Agentic AI holds promise for usability testing, yet its role as an audio moderator in think-aloud protocols is not well understood. This study explores: (1) how to design and develop an agentic audio moderator for think-aloud usability testing, and (2) how participants moderated by an agentic moderator differ from those moderated by a human regarding task performance, verbalization behaviors, user experience, and social perceptions of the moderator. Using a design-based research approach, we interviewed nine UX experts, iteratively developed an AI moderator, and evaluated it in a randomized controlled trial (N = 60) with a note-taking application. Results suggest that significant differences were not observed between AI and human moderators in task performance or verbalization behaviors, though AI moderators received lower social perception ratings. This work contributes the first design-oriented evaluation of AI moderators in usability testing, offering implications for developing more acceptable and effective agentic audio moderators.

The N-back task is widely recognized as a paradigm for cognitive training due to its adaptability and effectiveness in mitigating working memory decline. However, its repetitive and monotonous design often leads to reduced engagement and poor long-term adherence, lowering intervention effectiveness. To address these limitations, researchers have turned to game-based interventions and immersive technologies to enhance user motivation and sustain training participation. In this study, we propose VRN-back, a music rhythm-based gamified cognitive training system in virtual reality (VR). The system combines the adaptive N-back paradigm with rhythm-game mechanics to increase enjoyment and motivation, while immersive interaction fosters engagement and enhances training outcomes. We conducted a seven-day study with 15 young adults to evaluate the system's impact on their working memory, transfer to related cognitive domains, and user experience. Results showed significant improvements in N-back accuracy, reaction time, and maximum N level, as well as transfer effects on attention and inhibitory control tasks. Subjective evaluations indicated good usability and user experience, with the System Usability Scale scoring 80.83/100, the short User Experience Questionnaire rating pragmatic quality as "Good" and hedonic quality as "Above Average", and the Pleasure-Arousal-Dominance scale confirming sustained positive emotional states. Simulator sickness remained minimal before and after training, ensuring usability. These findings suggest the feasibility and effectiveness of immersive rhythm-based gamified VR systems for cognitive training, laying a foundation for future research on long-term cognitive intervention technologies.

Some online learning platforms frequently recommend educational materials to attract student engagement, with visual elements playing a critical role in capturing attention. To optimize the visual design of mathematical stories, this study examines the relationship between visual features and click frequency, based on log data from a U.S. platform featuring AI-generated mathematical stories for elementary students. Our methodology involves a multi-level visual feature extraction framework, categorizing features into low-, mid-, and high-level. Low-level features capture fundamental visual elements like color, texture, shape, and composition, commonly used for their simplicity. Mid-level features, inspired by psychological and artistic theories, more directly link to emotional impact, including attributes like brightness and contrast. High-level features focus on semantic content, using AI models to extract aesthetic scores and identify entities. Based on the correlation analysis between visual features and clicks, our findings indicate that images featuring characters and natural landscapes positively correlate with student interest, aligning with theories of situational interest. In contrast, images with pronounced brightness contrasts negatively impact engagement, likely due to increased cognitive load. The study highlights the limited influence of mid-level aesthetic features on elementary students' engagement, emphasizing the importance of visual clarity and educational relevance over purely aesthetic considerations.

Despite extensive research on code plagiarism detection in higher education and for programming languages like Java and Python, limited work has focused on K-12 settings, particularly for pseudocode. This study aims to address this gap by building explainable machine learning models for pseudocode plagiarism detection in online programming education. To achieve this, we construct a comprehensive dataset comprising 7,838 pseudocode submissions from 2,578 high school students enrolled in an online programming foundations course, along with 6,300 pseudocode samples generated by three versions of generative pre-trained transformer (GPT) models. Utilizing this dataset, we develop an explainable model to detect AI-generated pseudocode across various assessments. The model not only identifies AI-generated content but also provides insights into its predictions at both the student and problem levels, thus enhancing our understanding of AI-generated pseudocode in K-12 education. Furthermore, we analyzed SHAP values and key features of the model to pinpoint student submissions that closely resemble AI-generated pseudocode. This research offers implications for developing robust educational technologies and methodologies to uphold academic integrity in online programming courses.

The importance of discussing support for learning within online learning communities is widely recognized, yet the diverse user behaviors, especially in the realm of online math learning, are not thoroughly investigated. This work in progress research paper explores the mathematical literacies, and success rates of discussion learning among students taking on different participation roles in an asynchronous online math learning setting. This inquiry is pivotal for comprehending the mechanisms that uphold online learning communities and can provide insights for crafting online discussion activities. This paper employs a mixed-methods approach to analyze big educational data and core-periphery structures within the community. Initially, users are classified into core, periphery, and X-periphery (extra) groups using an extended Surprise detection algorithm, which evaluates interaction quality. The Mann-Whitney U test is applied to assess differences in math literacy and discussion success rates among the groups. The findings reveal that each group is more responsive to its own members, with the core group exhibiting a more balanced response pattern. Notably, the X-periphery group shows the highest success rate in discussions, suggesting that lower activity levels do not compromise communication efficiency. Statistical analysis indicates that while the core and periphery groups have similar levels of math literacy, the X-periphery group excels in all three types of mathematical literacy assessed. These results highlight the importance of considering group dynamics and participation roles when designing online math learning activities to foster effective communication and support within the community. The study's insights into social support traffic offer practical implications for practitioners aiming to enhance the sustainability of online learning communities through tailored discussion activities.

This study examines public sentiment towards AI in education, focusing on the impact of ChatGPT's launch by OpenAI on November 30, 2022. Analyzing around 80,000 Twitter posts from before and after the launch, we conducted a comprehensive sentiment analysis using a fine-tuned BERT, outperforming traditional methods such as VADER and SVM. We applied an RDD to assess the causal impacts of ChatGPT's introduction on public sentiment track sentiment shifts, highlighting how the introduction of AI technologies like ChatGPT has influenced educational discourse. Our findings reveal significant public sentiment changes post-launch, contributing new insights into AI's role in education and public discourse.

The significance of nurturing a deep conceptual understanding in math learning cannot be overstated. Grounded in the pedagogical strategies of induction, concretization, and exemplification (ICE), we designed and developed a conversational AI using both rule- and generation-based techniques to facilitate math learning. Serving as a preliminary step, this study employed an experimental design involving 151 U.S.-based college students to reveal students’ attention patterns, technology acceptance model, and qualitative feedback when using the developed ConvAI. Our findings suggest that participants in the ConvAI group generally exhibit higher attention levels than those in the control group, aside from the initial stage where the control group was more attentive. Meanwhile, participants appreciated their experience with the ConvAI, particularly valuing the ICE support features. Finally, qualitative analysis of participants’ feedback was conducted to inform future refinement and to inspire educational researchers and practitioners.

This research aims to investigate the allocation mechanisms of written positive affective feedback (PAF) in online mathematics assignments provided by teachers, employing multimodal learning analytics. We extract mathematical text features and readability indicators from teacher comments, utilizing collinearity matrices for linear feature selection. Student multimodal response patterns are obtained through clustering. To analyze the teacher comment strategies under different student response patterns, Mann-Whitney U tests were employed to investigate differences in student scores and feedback readability between scenarios with and without PAF. Our findings uncover the linguistic characteristics of teacher-provided PAF and the corresponding strategies they adopt. Teachers are more inclined to offer PAF to K-12 students with higher scores, challenging assignments, and younger ages. The study points out potential imbalances in the allocation of teacher PAF and emphasizes key factors that teachers need to consider when providing PAF. The findings offer new insights for educators to contemplate on designing and implementing more effective PAF strategies.