The ARTICULATE project is an ambitious and interdisciplinary initiative funded by the CHIST-ERA call 2025. Its vision is to revolutionize science education and democratize scientific knowledge beyond academia and English-speaking audiences through the integration of AI with self-regulated learning. The aim is to translate science not just across language but across language style, to create engaging spoken digital experiences. We present an introduction to this project, an overview of the consortium and research approach, and a number of expected impacts.

Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI’s capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.

Socially Shared Regulation (SSRL) contributes to collaborative learning success. Recent advancements in Artificial Intelligence (AI) and Learning Analytics (LA) have enabled examination of this phenomenon’s temporal and cyclical complexities. However, most of these studies focus on students’ verbalised interactions, not accounting for the intertwined ’silent pauses’ that can index learners’ internal cognitive and emotional processes, potentially offering insight into regulation’s core mental processes. To address this gap, we employed AI-driven LA to explore the deliberation tactics among ten triads of secondary students during a face-to-face collaborative task (2,898 events). Discourse was coded for deliberative interactions for SSRL. With the micro-annotation of ‘silent pause’ added, sequences were analysed with the Optimal Matching algorithm, Ward’s Clustering and Lag Sequential Analysis. Three distinct deliberation tactics with different patterns and characteristics involving silent pauses emerged: i) Elaborated deliberation, ii) Coordinated deliberation, and iii) Solitary deliberation. Our findings highlight the role of ‘silent pauses’ in revealing not only the pattern but also the dynamics and characteristics of each deliberative interaction. This study illustrates the potential of AI-driven LA to tap into granular data points that enrich discourse analysis, presenting theoretical, methodological, and practical contributions and implications.

Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, which constitute the focal point of SSRL. To address this challenge, this article gathers interdisciplinary researchers to establish a multimodal dataset with cognitive and socio-emotional interactions for SSRL study. Firstly, to induce cognitive and socio-emotional interactions, learning science researchers designed a special collaborative learning task with regulatory trigger events among triadic people for the SSRL study. Secondly, this dataset includes various modalities like video, Kinect data, audio, and physiological data (accelerometer, EDA, heart rate) from 81 high school students in 28 groups, offering a comprehensive view of the SSRL process. Thirdly, three-level verbal interaction annotations and nonverbal interactions including facial expression, eye gaze, gesture, and posture are provided, which could further contribute to interdisciplinary fields such as computer science, sociology, and education. In addition, comprehensive analysis verifies the dataset’s effectiveness. As far as we know, this is the first multimodal dataset for studying SSRL among triadic group members.

Recent advances in Learning Analytics (LA) and Artificial Intelligence (AI) have enabled us to gain a better understanding of socially shared regulation (SSRL), which is in collaborative learning. Although recent progress in studying SSRL with LA and AI has provided holistic insights into the temporal and cyclical processes of SSRL, few studies have investigated SSRL processes at a granular level. To address these limitations, we utilise AI techniques to explore the sequences of group-level deliberation as a process and its pattern through cognitive and emotional regulation triggering events in the context of face-to-face collaborative learning. This study involved ten triads of secondary students ($\mathrm{N}=30$) working on a collaborative learning task and receiving regulation-triggering events during their learning. Results from Agglomerative Hierarchical Clustering (AHC) identified two distinct types of deliberation sequences with different approaches to regulation and collaboration practices: 1) the plan and implementation approach (PIA) focused on analysing, discussing, and collaborating; and 2) the trials and failures approach (TFA) focused on random idea testing. Interestingly, we found that most groups maintain the same approach in response to triggering events, emphasizing the importance of supporting learners to recognize and react to the emerging needs of regulation.

Self-regulated learning (SRL) is a critical skill for lifelong learning. However, many learners struggle with SRL and need support. With the recent advancement, learning analytics (LA) has offered capabilities for supporting learning, particularly, for SRL in lifelong learning. In designing SRL analytics, recent calls asked to consider the learners' voices as well as to apply the learning theories. This study aims to explore learners' expectations of personalized support features from SRL analytics based on the learners' challenges and needs in the different phases of SRL. We conducted 10 focus group discussions with 27 students from non-formal online professional development courses. We applied thematic analysis to explore the challenges faced by learners and their expectations of SRL analytics features to support their needs. The findings highlight the importance of features to personalize goal setting, progress tracking, socio-emotional and motivational support, and feedback among other features, in facilitating SRL. The results of the study provide insights into the design of SRL analytics that can effectively support learners.

This article explores the potential of emotional mimicry in identifying the leader and follower students in collaborative learning settings. Our data include video recorded interactions of 24 high school students who worked together in groups of three during a collaborative exam. A facial emotions recognition method was used to capture participants’ facial emotions during the collaborative work. Cross-recurrence quantification analysis was applied on the detected facial emotions to see the level and direction of emotional mimicry among the dyads in the same groups. In order to validate the cross-recurrence quantification analysis results, student interactions in terms of leading or following the task were video coded. Our findings showed that the leaders and followers identified by cross-recurrence quantification analysis findings matched the leaders and followers identified by the video coding in 70 percent of the dyadic interactions across the collaborating groups. The current findings show that video-based facial emotions recognition as a method can add to collaborative learning research, especially explaining some social, and affective dynamics about it. The study further discusses the possible variables that might confound the relationship between emotional mimicry and leader-follower interactions during collaboration.

There is much interest to advance digital technologies supporting teaching, learning and education. Yet, many ideas, e.g., implementing data and artificial intelligence in education, still lack systematic understanding of human learning process. Also, new kind of capabilities are needed that are necessary to succeed in a rapidly changing world. In my talk I introduce recent advancements in research on socially shared regulation in learning which provides a framework for developing these competences. I discuss the role of technology in understanding and supporting socially shared regulation and conclude with future perspective how co-evolution of human capabilities and technologies can be enhanced for digital education.

In this poster, we describe the aim and current activities of the EARLI-Centre for Innovative Research (E-CIR) "Measuring and Supporting Student's Self-Regulated Learning in Adaptive Educational Technologies" which is funded by the European Association for Research on Learning and Instruction (EARLI) from 2015 to 2019. The aim is to develop our understanding of multimodal data that unobtrusively capture cognitive, meta-cognitive, affective and motivational states of learners over time. This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation, and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods including learning analytics to develop more powerful adaptive educational technologies.

Collaborative learning is considered a critical 21st century skill. Much is known about its contribution to learning, but still investigating a process of collaboration remains a challenge. This paper approaches the investigation on collaborative learning from a psychophysiological perspective. An experiment was set up to explore whether biosensors can play a role in analysing collaborative learning. On the one hand, we identified five physiological coupling indices (PCIs) found in the literature: 1) Signal Matching (SM), 2) Instantaneous Derivative Matching (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and the 5) Fisher's z-transform (FZT) of the PCC. On the other hand, three collaborative learning measurements were used: 1) collaborative will (CW), 2) collaborative learning product (CLP) and 3) dual learning gain (DLG). Regression analyses showed that out of the five PCIs, IDM related the most to CW and was the best predictor of the CLP. Meanwhile, DA predicted DLG the best. These results play a role in determining informative collaboration measures for designing a learning analytics, biofeedback dashboard.