Reading proficiency is foundational for children’s future opportunities, yet many struggle to develop adequate literacy skills due to limited instructional resources and increasingly diverse classrooms. While one-to-one tutoring is highly effective, it is difficult to scale. We present AI paired reading for Swedish, co-designed iteratively with teachers and children to complement classroom practice and promote reading engagement. The system combines automatic speech recognition, text-to-speech, and large language models to enable children to read with a virtual peer. A mixed-methods classroom study with 43 second-grade pupils showed high engagement, sustained motivation and voluntary re-use. However, the study also revealed key design tensions around feedback clarity, difficulty adaptation, and the role of AI as a peer versus evaluator. We contribute empirically grounded design insights for AI-supported reading with children and discuss implications for integrating AI into classroom literacy practices.

Learner data have become foundational to the feedback and personalisation functions of modern educational technologies. Decisions to adopt and use data-driven educational technologies, in part, depend on learner privacy perceptions related to data sharing. Some privacy theorists posit that these perceptions are shaped by individual characteristics, while others argue that they are driven by contextual details. In learning analytics (LA), little work has systematically examined whether learner characteristics, such as demographics and privacy concerns, predict student perceptions of LA-driven educational technologies and to what extent contextual factors also play a role. To investigate this, we conducted a vignette-based experiment (N = 256) asking students to evaluate acceptability and intended personal use of data-driven educational technologies across systematically manipulated scenarios. Our analysis showed that demographics and privacy concerns predicted adoption-related perceptions, and that individual and contextual factors explained comparable variance. The study quantifies the contributions of individual and contextual factors to adoption-related perceptions showing them to be similar. These findings imply that LA policies need to embed participatory, context-aware processes to help learners interrogate context and revise their data-sharing and adoption decisions accordingly. In addition, LA tools need to offer customisation of data-sharing choices to address diverse dispositions and context-specific choices.

As AI-mediated learning systems increasingly shape how learners plan, make decisions, and progress through education, learner agency is becoming both more consequential and harder to conceptualize at scale. Existing research often treats agency as a proxy for engagement and self-regulation, leaving unclear who actually holds decision-making authority in large-scale, automated learning environments. This paper reframes learner agency as the allocation of decision authority across learners, educators, institutions, and AI systems. We introduce the Agency Allocation Framework (AAF) for analyzing how decisions are distributed, how choices are architected, what evidence supports them, and over what time horizons their consequences unfold. Drawing on a focused review of Learning at Scale literature and an illustrative tutoring-system example, we identify four recurring challenges for studying learner agency at scale: (1) conceptual ambiguity, (2) reliance on behavioral proxies, (3) trade-offs between efficiency and learner control, and (4) the redistribution of agency through AI-mediated systems. Rather than advocating more or less automation, the AAF supports systematic analysis of when AI scaffolds learners' capacity to act and when it substitutes for it. By making decision authority explicit, the framework provides researchers and designers with analytic tools for studying, comparing, and evaluating agency-preserving learning systems in increasingly automated educational contexts.

Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely on fragmented evidence, as most studies of teachers' perceptions focus on single countries or small samples. This lack of representative cross-national evidence limits both theory building and policy development. At the same time, large language models (LLMs) are increasingly used in research, policy, and teachers' professional workflows, despite limited validation in education. To address these gaps, we conduct a large-scale audit of LLM alignment with teachers' perceptions of AI by combining representative international survey data with systematic model evaluation. Using OECD TALIS data from 55 countries and territories, we measure cross-national variation in teachers' perceived benefits and risks of AI. We then benchmark responses from eight state-of-the-art LLMs across four providers under both general and country-specific prompting, comparing higher- and lower-reasoning models. Results reveal substantial cross-national variation in teacher perceptions that is not reliably reflected in LLM outputs. Models compress country differences, overestimate both benefits and risks, and show limited gains from identity prompting or enhanced reasoning. This misalignment matters because LLM-generated guidance and professional discourse increasingly shape how teachers learn about and discuss AI, potentially influencing trust and future adoption decisions. Our findings caution against treating LLM outputs as substitutes for direct engagement with teachers when informing global AI-in-education initiatives. At the same time, some models (e.g., Gemini 3 Fast) partially capture cross-national ranking patterns, suggesting a complementary role in hypothesis generation and exploratory comparative analysis.

The growing adoption of Large Language Models (LLMs) is transforming learning in higher education. While they provide flexible support, concerns remain about over-reliance, uncritical acceptance of outputs, and excessive cognitive offloading. To address these challenges, we foreground self-regulated learning for LLMs (SRL-for-LLM), defined as learners' ability to plan, monitor, and reflect on AI interactions to support learning. Guided by SRL theory, we introduce ChatWise, a proof-of-concept browser extension that combines conversational scaffolding with learning analytics to classify student prompts by SRL strategies and deliver adaptive metacognitive feedback in real time. A two-phase study included the design of ChatWise and a within-subject field study with STEM undergraduates. Log analyses using mixed-effects modeling showed that access to ChatWise more than doubled the likelihood of producing high-quality prompts. Findings further indicate improved prompt refinement, greater strategic awareness, and more reflective engagement with AI-supported learning. These results highlight the potential of SRL-aligned, analytics-driven scaffolding to support more effective student–LLM interactions.

