This study examines how AI-based teachable agents with distinct personality traits influence middle school students’ emotions and mathematics learning. Grounded in the Big Five personality framework, six agents were developed: five designed to emphasize one of the Big Five traits and one without a personality emphasis. Students engaged in teaching the agents to solve mathematical problems. Guided by the Control-Value Theory of Achievement Emotions, students’ emotions were coded by valence (positive vs. negative) and activation (activating vs. deactivating) based on their conversations with the agents, while mathematics learning was assessed through coded applications of knowledge during interaction and a posttest. Results showed that extraversion-, openness-, and agreeableness-emphasis agents promoted positive activating emotions (e.g., enjoyment), whereas conscientiousness-emphasis agents were particularly effective in reducing both negative activating emotions (e.g., anxiety) and negative deactivating emotions (e.g., boredom). Emotions were further linked to learning outcomes: positive activating emotions positively predicted knowledge application and posttest performance, whereas negative deactivating emotions negatively predicted students’ knowledge application. These findings highlight the nuanced role of teachable agent personality in shaping students’ emotional experiences and provide design implications for developing teachable agents that effectively support affective and academic dimensions of mathematics learning.

Building on growing evidence of the effectiveness of teachable agents for learning, this study investigated their use of instructional, cognitive, and pastoral conversational strategies, three dimensions of support that learning theories (e.g., the Community of Inquiry framework and Self-Determination Theory) identify as interconnected and critical for student learning, to inform the design of pedagogical conversational agents. By analyzing over 8,000 conversations between teachable agents and students, we found that agents’ cognitive and instructional strategies strongly promoted students’ cognitive elaboration, whereas pastoral strategies were associated with surface-level cognitive engagement and higher affective engagement. Moreover, two-strategy combinations (e.g., cognitive + instructional, instructional + pastoral) were generally more effective for fostering cognitive, affective, and metacognitive engagement than any single strategy, while combining all three strategies often weakened effects. Integration of cognitive and pastoral strategies further enhanced procedural knowledge application, whereas instructional strategies supported conceptual knowledge. Overall, these results clarify the complementary yet distinct roles of instructional, cognitive, and pastoral strategies and provide insights for pedagogical agents to dynamically pair functional strategies to support student learning.

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 work-in-progress research paper aims to explore students' dropout behavior during video engagement in online learning platforms. As online learning becomes increasingly popular, analyzing how students engage with video content provides important insights into their learning behaviors. This study explores multiple factors influencing K-12 students' in-video dropout rates in online math education. We examined 34,666,481 log entries from Math Nation, covering 1313 videos and 14,251 students. Using survival analysis, we evaluated how 27 variables, including demographic details, video interaction behaviors, and video characteristics(e.g. length, category), affect in-video dropout. Our findings reveal that video length significantly predicts dropout, with each additional minute increasing the dropout rate by 1.26%. Videos with higher dropout rates often feature more frequent pauses, jumps, and rewatches. The study also highlights that the quality of video content, the creators of the videos, and how students interact with the videos are crucial factors affecting dropout rates. Further research is needed to determine the specific causes of video dropout.

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.

Mathematical stories have demonstrated the ability to bolster the motivation and interest of students in learning mathematics, thereby exerting a positive influence on their academic performance. However, due to a lack of adequate resources and the desire to engage students in the creation process, Generative Artificial Intelligence (GAI) is utilized to generate mathematical stories accompanied by images. This study presents a framework for automatic quality assessment to evaluate the coherence of multimodal text and images generated by GAI, as well as the appropriateness of the stories for different grade levels. The dataset comprises mathematical stories generated by GAI for grades 3, 4, and 5, obtained from an American online learning platform, each story consisting of titles, bodies, and image illustrations generated by GP4, images generated by DALL-E3. Initially, a method is devised based on CLIP model and Mini-GPT4 for extracting multimodal semantic features to establish the relationship between text and images in mathematical stories and their generation parameters. Subsequently, mathematical text features are designed, including nine mathematical text attributes and ten traditional text readability indicators, followed by collinearity feature selection and statistical testing. Finally, five machine learning grade regressor for mathematical stories were trained, and the correlation between these 19 features and grades is explored using genetic algorithm-based factor mining and the interpretable artificial intelligence method SHapley Additive exPlanations (SHAP). To further understand advanced text features, the latest natural language processing (NLP) readability indicators are also integrated into the analysis. Through multimodal features, traditional text readability metrics, and NLP readability indicators, this method introduces a novel approach for automatically assessing the quality of GAI-generated multimodal mathematical stories, providing a tool for grade predictor and shedding light on the factors (Image-text relevance and textual features) influencing the grade level of analyzed stories, thereby offering new insights for leveraging GAI in mathematical education.