Qi Zhou

The dynamic nature of interactions between students and GenAI, as well as their relationship to writing quality, remains underexplored. While most research has examined how general-purpose GenAI can support writing, fewer studies have investigated how students interact with pedagogically designed systems across different phases of the writing process. To address this gap, we evaluated a GenAI-driven essay-writing assistant (EWA) designed to support higher education students in argumentative writing. Drawing on 1,282 interaction logs from 32 undergraduates during a two-hour writing session, Sequential Pattern Mining and K-Means clustering were used to identify behavioral patterns. Two clusters emerged: Cluster 1 emphasized outline planning and essay structure, while Cluster 2 focused on content development. A Mann-Whitney U test revealed a moderate effect size (r = 0.36) in the essay Organization dimension, with Cluster 1 showing higher scores. Qualitative analysis indicated that students with better performance actively wrote and shared essay sections with EWA for feedback, rather than interacted passively by asking questions. These findings suggest implications for teaching and system design. Teachers can encourage active engagement, while future EWAs may integrate automatic labeling and monitoring to prompt students to move from questioning to writing, enabling fuller benefits from GenAI-supported learning.

Generative artificial intelligence (GenAI) is increasingly used in education, posing significant challenges for teachers adapting to these changes. GenAI offers unprecedented opportunities for accessibility, scalability and productivity in educational tasks. However, the automation of teaching tasks through GenAI raises concerns about reduced teacher agency, potential cognitive atrophy, and the broader deprofessionalisation of teaching. Drawing findings from prior literature on AI in Education, and refining through a recent systematic literature review, this chapter presents a conceptualisation of five levels of teacher-AI teaming: transactional, situational, operational, praxical and synergistic teaming. The framework aims to capture the nuanced dynamics of teacher-AI interactions, particularly with GenAI, that may lead to the replacement, complementarity, or augmentation of teachers' competences and professional practice. GenAI technological affordances required in supporting teaming, along with empirical studies, are discussed. Drawing on empirical observations, we outline a future vision that moves beyond individual teacher agency toward collaborative decision-making between teachers and AI, in which both agents engage in negotiation, constructive challenge, and co-reasoning that enhance each other's capabilities and enable outcomes neither could realise independently. Further discussion of socio-technical factors beyond teacher-AI teaming is also included to streamline the synergy of teachers and AI in education ethically and practically.

Background: Many researchers work on the design and development of multimodal collaboration support systems with AI, yet very few of these systems are mature enough to provide actionable feedback to students in real‐world settings. Therefore, a notable gap exists in the literature regarding students' perceptions of such systems and the feedback they generate.Objectives: This study designed, built and implemented a set of collaboration analytics to capture, interpret and provide feedback on students' collaborative processes, including their non‐verbal group interactions as well as group challenges and regulation arising from discourse in authentic collocated collaborative settings.Methods: Seven groups of five to six postgraduate students with varying backgrounds participated in face‐to‐face collaborative design tasks (n = 36) for an 11‐week‐long semester. Multimodal data from audio and video recordings of collaborative learning sessions were analysed using various machine learning techniques to model students' group processes and to generate feedback. A post hoc evaluation of the collaboration analytics feedback was conducted using individual student reflections and focus group interviews.Results and Conclusions: The findings suggest that analytics feedback has the potential to promote students' understanding of their collaborative processes (e.g., awareness of individual, peer and group behaviours and alterations at the individual level). However, the study also identified significant limitations and challenges associated with the real‐world application of collaboration analytics (e.g., limited group transactions stemmed from a lack of group interpretative sessions). The paper concludes with a discussion on future design suggestions and principles (e.g., an integration of analytics with the learning design, value alignments among stakeholders and roles of teachers).

Incorporating feedback into the learning process is crucial for learners’ success. Recent advancements in Learning Analytics (LA) and Artificial Intelligence (AI) have introduced a range of feedback generation and delivery opportunities to improve teaching and learning processes, yet questions remain about how learners engage with such tools and their impact. In this study, we investigated postgraduate students’ engagement with analytics feedback in relation to their level of self-regulated learning (SRL) competence and performance in a semester-long (ten-week) course. We specifically focused on the Interactive-Constructive-Active–Passive (ICAP) framework of cognitive engagement. Initially, students were asked to participate in an established SRL questionnaire, based on Zimmerman’s theory of self-regulation to evaluate their SRL competence (N = 39). Throughout the semester, their online behaviour data from Moodle and Google Docs was collected and analyzed to form personalised analytics feedback for each student. We examined how students with different SRL competencies engage with analytics feedback and the impact of this engagement on students’ course performance. Results indicated that students with high SRL competence actively engage with analytics feedback more than students with low SRL competence. However, students' analytics feedback engagement did not significantly affect their course performance. Additionally, we analyzed students’ reflections on the feedback provided to investigate how they perceived it in relation to their learning experiences and performance. Students argued in their reflections that analytics feedback was beneficial in identifying and regulating their online behaviours and providing motivation through objective insights. They also noted limitations in accurately reflecting their behaviours and learning quality, the need for more personalised recommendations and timely feedback, and suggested design improvements to ensure clarity, foster interaction and incorporate tailored, in-depth insights. We conclude the paper with a discussion on future design and research suggestions for ways of monitoring and supporting students’ cognitive engagement with analytics feedback interventions.