Reading skills are crucial for students' success in education and beyond. However, reading proficiency among K-12 students has been declining globally, including in Sweden, leaving many underprepared for post-secondary education. Additionally, an increasing number of students have reading disorders, such as dyslexia, which require support. Generative artificial intelligence (genAI) technologies, like ChatGPT, may offer new opportunities to improve reading practices by enhancing the readability of educational texts. This study investigates whether ChatGPT-4 can simplify academic texts and which prompting strategies are most effective. We tasked ChatGPT to re-write 136 academic texts using four prompting approaches: Standard, Meta, Roleplay, and Chain-of-Thought. All four approaches improved text readability, with Meta performing the best overall and the Standard prompt sometimes creating texts that were less readable than the original. This study found variability in the simplified texts, suggesting that different strategies should be used based on the specific needs of individual learners. Overall, the findings highlight the potential of genAI tools, like ChatGPT, to improve the accessibility of academic texts, offering valuable support for students with reading difficulties and promoting more equitable learning opportunities.

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Adaptive tutoring systems have demonstrated significant improvements in math learning, yet their adoption outside of the United States remains limited. The absence of these technologies, along with a lack of research on localizing tutoring systems to different educational contexts, presents a significant barrier for institutions seeking to integrate these tools into their classrooms to support students' math learning. This paper presents a case study on the localization and deployment of OATutor, an adaptive tutoring system developed in the U.S., for use in a math course at KTH Royal Institute of Technology in Sweden. Our study explores using artificial intelligence to automate and validate this process, focusing on translation and syllabus adaptation to ensure the content aligns with the course curriculum and the Swedish educational context. We successfully deployed the system in the course, demonstrating a novel method for translating math content and providing an analysis of syllabus adaptation tailored to the local context. By documenting this process, we contribute to the broader effort to make educational technologies more accessible to diverse learner populations by providing a scalable approach to localization.

AI-powered education technologies can support students and teachers in computer science education. However, with the recent developments in generative AI, and especially the increasingly emerging popularity of ChatGPT, the effectiveness of using large language models for solving programming tasks has been underexplored. The present study examines ChatGPT’s ability to generate code solutions at different difficulty levels for introductory programming courses. We conducted an experiment where ChatGPT was tested on 127 randomly selected programming problems provided by Kattis, an automatic software grading tool for computer science programs, often used in higher education. The results showed that ChatGPT independently could solve 19 out of 127 programming tasks generated and assessed by Kattis. Further, ChatGPT was found to be able to generate accurate code solutions for simple problems but encountered difficulties with more complex programming tasks. The results contribute to the ongoing debate on the utility of AI-powered tools in programming education.

Abstract Online learning has become an everyday form of learning for many students across different disciplines, including STEM subjects in the setting of higher education. Studying in these settings requires students to self-regulate their learning to a higher degree as compared to campus-based education. A vital aspect of self-regulated learning is the application of goal-setting strategies. Universities act to support students’ goal-setting through the achievement of course learning outcomes, which work both as a promise and metric of academic achievement. However, a lack of clear integration between course activities and course learning outcomes leaves a dissonance between students’ study efforts and the course progress. This demo study presents a student-centered learning analytics dashboard aimed at assisting students in their achievement of course learning goals in the setting of STEM higher education. The dashboard was designed using a design science methodological approach. Thirty-seven students have contributed to its development and evaluation during different stages of the design process, including the conceptual iterative design and prototyping. The preliminary results show that students found the tool to be easy to use and useful for the achievement of the course goals.

Abstract Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students’ engagement with massive courses. Although the peer feedback process increases students’ self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.

Abstract The adoption of Extended Reality (XR) technologies for supporting learning processes is an increasingly popular research topic for a wide variety of domains, including medical education. Currently, within this community, the metrics applied to quantify the potential impact these technologies have on procedural knowledge acquisition are inconsistent. This paper proposes a practical definition of standard metrics for the learning goals in the application of XR to surgical training. Their value in the context of previous research in neurosurgical training is also discussed. Objective metrics of performance include: spatial accuracy and precision, time-to-task completion, number of attempts. The objective definition of what the learner’s aims are enables the creation of comparable XR systems that track progress during training. The first impact is to provide a community-wide metric of progress that allows for consistent measurements. Furthermore, a measurable target opens the possibility for automated performance assessments with constructive feedback.

Student engagement has been identified as a critical construct for understanding and predicting educational success. However, research has shown that it can be hard to align data-driven insights of engagement with observed and self-reported levels of engagement. Given the emergence and increasing application of learning analytics (LA) within K-12 education, further research is needed to understand how engagement is being conceptualized and measured within LA research. This scoping review identifies and synthesizes literature published between 2011-2022, focused on LA and student engagement in K-12 contexts, and indexed in five international databases. 27 articles and conference papers from 13 different countries were included for review. We found that most of the research was undertaken in middle school years within STEM subjects. The results show that there is a wide discrepancy in researchers’ understanding and operationalization of engagement and little evidence to suggest that LA improves learning outcomes and support. However, the potential to do so remains strong. Guidance is provided for future LA engagement research to better align with these goals.

