Linguistic alignment, the tendency of speakers to share common linguistic features during conversations, has emerged as a key area of research in computer-supported collaborative learning. While previous studies have shown that linguistic alignment can have a significant impact on collaborative outcomes, there is limited research exploring its role in K-12 learning contexts. This study investigates syntactic and lexical linguistic alignment in a collaborative computer science learning corpus from 24 pairs (48 individuals) of middle school students (aged 11-13). The results show stronger effects of self-alignment than partner-alignment on both syntactic and lexical levels, with students often diverging from their partners on task-relevant words. Furthermore, student self-alignment on the syntactic level is negatively correlated with partner satisfaction ratings, while self-alignment on lexical level is positively correlated with their partner's satisfaction.

Collaborative learning offers numerous benefits to learners, largely due to the dialogue that is unfolding between them. However, there is still much to learn about the structure of collaborative dialogue, and especially little is known about co-creative dialogues during learning. This paper reports on a study with learners engaged in co-creative tasks where the learners wrote code to create a song and while engaging in textual dialogue as they did so. After gathering the textual dialogue and the actions within the interface, we learned a hidden Markov model (HMM) to reveal co-creative states. The seven-state model revealed four states primarily composed of coding actions that included browsing the curriculum documents, working in the code editor, compiling the code successfully, and receiving a compile error. The remaining three states are primarily composed of dialogue that can be characterized as social, aesthetic, and technical dialogue. Next, we analyzed the relationships between the co-creative states revealed by the HMM and students’ partner satisfaction scores from a post-survey. The results reveal the relative frequency of actions in certain states and some transitions between states were predictive of partner satisfaction. For example, partner satisfaction was negatively associated with the Compilation Error state and with the relative frequency of transitions from the Curriculum Browsing state to the Code Editing state. Partner satisfaction was also negatively associated with the relative frequency of transitions from the Aesthetic Dialogue state to the Technical Dialogue state and the Code Editing state. This line of investigation reveals how co-creative processes are associated with partner satisfaction, and holds the potential to inform scaffolding for collaborative learning.

Intelligent learning environments have demonstrated effectiveness for providing individualized instruction to students of computer science (CS). However, the great potential of intelligent agents has not yet been explored within expressive environments, which are increasingly common for supporting and motivating K-12 students. This paper presents the prototype design and implementation of a novel Co-creative Artificial Intelligence (CAI) integrated within EarSketch, an online environment for learning introductory computing concepts through code-driven, sample-based music remix-ing. CAI is intended to scaffold student learning from EarSketch’s expressive computing curriculum by co-creating algorithmic music alongside a human learner. This paper presents an initial version of CAI, which engages with EarSketch users by offering menu-based dialogue and suggestions based on the state of a project. We report a pilot study in classrooms, showing promising results in students’ satisfaction with the system’s capabilities. The findings of this pilot study suggest the ability of a co-creative agent to support users in learning and creative objectives, and should inspire research into combined computational and creative user models.

Many important forms of collaborative learning are co-creative in nature. AI systems to support co-creativity in learning are highly underinvestigated, and very little is known about the dialogue mechanisms that support learning during collaborative co-creativity. To address this need, we analyzed the structure of collaborative dialogue between pairs of high school students who co-created music by writing code. We used hidden Markov models to analyze 68 co-creative dialogues consisting of 3,305 total utterances. The results distinguish seven hidden states: three of the hidden states are characterized by conversation, such as social, aesthetic, or technical dialogue. The remaining four hidden states are characterized by task actions including code editing, accessing the curriculum, running the code successfully, and receiving an error when running the code. The model reveals that immediately after the pairs ran their code successfully, they often transitioned into the aesthetic or technical dialogue state. However, when facing code errors, learners were unlikely to transition into a conversation state. In the few cases where they did transition to a conversation state, this transition was almost always to the technical dialogue state. These findings reveal processes of human co-creativity and can inform the design of intelligent co-creative agents that support human collaboration and learning.