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- PaperarXiv — AI in Education (cs.CY)21 Jul 2026
Assessment in Team Problem-Solving Exercises in Computing Education
Valdemar Švábenský, Jan Vykopal, Sukrit Leelaluk, Pavel Čeleda et al.
Compared two post-exercise team assessment methods—clustering and large language models (LLMs)—for tabletop exercises in computing education. Clustering grouped teams by task approach, enabling faster, targeted feedback with low computational cost. LLMs showed mixed accuracy against instructor scores, with GPT-5.2 performing better than GPT-4o.
Original abstract
This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace tasks and practice crisis responses, such as resolving cybersecurity incidents. While assessment is essential for determining how well teams achieve learning objectives, the complex, open-ended nature of TTXs often leads to delayed or incomplete feedback. TTX learning platforms can record teams' actions and communication; yet, leveraging these data to assess performance is underexplored. To address this gap, we compared two post-TTX team assessment methods -- clustering and large language models (LLMs) -- using an original dataset from 81 participants across two countries. We evaluated these methods against instructor-assigned scores based on standardized rubrics. Clustering grouped teams that approached TTX tasks similarly, enabling instructors to deliver faster, targeted feedback to teams within a cluster. This method was valid and reliable, with low computational requirements. LLMs used the standardized rubrics to assess teams' communication. While GPT-4o frequently disagreed with instructor scores, GPT-5.2 demonstrated considerably lower error. The researched methods have been integrated into INJECT, an open-source TTX learning platform, to support scalability and teaching practice. To encourage community adoption, we publicly share all datasets, software tools, and a full-fledged TTX scenario.
- 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 compared learning rates and gains between middle-school students practicing math problems with an intelligent tutoring system (ITS) versus on paper. Results showed significantly higher learning rates and gains with ITS, especially for lower-proficiency students, while paper-based practice showed little measurable learning. The findings emphasize the importance of immediate feedback and adaptive support in learning.
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 with 92 Chinese undergraduates compared GenAI-generated growth-oriented feedback to corrective feedback in L2 writing. Growth-oriented feedback enhanced students' growth mindset and maintained adaptive responses to mistakes, while corrective feedback led to a decline in adaptive responses. The change in growth mindset mediated effects on adaptive responses and writing performance.
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.
- PaperAssessing Writing14 Jul 2026
Development and evaluation of a student feedback agency scale
Jiaxian Ye, Lawrence Jun Zhang, Helen R. Dixon
Developed and validated a Student Feedback Agency Scale (SFAS) with 23 items across six components, using PCA and CFA on samples of Chinese postgraduate students. The scale shows good psychometric properties and invariance across gender, academic level, and discipline.
Original abstract
Feedback agency is a key concept in enhancing student writing performance. While growing attention has been paid to student feedback agency, existing research remains largely theoretical and qualitative. As a result, there is a lack of psychometrically supported instruments to measure this construct. To address this gap, the present two-phase study aimed to develop and evaluate a Student Feedback Agency Scale (SFAS), drawing on social cognitive theory. In the development stage, Principal Component Analysis (PCA) was conducted on a sample of 235 Chinese international postgraduate students. The results yielded a 23-item SFAS comprising six components: Action Taking, Goal Setting, Processing, Generating, Self-efficacy, and Seeking. Using an independent sample of 349 participants from the same population, Confirmatory Factor Analysis (CFA) was conducted in the evaluation stage. The results supported a good model fit (RMSEA = .055, IFI = .923, TLI = .908, and CFI = .922). Multi-group CFAs further confirmed the structural invariance across gender, academic level, and discipline. Overall, the findings provide psychometric evidence to support the interpretation and use of the SFAS scores to measure student agency in writing feedback processes. Based on these results, the factor structure and subscales of the SFAS are discussed, and implications are outlined.
