Tao He

While generative artificial intelligence tools have been increasingly integrated into language education to optimize learners' learning outcomes, the effects of ChatGPT on EFL learners' oral presentation performance, critical thinking, enjoyment, and motivation have not been fully explored. Although traditional AI chatbots have shown potential in language learning support, their reliance on fixed interaction paths and limited capacity for human‐like dialogue have constrained their effectiveness in cognitively demanding tasks such as oral presentation preparation. To address this gap, this study drew on Activity Theory (AT) as a theoretical lens to examine how generative AI (GenAI) mediates EFL oral presentation preparation in flipped classrooms. A quasi‐experiment was conducted to evaluate the effects of GenAI intervention. Two intact classes with 99 EFL students (without GenAI experiences) were randomly assigned to either an experimental ( n = 49) or control ( n = 50) group. The experimental group (EG) used both search engines and ChatGPT, while the control group (CG) only used search engines. The results demonstrated that students in EG exhibited significantly higher levels of critical thinking disposition (CTD) and enjoyment compared to those in CG. Notably, there were no significant differences in learning motivation between the two groups. Furthermore, students in EG received higher oral presentation scores. During the interviews, participants highlighted that ChatGPT helped offload cognitive demands related to information processing, allowing them to allocate more mental resources toward thinking more comprehensively and critically about the presentation topic. This study is among the first to propose and validate the use of GenAI to free cognitive capacity to address critical thinking challenges in the context of EFL oral presentations, while also enhancing learning enjoyment within a flipped classroom setting.

The rapid growth of massive online learning has intensified interest in self‐regulated learning (SRL) and socially shared regulation of learning (SSRL), yet empirical insights into their interplay in online collaborative learning (OCL) remain limited. This study employed a three‐layer analytical method combining cluster analysis, content analysis and complex network analysis to investigate how SSRL patterns evolve in relation to individual SRL profiles. Data from 60 undergraduates in a 16‐week course included over 16,000 trace entries (e.g. posts, replies, peer feedback) were collected and analysed. Results revealed that (1) three profiles of SRL were identified, based on learners' time investment, study regularity and help‐seeking behaviours; (2) groups with higher SSRL behavioural interaction displayed a more diverse and balanced role composition; and (3) distinct SSRL patterns emerged across SRL profiles over time. Individuals with high SRL profiles more frequently connected idea sharing with strategy using and process monitoring, while those with low SRL profiles relied on limited strategies focused mainly on idea sharing. These findings deepen the understanding of how individual regulation shapes group dynamics in online collaboration and suggest that instructional design should consider learners' SRL profiles when scaffolding their collaborative regulation processes. Practitioner Notes: What is already known about this topic: Self‐regulated learning (SRL) and socially shared regulation of learning (SSRL) are both essential for effective online collaborative learning (OCL), with prior research recognising their theoretical interplay. Trace data (e.g. login frequency, timestamp and posts) offer valuable opportunities to uncover how learners engage in and coordinate regulatory behaviours throughout online collaboration. What this paper adds: Integrating SRL profiling with mixed network analysis, this study shows how individual SRL profiles drive divergent SSRL development, highlighting shared coordination patterns and evolving interactive roles across phases. Groups with higher SSRL interaction exhibited more diverse and balanced role composition, with central roles (e.g. leaders, animators) facilitating stronger collaborative regulation processes. Time‐series epistemic network analysis (ENA) provides a novel analytical lens to track the evolution of SSRL patterns across collaborative phases, linking SRL profiles with group‐level regulatory dynamics. Implications for practice and/or policy: Tailoring support to SRL profiles allows educators to offer targeted scaffolds, using prompts and planning tools for low SRL learners, while encouraging high SRL learners to lead coordination and monitoring in SSRL. Role‐driven group design promotes collaboration structured by learners' SRL profiles.

