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Elvis Ortega-Ochoa; Marta Arguedas; Thanasis Daradoumis – British Journal of Educational Technology, 2024
Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline…
Descriptors: Empathy, Artificial Intelligence, Databases, Dialogs (Language)
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Xiaomei Wang – Education and Information Technologies, 2025
Automated writing evaluation (AWE) provides an instant and cost-effective alternative to human feedback in assessing student writing, and therefore is widely used as a pedagogical supportive tool in writing instruction. However, studies on how students perceive the usage of AWE as a surrogate writing tutor in out-of-class autonomous learning are…
Descriptors: Student Attitudes, Automation, Writing Evaluation, Undergraduate Students
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A. N. Varnavsky – IEEE Transactions on Learning Technologies, 2024
The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The…
Descriptors: Hierarchical Linear Modeling, Intelligent Tutoring Systems, Individualized Instruction, Electronic Learning
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Valentina Grion; Juliana Raffaghelli; Beatrice Doria; Anna Serbati – Educational Research and Evaluation, 2024
Feedback is crucial for improving student learning. In this regard, overcoming the transmissive conception of feedback in favour of its dialogic function introduces new reflections concerning the internal generative feedback process. In this regard, Nicol [(2020). The power of internal feedback: Exploiting natural comparator processes.…
Descriptors: Student Attitudes, Self Evaluation (Individuals), Feedback (Response), Individual Differences
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Arthur William Fodouop Kouam – Discover Education, 2024
This study investigates the effectiveness of Intelligent Tutoring Systems (ITS) in supporting students with varying levels of programming experience. Through a mixed-methods research design, the study explores the impact of ITS on student performance, adaptability to different skill levels, and best practices for utilizing ITS in heterogeneous…
Descriptors: Intelligent Tutoring Systems, Instructional Effectiveness, Programming, Skill Development
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Lodder, Josje; Heeren, Bastiaan; Jeuring, Johan; Neijenhuis, Wendy – International Journal of Artificial Intelligence in Education, 2021
This paper describes LOGAX, an interactive tutoring tool that gives hints and feedback to a student who stepwise constructs a Hilbert-style axiomatic proof in propositional logic. LOGAX generates proofs to calculate hints and feedback. We compare these generated proofs with expert proofs and student solutions, and conclude that the quality of the…
Descriptors: Intelligent Tutoring Systems, Cues, Feedback (Response), Mathematical Logic
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Conrad Borchers; Cindy Peng; Qianru Lyu; Paulo F. Carvalho; Kenneth R. Koedinger; Vincent Aleven – Grantee Submission, 2025
Many AIED systems support self-regulated learning, yet, support for setting and achieving practice goals has received little attention. We examine how middle school students respond to system-recommended practice goals, building on the success of similar data-driven recommendations in other domains. We introduce an adaptive dashboard in an…
Descriptors: Goal Orientation, Student Attitudes, Self Control, Intelligent Tutoring Systems
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Kang, Jiwon; Kang, Chaewon; Yoon, Jeewoo; Ji, Houggeun; Li, Taihu; Moon, Hyunmi; Ko, Minsam; Han, Jinyoung – Education and Information Technologies, 2023
Recent technologies have extended opportunities for online dance learning by overcoming the limitations of space and time. However, dance teachers report that student-teacher interaction is more likely to be challenging in a distant and asynchronous learning environment than in a conventional dance class, such as a dance studio. To address this…
Descriptors: Educational Technology, Online Courses, Dance Education, Artificial Intelligence
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Daocheng Hong – Interactive Learning Environments, 2024
The digital transformation of education is greatly accelerating in various computer-supported applications. As a particularly prominent application of the human-machine interactive system, intelligent learning systems aim to capture users' current intentions and provide recommendations through real-time feedback. However, we have a limited…
Descriptors: Feedback (Response), Users (Information), Learner Engagement, Tests
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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
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Luiz Rodrigues; Guilherme Guerino; Thomaz E. V. Silva; Geiser C. Challco; Lívia Oliveira; Rodolfo S. da Penha; Rafael F. Melo; Thales Vieira; Marcelo Marinho; Valmir Macario; Ig I. Bittencourt; Diego Dermeval; Seiji Isotani – International Journal of Artificial Intelligence in Education, 2025
Intelligent Tutoring Systems (ITS) possess significant potential to enhance learning outcomes. However, deploying ITSs in global south countries presents challenges due to their frequent lack of essential technological resources, such as computers and internet access. The concept of AIED Unplugged has emerged to bridge this digital divide,…
Descriptors: Teacher Attitudes, Intelligent Tutoring Systems, Numeracy, Mathematics Education
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Conrad Borchers; Tianze Shou – Grantee Submission, 2025
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…
Descriptors: Benchmarking, Computational Linguistics, Artificial Intelligence, Computer Software
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Sam Ford; Mohamed Allali – International Journal of Mathematical Education in Science and Technology, 2023
Studies across a variety of educational fields have shown the efficacy of feedback on student performance and learning. Web-based homework is a common feature of secondary and collegiate mathematics courses to provide such feedback. While web-based homework provides often instantaneous feedback to students as they complete assignments, the…
Descriptors: Intelligent Tutoring Systems, Feedback (Response), Equations (Mathematics), Homework
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Dever, Daryn A.; Sonnenfeld, Nathan A.; Wiedbusch, Megan D.; Schmorrow, S. Grace; Amon, Mary Jean; Azevedo, Roger – Metacognition and Learning, 2023
Self-regulated learning (SRL), learners' monitoring and control of cognitive, affective, metacognitive, and motivational processes, is essential for learning. However, cognitive and metacognitive SRL strategies are not typically used accurately leading to poor learning outcomes. Intelligent tutoring systems (ITSs) attempt to address this issue by…
Descriptors: Independent Study, Artificial Intelligence, Systems Approach, Intelligent Tutoring Systems
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Matsuda, Noboru – International Journal of Artificial Intelligence in Education, 2022
This paper demonstrates that a teachable agent (TA) can play a dual role in an online learning environment (OLE) for learning by teaching--the teachable agent working as a synthetic peer for students to learn by teaching and as an interactive tool for cognitive task analysis when authoring an OLE for learning by teaching. We have developed an OLE…
Descriptors: Artificial Intelligence, Teaching Methods, Intelligent Tutoring Systems, Feedback (Response)
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