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- 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.
Intelligent tutoring systems (ITS) led to higher learning gains and learning rates compared to paper-based practice in a middle school math experiment. The study introduced a method to convert paper work into step-level data for comparison, finding that immediate feedback and adaptive support are critical for 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.
- PaperJournal of Second Language Writing17 Jul 2026
Can GenAI deliver growth-oriented feedback? Evidence for enhancing learners’ growth mindset and adaptive motivation in second language writing
Yuan Yao, Mi Rong, Nigel Mantou Lou
A field experiment tested whether generative AI can deliver growth-oriented feedback to L2 English writers. Growth-oriented feedback enhanced students' growth mindset and maintained adaptive responses to mistakes, while corrective feedback reduced adaptive responses. The findings suggest GenAI feedback in L2 writing can foster motivation and development beyond error correction.
Original abstract
Drawing on the mindsets theory, this study explores the use of generative artificial intelligence (GenAI) in producing growth-oriented feedback (i.e., feedback that aligns with the principles of growth mindset) and investigates its impact on students’ growth mindset, adaptive responses, and second language (L2) English writing performance. This field experiment was conducted at a university in China, involving 92 first- and second-year undergraduate students ( M age = 18.76, SD =.882; 80.4% males and 19.6% females). The participants were randomly assigned to an experimental group ( n = 49) or a control group ( n = 43) and completed pre- and post-questionnaires. Over the course of a semester, the participants completed three argumentative writing tasks. After each task, the experimental group received GenAI-generated growth-oriented feedback, whereas the control group received GenAI-generated corrective feedback. The results showed that growth-oriented feedback significantly enhanced students’ growth mindset and maintained their adaptive responses to mistakes at a relatively high level. In contrast, GenAI-generated corrective feedback led to a decline in adaptive responses to mistakes. Moreover, the change in growth mindset mediated the effect of feedback types on adaptive responses and writing performance. This study offers insights into the effectiveness of GenAI in generating growth-oriented feedback, highlighting its potential of GenAI feedback in L2 writing to go beyond error correction and foster motivational, behavioral, and academic development.
- PaperRELC Journal9 Jul 2026
Using dynamic feedback from customized LLM-powered chatbots in EFL argumentative writing
Yi Chen
An innovative teaching practice used two custom-built LLM-powered chatbots to provide dynamic, instruction-aligned feedback on argumentation quality and linguistic accuracy in an EFL argumentative writing course, supporting iterative multi-draft revision while keeping instructors in the loop to address automated feedback limitations.
Original abstract
With the growing use of large language models (LLMs) in English as a foreign language (EFL) teaching, balancing teacher control over instructional focus with students’ autonomy in using artificial intelligence chatbots while ensuring more dynamic interaction have become key pedagogical concerns. This article reports on an innovative teaching practice implemented in a foundation undergraduate English for Academic Purposes course, which featured interactive feedback, including chatbot-mediated dynamic assessment, delivered through two custom-built LLM-powered chatbots. The intervention aimed to support students’ argumentative writing with specific reference to argumentation quality and linguistic accuracy, through an iterative multi-draft process. Drawing on observation and critical reflection on this practicum, the article argues that customized LLM-powered chatbots can provide dynamic, instruction-aligned feedback at scale to scaffold EFL learners’ writing process, while instructors remain essential in the loop to address the limitations of automated feedback. The article concludes with suggestions for refining this approach in future practice.
- PaperComputer Assisted Language Learning4 May 2026
Feedback timing and engagement with feedback. Effects on L2 written accuracy
Florentina Nicolás-Conesa, Lourdes Cerezo, Sophie McBride
Investigates how timing of feedback and learners' engagement with it affect second language written accuracy.
- PaperERIC — Assessment & second language1 Jan 2025
Feedforwarding Diagnostic Language Assessment: Artificial Intelligence- (AI-) Driven Weakness Identification and Contextualised Feedback for Second Language Speaking
Shungo Suzuki, Hiroaki Takatsu, Ryuki Matsuura, Miina Koyama et al.
An AI-driven diagnostic language assessment program was developed that identifies lexical weaknesses and provides contextualised feedback for second language speaking. An experiment with Japanese English learners showed that the group receiving diagnostic feedback outperformed the control group in posttest and showed better retention, despite the control group improving during task repetition alone.
Original abstract
The current study proposes a new approach to weakness identification in diagnostic language assessment (DLA) for speaking skills. We also propose to design actionable and contextualised diagnostic feedback through the systematic integration of feedback and remedial learning activities. Focusing on lexical use in second language speaking, the current study developed and validated our DLA programme in terms of actual learning gains, using an experimental design. A total of 59 beginner-to-intermediate-level Japanese learners of English were randomly assigned to control or experimental groups. While both groups engaged in task repetition with a conversational artificial intelligence (AI) agent on six occasions, only the experimental group received the diagnostic feedback on lexical use including the paraphrased utterances of their original utterance. The results showed that the control group (task repetition only) demonstrated significant improvement during the task repetition sessions but failed to transfer and retain the learning gains. In contrast, despite the lack of practice effects, the experimental group (task repetition with diagnostic feedback) outperformed the control group at the posttest with a near-medium effect size. A qualitative investigation into learners' perceptions further confirmed that the proposed contextualised diagnostic feedback succeeded in heightening their awareness of weaknesses.