intelligent-tutoring-systems
Filtering by topic intelligent-tutoring-systems(2)Clear all filters
- PaperOpenAlex — TESOL research22 Jul 2026
ENHANCING FOREIGN LANGUAGE TEACHING METHODOLOGY THROUGH ARTIFICIAL INTELLIGENCE: A CASE STUDY OF ENGLISH LANGUAGE LEARNING
Farmonova Naimakhon Furkat kizi
A mixed-method study evaluated AI-based tools—intelligent tutoring systems, natural language processing applications, and adaptive learning platforms—for English language teaching in higher education. Results showed significant improvements in vocabulary acquisition, communicative competence, and learner autonomy, though challenges in teacher readiness, infrastructure, and ethics remain.
Original abstract
Artificial Intelligence (AI) is rapidly transforming educational practices, particularly in the field of foreign language teaching. This study explores how AI can enhance English language teaching methodology in higher education. Using a mixed-method research design, the study evaluates the impact of AI-based tools such as intelligent tutoring systems, natural language processing applications, and adaptive learning platforms on students’ language proficiency. The findings reveal that AI significantly improves vocabulary acquisition, communicative competence, and learner autonomy. However, challenges related to teacher readiness, infrastructure, and ethical considerations remain. The study contributes to the development of modern AI-based pedagogical frameworks.
- PaperEdArXiv (OSF Preprints)20 Jul 2026
Student Learning Rates When Practicing with an Intelligent Tutoring System Versus On Paper
Conrad Borchers, Qianru Lyu, Ritesh Kanchi, Kenneth R. Koedinger et al.
This study directly compared learning rates, measured as improvement per problem-solving step, between practice with an intelligent tutoring system (ITS) and paper-based practice among 97 middle-school students. ITS-supported practice yielded significantly higher overall learning gains and positive learning rates, while paper-based learning rates were near zero, especially for lower-proficiency students. The findings underscore the critical role of immediate feedback and adaptive support in enabling learning during practice.
Original abstract
Intelligent tutoring systems (ITSs) are widely used in K-12 education and are known to improve learning outcomes compared to traditional instruction. However, prior work has focused primarily on end-of-test performance, with little attention to learning processes, such as how much students learn at each practice opportunity. In particular, learning rates have been extensively studied in ITS environments but not for paper-based problem solving. We conducted a within-subjects classroom experiment with 97 middle-school students solving matched mathematics problems either with an ITS or on paper. We compared both overall learning gains and process-level learning rates, defined as improvement per problem-solving step. To enable this comparison, we introduce a novel method for converting paper-based work into step-level transaction data compatible with learning-curve modeling. Results show that ITS-supported practice led to significantly higher learning gains and substantially higher learning rates than paper-based practice. While students in the ITS condition exhibited consistent positive learning rates, learning rates in the paper condition were indistinguishable from zero, indicating little measurable learning during problem solving without feedback. These differences were especially pronounced for students with lower prior proficiency. This study provides the first direct comparison of learning rates between ITS and paper-based practice and introduces a generalizable methodology for analyzing learning processes in non-digital environments. The findings highlight the critical role of immediate feedback and adaptive support in enabling learning during practice and support the broader adoption of ITS in K-12 education.