Advancements in digital technology are profoundly transforming education, especially through gamified learning platforms offering unprecedented interactivity and engagement. The Metaverse further enriches these virtual environments but increases demands on learners’ self-regulation. By integrating immersive environments and interactive experiences, these platforms address learners’ self-regulation needs, especially where technology and self-regulation theory intersect. This study uses the Technology Acceptance Model (TAM) and the Perceived Enjoyment Motivation Model to explore Generation Z’s attitudes toward Metaverse-based gamified learning platforms and their psychological acceptance. An empirical analysis in China employed structural equation modeling and confirmatory factor analysis on 690 survey responses. Results indicate that Metaverse features—social interaction, immersion, virtual avatars—and self-regulation abilities significantly influence users’ intentions to use gamified learning platforms. This suggests Generation Z’s learning methods in digital environments are evolving, with Metaverse-based platforms fostering student-centered learning and innovating learning experiences.

Kyungjin Park, Hyunwoo Sohn, Wookhee Min, Bradford Mott, Krista Glazewski, Cindy E. Hmelo-Silver, James Lester. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.

Artificial intelligence has emerged as a technology that is profoundly reshaping society and enabling rapid improvements in science, engineering, and mathematics, as well as information technology itself. This has generated increased demand for fostering an AI-literate populace as well as a growing recognition of the importance of promoting K-12 students’ awareness and interest in AI. Although efforts are be-ginning to incorporate AI learning within K-12 education, there is little research exploring how to introduce students to AI and how to support teachers to integrate AI learning experiences in their classrooms. This is especially true at the elementary school level. A particularly promising approach for providing effective and engaging AI learning experiences for elementary students is game-based learning. In this paper, we explore how to introduce AI-infused collaborative inquiry learning into upper elementary school (student ages 8 to 11) using game-based learning. To ground the work in the realities of elementary school classrooms, we present insights from interviews with elementary school teachers to under-stand how best to support them in integrating AI into their classrooms. We then present the design of PrimaryAI, a game-based learning environment that supports rich problem-based learning activities within upper elementary classrooms centered on AI applied toward solving life-science problems. Finally, we discuss some of the challenges we face in bringing AI-infused collaborative inquiry learning to upper elementary students.

Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students’ in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students’ learning outcomes and varies across gender and students’ prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework offers significant potential for supporting effective collaborative game-based learning through the identification of disruptive talk.

Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this paper, we propose a visual interface to enable upper elementary students (grades 3–5, ages 8–11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a qualitative study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks.

Modeling patient disease progression using Electronic Health Records (EHRs) is crucial to assist clinical decision making. In recent years, deep learning models such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) have shown great success in handling sequential multivariate data, such as EHRs. Despite their great success, it is often difficult to interpret and visualize patient disease progression learned from these models in a meaningful yet unified way. In this work, we present MuLan: a Multilevel Language-based representation learning framework that can automatically learn a hierarchical representation for EHRs at entry, event, and visit levels. We validate MuLan on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results showed that these unified multilevel representations can be utilized not only for interpreting and visualizing the latent mechanism of patients' septic shock progressions but also for early detection of septic shock.

Recent years have seen increasing awareness of the need for all students in primary and secondary education to learn computer science (CS) concepts and skills. Educational games hold significant potential to serve as a platform for CS education because they integrate engaging problem solving with effective pedagogical strategies. This potential is especially high for narrative-centered educational games that embed learning activities within rich interactive stories. In this paper, we present an educational game featuring block-based programming challenges contextualized within an engaging narrative, designed to promote CS learning for middle school students (ages 11 to 13). In the game, students undertake problem-solving challenges that are aligned with the K-12 Computer Science Framework. Results from a classroom implementation of the game with middle grade students suggest that their perceived game control ratings are positively correlated with their progress in the game, which suggests the need for adaptively supporting students' game-based learning activities. Building on these findings, we discuss design implications for creating student-adaptive CS learning experiences in educational games that incorporate block-based programming enriched narrative-centered gameplay.

Educational games offer significant potential for supporting personalized learning in engaging virtual worlds. However, many educational games do not provide adaptive gameplay to meet the needs of individual students. To address this issue, educational games should include game levels that can self-adjust to the specific needs of individual students. However, creating a large number of adaptable game levels requires considerable effort by game developers. A promising solution to this problem is to leverage procedural content generation to automatically generate levels for educational games that incorporate the desired learning objectives. In this paper, we propose a multistep deep convolutional generative adversarial network for generating new levels within a game for middle school computer science education. The model operates in two phases: (1) train a generator with a small set of human-authored example levels and generate a much larger set of synthetic levels to augment the training data for a second generator, and (2) train a second generator using the augmented training data and use it to generate novel educational game levels with enhanced solvability. We evaluate the performance of the model by comparing the novelty and solvability of generated levels between the two generators. Results suggest that the proposed multistep model significantly enhances the solvability of the generated levels with only minor degradation in the novelty of the generated levels.