Angxuan Chen

Background: Generative AI is rapidly being integrated into educational settings, yet equitable benefits depend heavily on students' GenAI literacy. While access to these tools is increasing, empirical evidence remains limited on how specific dimensions of assessed GenAI literacy relate to students' task performance, cognitive experiences, and actual interactional behaviours during learning. Objectives: This study examined the associations of overall GenAI literacy and its four dimensions (Know and Understand, Use and Apply, Evaluate and Create, Ethics) with students' abstract‐writing performance, self‐efficacy, motivation, and cognitive load. It also explored how assessed literacy is reflected in the configurations of students' interactions with GenAI. Methods: A total of 162 university students completed an abstract‐writing task using a custom GenAI assistant. Ordinary least‐squares regression models evaluated the continuous GenAI literacy scores against writing performance, motivation, and cognitive load, adjusting for prior academic writing knowledge and English proficiency. Additionally, Epistemic Network Analysis (ENA) was applied to conversational trace data to compare the configurations of questioning and commanding behaviours between higher‐ and lower‐literacy students. Results and Conclusions: Higher overall GenAI literacy was positively associated with better writing performance and negatively associated with perceived mental load. Dimension‐level analyses revealed distinct roles: the Use and Apply dimension was independently associated with higher writing performance, while the Know and Understand dimension was uniquely associated with lower mental load. Neither the total score nor the sub‐dimensions showed reliable associations with motivation or self‐efficacy. Furthermore, ENA profiles demonstrated that higher‐literacy students engaged in a refinement‐oriented process, whereas lower‐literacy students relied predominantly on direct content generation and factual retrieval. These findings suggest that instruction should address both conceptual understanding and practical application of GenAI, helping students critically evaluate and refine AI‐generated content during writing.

Background: Language assessment plays a pivotal role in language education, serving as a bridge between students' understanding and educators' instructional approaches. Recently, advancements in Artificial Intelligence (AI) technologies have introduced transformative possibilities for automating and personalising language assessments.Objectives: This article aims to explore the design and implementation of AI‐enabled assessment tools in language education, filling the research gaps regarding the impact of assessment type, intervention duration, education level, and first language learner/second language learner (L1/L2) on the effectiveness of AI‐enabled assessment tools in enhancing students' language learning outcome.Methods: This study conducted a systematic review and meta‐analysis to examine 25 empirical studies from January 2012 to March 2024 from six databases (including EBSCO, ProQuest, Scopus, Web of Science, ACM Digital Library and CNKI).Results: The predominant design in AI‐driven assessment tools is the structural AI architecture. These tools are most frequently deployed in classroom settings for upper primary students within a short duration. A subsequent meta‐analysis showed a medium overall effect size (Hedges's g = 0.390, p < 0.001) for the application of AI‐enabled assessment tools in enhancing students' language learning, underscoring their significant impact on language learning outcomes. This evidence robustly supports the practical utility of these tools in educational contexts.Conclusions: The analysis of several moderator variables (i.e., assessment type, intervention duration, educational level and L1/L2 learners) and potential impacts on language learning performance indicates that AI‐enabled assessment could be more useful in language education with a proper implementation design. Future research could investigate diverse instructional designs for integrating AI‐based assessment tools in language education.

Role-play activities are considered a useful instructional design in enhancing the speaking performance of foreign language learners. However, in the traditional classroom context, learners may not readily have access to interlocutors for role-play activities. In this study, we proposed a designed method that integrated the GenAI agent into role-play activities and conducted a quasi-experiment at a Chinese university. A total of 53 Chinese students in an English course were assigned to an experimental group ( n = 29) that engaged in role-playing with a GenAI agent and a control group ( n = 24) that engaged in role-playing with their class peers. The results showed role-play activities with GenAI could facilitate students’ speaking performance, and one-way ANCOVA showed there was no significant difference in speaking performance improvement when compared with traditional role-play activities with peers. Moreover, the paired-sample Wilcoxon test showed that the treatment students had significant intrinsic motivation and self-efficacy improvement, which were higher than the control group. The findings provide important implications for the design and implementation of GenAI-integrated role-play activities for foreign language speaking learning.

Student-athletes frequently struggle to strike a balance between their academic and athletic responsibilities. Various factors, such as age and competitive level, contribute to differences in their academic motivation and identity, showcasing the multifaceted needs they possess. While self-determination theory (SDT) has been proven effective for explaining student-athletes academic needs, its integration into learning design for this group remains limited. The developing AI technology, especially the Intelligent Tutoring System (ITS), offers the potential for creating personalized learning environments that can cater to the varying levels of motivation among student-athletes within the framework of SDT. Therefore, our paper explored how to build an SDT-based ITS for student-athletes to enhance their academic engagement and motivation. A two-stage experiment was conducted for: (a) identifying academic challenges faced by student-athletes in an online ITS; (b) evaluating the effectiveness of an SDT-based ITS design; and (c) exploring how autonomy, competence, and relatedness design affect their motivation. Results revealed that student-athletes face three challenges in learning in ITS: inflexible technology, identity missing, and mismatched learning difficulty. However, a significant improvement in academic engagement and motivation was shown when student-athletes faced an SDT-based ITS. In the meantime, the athletic motivation, which leads them to higher athletic performance, remains preserved and unaffected, showing a favorable outcome for student-athletes. This paper can provide practical implications for building a more inclusive and diverse learning environment for student-athletes.