This study examines how generative AI (GAI) impacts primary students’ in-depth learning, focusing on critical thinking and prior knowledge. A quasi-experiment involved 163 sixth-graders divided into three groups: a control group (lecture-based instruction) and two experimental groups using GAI as a cognitive tool (materials generation) or thinking tool (critical analysis), in which 126 participants successfully completed all the tests and were included in the analysis. ANOVA revealed the thinking-tool group and cognitive-tool group both outperformed the control group in in-depth learning, which was reflected by the knowledge transfer. Hierarchical regression showed students’ critical thinking skills and use of generative artificial intelligence significantly contributed to their in-depth learning, while prior knowledge did not. Further analysis found that significant interaction effects existed between the use of generative artificial intelligence and critical thinking skills, while no significant interaction was found between the use of generative artificial intelligence and students’ prior knowledge. In sum, critical thinking amplified GAI’s impact, while prior knowledge showed no interaction. The results suggest GAI enhances deep learning when integrated with critical thinking, reducing reliance on prior knowledge. Educators should prioritize fostering critical thinking to maximize GAI’s benefits. The findings underscore the need for pedagogical designs that balance GAI’s cognitive support with metacognitive skill development.

Despite the continuous emphasis on emotion in multimedia learning, it was still unclear how pedagogical agent emotional cues might affect learning. In the present study, a between-subjects experiment was performed to examine the effects of a pedagogical agent with dual-channel emotional cues on learners' emotions, cognitive load, and knowledge transfer performance. Participants from a central Chinese university (age mean = 21.26, N = 66) were randomly divided into three groups. These groups received instructions from an affective pedagogical agent, a neutral pedagogical agent, or a neutral voice narration without pedagogical agent embodiment. Results showed that learners assigned the affective pedagogical agent reported a significantly higher emotional level than learners assigned the neutral pedagogical agent. Learners’ perceived task difficulty was not significantly different among groups while instructional efficiency was significantly higher for learners with the affective pedagogical agent. Moreover, learners assigned to the affective pedagogical agent performed significantly better on the knowledge transfer test than those assigned the neutral pedagogical agent or the neutral voice.

Background: As a fundamental approach to fostering students’ scientific literacy, argumentation has received more and more attention from science researchers and educators. Preservice science teachers’ (PSTs) abilities to both construct and evaluate arguments are fundamental to their future science teaching. Research combining these two aspects of PSTs’ argumentation abilities is lacking. Purpose: This study aims to investigate PSTs’ argumentation ability from both construction and evaluation perspectives and to explore their relationship. Sample: A total of 76 first-year graduates enrolling in a postgraduate-level science teachers preparation program at a university in China participated in this study, and 69 valid responses were obtained. Design and methods: This study employed the Chinese version of the Argument Evaluation Test (AET) translated from Martin-Gamez and Erduran (2018) to assess the participants’ abilities to evaluate arguments, and the Argument Construction Test (ACT) designed following Toulmin’s Argument Pattern (TAP) to assess the participants’ abilities to construct arguments. The participants completed the tests via an online questionnaire system. Results: The findings show: (1) Many limitations concerning PSTs’ abilities to evaluate arguments were visible. They were more incompetent in identifying ‘what is a good rebuttal’ than in identifying ‘what is a good argument’. (2) PSTs had obvious deficiencies in constructing arguments. Their performance in the dimensions of data, rebuttal and backing was significantly lower than their performance in the dimesion of warrant. (3) PSTs performed significantly better in a socio-technological issue (STI) than in a socio-scientific issue (SSI) in the dimension of evidence, and significantly better in an SSI than in a social issue (SI) in the dimension of the warrant. (4) A significant and moderate correlation was found between PSTs’ abilities to evaluate arguments and their abilities to construct arguments. Conclusions: This study suggests that first-year graduates (also postgraduate-level PSTs) are not well-prepared for teaching argumentation. There are a high necessity and urgency to offer systematic courses focusing on argumentation skills in PSTs programs. The ability to evaluate arguments and ability to construct rebuttals need to be highlighted in such courses. More attention needs to be paid to PSTs’ abilities to construct arguments with data and evidence rather than their abilities to simply propose claims and to show their warrants.