- PaperAssessment & Evaluation in Higher Education8 Jul 2026
Artificial intelligence and feedback in university education: effectiveness and student perceptions
Valentina Grion, Beatrice Doria, Daniele Agostini, Giorgia Slaviero
A quasi-experimental study compared AI-generated feedback from two large language models (GPT-o4-mini and DeepSeek R1) with expert human feedback in a university project-based course. Results showed significant improvement in performance across all conditions, with no practical differences between AI and human feedback, suggesting that pedagogical design matters more than the source of feedback. Students' perceptions of mastery, emotions, and satisfaction were similarly high regardless of feedback source.
Original abstract
The integration of generative artificial intelligence (AI) into Higher Education has intensified debates about the role of technology in formative assessment. This study examines the effectiveness and practical comparability of AI-generated feedback in a project-based university course, comparing two large language models (GPT-o4-mini and DeepSeek R1) with feedback provided by an expert human teacher. Adopting a quasi-experimental design, 47 student groups (N = 238) were randomly assigned to one of three feedback conditions. Changes in project performance were analysed using non-parametric tests, robust models, and non-inferiority and equivalence analyses. Students’ perceptions were also assessed through a validated questionnaire (N = 200). Results showed significant improvement in project performance from pre- to post-feedback across all conditions (rrb = 0.77), with no significant differences between feedback sources. Equivalence analyses indicated practical comparability between GPT-o4-mini and teacher feedback, while DeepSeek R1 demonstrated non-inferiority. Students’ perceptions of mastery, emotions, and satisfaction were similarly high across conditions. Findings suggest that feedback effectiveness depends less on its source than on the pedagogical architecture in which it is embedded. When supported by strong assessment literacy and explicit criteria, AI-generated feedback can function as a credible component of formative assessment in higher education.
- PaperJournal of Second Language Writing22 Jun 2026
From comparison to calibration: Rethinking feedback authority, agency, and learning in human-AI blended feedback
Jessie S. Barrot
This paper rethinks feedback in human-AI blended contexts, moving from comparing human and AI feedback to calibrating their roles to enhance learner authority, agency, and learning outcomes.
- PaperAssessing Writing19 Jun 2026
When does GenAI feedback support learning? Trust calibration and verification in L2 writing assessment
Ruyang Li, Cheng Zhang, Hedi Ye
Investigates the conditions under which generative AI feedback facilitates learning in L2 writing assessment, emphasizing the roles of trust calibration and verification processes.
- PaperReCALL1 Dec 2025
Examining the effectiveness of integrating corpus-based and AI approaches for English speaking practice
Hsueh Chu Chen, Xiaona Zhou, Jing Xuan Tian
An online English speaking training approach integrating a spoken corpus, generative AI, and text-to-speech AI tools was developed and evaluated. Pre- and post-test results showed improvements in speaking performances, with participants using more complex sentences and reducing vowel errors. Participants had positive attitudes and affirmed the importance of integrating both tools for creating an interactive oral ability learning environment.
Original abstract
This study developed and evaluated an online English speaking training approach that integrates corpora and artificial intelligence (AI) tools. The training integrated a self-developed spoken corpus, generative AI tools, and text-to-speech AI tools. Pre- and post-test results identified improvements in participants’ speaking performances. Participants attempted to use more positive linguistic features (e.g. producing complex sentences more frequently) and avoid using negative linguistic features (e.g. reducing the number of vowel errors) after receiving the training. Participants showed positive attitudes towards this corpus-based and AI-integrated English oral ability learning approach and affirmed the importance of integrating both tools. The corpus helped raise participants’ awareness of features that influence speaking performance and offered prompt engineering and feedback-checking functions, while the generative AI tools provided useful feedback and tailor-made sample responses. Additionally, text-to-speech AI tools offered learners with tailor-made native speaker samples for imitation and helped learners learn pausing. Results also revealed that this approach helped create an interactive oral ability learning environment, and the combination of corpora and AI tools provided more accurate feedback for each subskill of speaking.