With the rapid development of generative AI, large language models (LLMs) have been increasingly applied in education. Although LLMs demonstrate considerable potential in supporting student learning, conventional LLMs tend to provide direct answers without explicitly articulating their reasoning processes. This hinders students' ability to comprehend the underlying logic of problem-solving, thereby limiting opportunities for metacognitive engagement and the development of higher-order thinking skills. To address this issue, this study proposes a cognitive apprenticeship approach based on an explicit-thinking LLM that provides structured thinking support during LLM-based learning activities. By employing the modeling, coaching, and reflection mechanisms of cognitive apprenticeship, the approach guides students to internalize the LLM's reasoning processes, thereby fostering their higher-order thinking skills. A quasi-experimental design was adopted, involving 68 college students divided into experimental and control groups. Participants completed a writing-based learning task using either the explicit-thinking LLM or a conventional LLM, and the data were analyzed using a mixed-methods approach that included eye-tracking. The results showed that, compared to the control group, the experimental group (1) exhibited enhanced metacognitive regulation, (2) demonstrated superior performance in both problem-solving and critical thinking, and (3) achieved better learning outcomes when they devoted greater attention to the reasoning process generated by the explicit-thinking LLM. This study suggests that the explicit-thinking LLM enhances students' learning performance and higher-order thinking by making reasoning processes more transparent. It further emphasizes the essential role of cultivating students' critical and creative engagement with AI to effectively support the development of higher-order thinking skills.

As generative artificial intelligence (GenAI) rapidly evolves, K-12 education is introducing AI courses whose interdisciplinary complexity exposes teachers' limited readiness. Research suggests that teachers can harness GenAI tools not only to enhance instruction but also to scaffold reflection that drives further teaching improvement. Therefore, this exploratory quasi-experimental study developed a customized GenAI agent system to support teachers' reflection on AI-course teaching and to enhance reflection self-efficacy, instructional design, and reflective thinking. A total of sixty in-service teachers were recruited and divided into two experimental groups, the self-reflection group (SRG) and the peer-reflection group (PRG), both supported by the customized GenAI agent system, and one control group (CG) with conventional technology-based reflection for a four-week AI course reflective practice experiment. The results revealed that the GenAI-agent-supported reflection approach could significantly promote teachers' self-reflection efficacy of two experimental groups compared with CG teachers. However, no significant differences between the SRG and PRG teachers' self-reflections efficacy could be found. Moreover, SRG and PRG teachers also outperformed the CG teachers on mutual-reflection self-efficacy. Furthermore, GenAI approach could significantly boost SRG and PRG teachers' instructional design reflection on methods and behavior, however, no significant differences were observed in instructional objectives or content among the three groups. To further explore the effects of GenAI agent support, Epistemic Network Analysis was applied to examine the coded results of teachers' reflective journals. The findings indicated that SRG and PRG teachers demonstrated broader and higher-order reflective thinking, integrating more dialogic and critical elements, whereas CG networks were predominantly descriptive. Overall, the study confirms that a customized GenAI agent can effectively deepen reflective thinking and practice, offering new insights into fostering teachers' professional development within K-12 AI education.

Knowledge tracing is a foundational task in intelligent education, aiming to predict students’ future performance by modeling their historical interactions. Traditional knowledge tracing methods primarily focus on analyzing students’ learning behaviors, often neglecting their dynamically changing emotional states during the learning process. Given that emotion is an integral part of the cognitive process, neglecting emotional states will limit the representation capacity of knowledge tracing models for modeling students’ learning processes. To address this issue, we propose a novel emotion-aware knowledge tracing method to explore the impact of complex emotions (e.g., concentration, frustration, boredom, and confusion) on knowledge states throughout the learning process. Specifically, we design an emotion-aware fusion module to capture the joint influence of multiple coexisting emotional states on students’ answers during each exercise session. Additionally, we utilize graph convolutional networks to propagate embeddings and obtain the complete knowledge structure information of exercises. We further integrate exercise attributes and students’ emotional information through a feature fusion module, thereby incorporating emotional states into the knowledge tracing task. Finally, we apply a transformer-based knowledge evolution module to model students’ evolving knowledge states, achieving multiview fusion modeling of emotions, knowledge topology, and learning sequences. Extensive experiments demonstrate that our method outperforms previous knowledge tracing methods in predicting student performance.