
| China | 1 |
| Czech Republic | 1 |
| Finland | 1 |
| France | 1 |
| Indonesia | 1 |
| Israel | 1 |
| Massachusetts | 1 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
| Massachusetts Comprehensive… | 1 |
| Motivated Strategies for… | 1 |
| Program for International… | 1 |
| Writing Apprehension Test | 1 |
What Works Clearinghouse Rating
Smitha S. Kumar; Michael A. Lones; Manuel Maarek; Hind Zantout – ACM Transactions on Computing Education, 2025
Programming demands a variety of cognitive skills, and mastering these competencies is essential for success in computer science education. The importance of formative feedback is well acknowledged in programming education, and thus, a diverse range of techniques has been proposed to generate and enhance formative feedback for programming…
Descriptors: Automation, Computer Science Education, Programming, Feedback (Response)
Jionghao Lin; Zifei Han; Danielle R. Thomas; Ashish Gurung; Shivang Gupta; Vincent Aleven; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2025
One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can…
Descriptors: Artificial Intelligence, Technology Uses in Education, Tutor Training, Trainees
Hyeongdon Moon; Richard Lee Davis; Seyed Parsa Neshaei; Pierre Dillenbourg – International Educational Data Mining Society, 2025
Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with…
Descriptors: Artificial Intelligence, Natural Language Processing, Automation, Information Management
Hao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
Olaperi Okuboyejo; Sigrid Ewert; Ian Sanders – ACM Transactions on Computing Education, 2025
Regular expressions (REs) are often taught to undergraduate computer science majors in the Formal Languages and Automata (FLA) course; they are widely used to implement different software functionalities such as search mechanisms and data validation in diverse fields. Despite their importance, the difficulty of REs has been asserted many times in…
Descriptors: Automation, Feedback (Response), Error Patterns, Error Correction
Tian Belawati; Dimas Prasetyo – Open Praxis, 2025
This paper presents the findings of a pilot study on the use of generative AI (GAI) in tutorial sessions within a large-scale distance education institution in Indonesia. The primary aim of the experiment was to assess the impact of GAI-based tutoring on student engagement and academic achievement. A secondary objective was to explore how GAI…
Descriptors: Artificial Intelligence, Technology Uses in Education, Distance Education, Foreign Countries
Kochmar, Ekaterina; Vu, Dung Do; Belfer, Robert; Gupta, Varun; Serban, Iulian Vlad; Pineau, Joelle – International Journal of Artificial Intelligence in Education, 2022
Intelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Automation, Feedback (Response), Dialogs (Language)
Caitlin Mills, Editor; Giora Alexandron, Editor; Davide Taibi, Editor; Giosuè Lo Bosco, Editor; Luc Paquette, Editor – International Educational Data Mining Society, 2025
The University of Palermo is proud to host the 18th International Conference on Educational Data Mining (EDM) in Palermo, Italy, from July 20 to July 23, 2025. EDM is the annual flagship conference of the International Educational Data Mining Society. This year's theme is "New Goals, New Measurements, New Incentives to Learn." The theme…
Descriptors: Artificial Intelligence, Data Analysis, Computer Science Education, Technology Uses in Education
Phung, Tung; Cambronero, José; Gulwani, Sumit; Kohn, Tobias; Majumdarm, Rupak; Singla, Adish; Soares, Gustavo – International Educational Data Mining Society, 2023
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming. More concretely, given a student's buggy program, our goal is…
Descriptors: Computational Linguistics, Feedback (Response), Programming, Computer Science Education
Panaite, Marilena; Ruseti, Stefan; Dascalu, Mihai; Balyan, Renu; McNamara, Danielle S.; Trausan-Matu, Stefan – Grantee Submission, 2019
Intelligence Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students' practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring…
Descriptors: Computer Assisted Testing, Scoring, Intelligent Tutoring Systems, Natural Language Processing
Jia, Qinjin; Young, Mitchell; Xiao, Yunkai; Cui, Jialin; Liu, Chengyuan; Rashid, Parvez; Gehringer, Edward – International Educational Data Mining Society, 2022
Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains,…
Descriptors: Student Projects, Feedback (Response), Natural Language Processing, Guidelines
Gillani, Nabeel; Eynon, Rebecca; Chiabaut, Catherine; Finkel, Kelsey – Educational Technology & Society, 2023
Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations--many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Educational Benefits
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
Pavlik, Philip I., Jr.; Olney, Andrew M.; Banker, Amanda; Eglington, Luke; Yarbro, Jeffrey – Grantee Submission, 2020
An intelligent textbook may be considered to be an interaction layer that lies between the text and the student, helping the student to master the content in the text. The Mobile Fact and Concept Training System (MoFaCTS) is an adaptive instructional system for simple content that has been developed into an interaction layer to mediate textbook…
Descriptors: Textbooks, Intelligent Tutoring Systems, Electronic Learning, Instructional Design
Rojano, Teresa; García-Campos, Montserrat – Teaching Mathematics and Its Applications, 2017
This article reports the outcomes of a study that seeks to investigate the role of feedback, by way of an intelligent support system in natural language, in parametrized modelling activities carried out by a group of tertiary education students. With such a system, it is possible to simultaneously display on a computer screen a dialogue window and…
Descriptors: Mathematics Instruction, Feedback (Response), Intelligent Tutoring Systems, College Students

Peer reviewed
Direct link