The protection of students’ privacy in learning analytics (LA) applications is critical for cultivating trust and effective implementations of LA in educational environments around the world. However, students’ privacy concerns and how they may vary along demographic dimensions that historically influence these concerns have yet to be studied in higher education. Gender differences, in particular, are known to be associated with people's information privacy concerns, including in educational settings. Building on an empirically validated model and survey instrument for student privacy concerns, their antecedents and their behavioral outcomes, we investigate the presence of gender differences in students’ privacy concerns about LA. We conducted a survey study of students in higher education across five countries (N = 762): Germany, South Korea, Spain, Sweden and the United States. Using multiple regression analysis, across all five countries, we find that female students have stronger trusting beliefs and they are more inclined to engage in self-disclosure behaviors compared to male students. However, at the country level, these gender differences are significant only in the German sample, for Bachelor's degree students, and for students between the ages of 18 and 24. Thus, national context, degree program, and age are important moderating factors for gender differences in student privacy concerns.

The potential of learning analytics (LA) to improve learning and teaching is high. Yet, the adoption of LA across countries still remains low. One reason behind this is that the LA services often do not adequately meet the expectations and needs of their key stakeholders, namely students and teachers. Presently, there is limited research focusing on the examination of the students’ expectations of LA across countries, especially in the Nordic, largely highly digitalized context. To fill this gap, this study examines Swedish students’ attitudes of LA in a higher education institution. To do so, the validated survey instrument, Student Expectations of Learning Analytics Questionnaire (SELAQ) has been used. Through the application of SELAQ, the students’ ideal and predicted expectations of the LA service and their expectations regarding privacy and ethics were examined. Data were collected in spring 2021. 132 students participated in the study. The results show that the students have higher ideal expectations of LA compared to the predicted ones, especially in regards to privacy and ethics. Also, the findings illustrate that the respondents have low expectations in areas related to the instructor feedback, based on the analytics results. Further, the results demonstrate that the students have high expectations on the part of the university in matters concerning privacy and ethics. In sum, the results from the study can be used as a basis for implementing LA in the selected context.

Migrant professional language learners need to be supported in acquiring a second language effectively beyond the classroom. These learners frequently lack opportunities to participate in language classes due to full-time jobs. Yet, quick acquisition of the target language is a needed prerequisite for their successful integration into job settings and the host society. To assist these learners in their smooth acquisition of the target language, this study takes advantage of the recent developments in artificial intelligence and presents the design of a chatbot, called SKANDIBOT aimed at fostering second language learners’ conversational practice in professional work settings of Sweden and Denmark. The results of this work-in-progress study indicate that both healthcare and learning professionals perceive the overall interaction design of SKANDIBOT and the information offered in it to be useful for their second language acquisition.

Self-regulated learning (SRL) can predict academic performance. Yet, it is difficult for learners. The ability to self-regulate learning becomes even more important in emerging online learning settings. To support learners in developing their SRL, learning analytics (LA), which can improve learning practice by transforming the ways we support learning, is critical. This scoping review is based on the analysis of 54 papers on LA empirical research for SRL in online learning contexts published between 2011 and 2019. The research question is: What is the current state of the applications of learning analytics to measure and support students' SRL in online learning environments? The focus is on SRL phases, methods, forms of SRL support, evidence for LA and types of online learning settings. Zimmerman's model (2002) was used to examine SRL phases. The evidence about LA was examined in relation to four propositions: whether LA i) improve learning outcomes, ii) improve learning support and teaching, iii) are deployed widely, and iv) used ethically. Results showed most studies focused on SRL parts from the forethought and performance phase but much less focus on reflection. We found little evidence for LA that showed i) improvements in learning outcomes (20%), ii) improvements in learning support and teaching (22%). LA was also found iii) not used widely and iv) few studies (15%) approached research ethically. Overall, the findings show LA research was conducted mainly to measure rather than to support SRL. Thus, there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.

This is a reflection paper that discusses the notion of knowledge management in massive online open courses (MOOCs). We explain MOOCs’ structure in terms of representations of participants’ minds (both designers and learners), where knowing is understood as a process and a result of sociotechnical construction, rather than purely social construction mediated by users and learning tools.By applying Walsham’s human-centered view of knowledge (2001) we problematise the nature of MOOCs in relation to individuals’ knowledge management. Such a view emphasises issues of representations in relation to humans’ know ledge construction. This paper is organised as follows: firstly, pedagogical assumptions of MOOCs are discussed; secondly, the notion of sense making in a MOOC context is focused; thirdly, social learning analytics (SLA) is suggested as a key institutional asset to approach individuals’ knowledge management. Our analysis suggests that the distributed and fragmented nature of MOOCs sets the scene for a number of challenges in regard to assessment, knowledge management and pedagogy in MOOCs. Due to the diverse social contexts and learners’ cultural backgrounds, we believe that it is a rather problematic enterprise for MOOCs’ designers and learners to attempt to find a unified pedagogical model. Consequently MOOCs are understood as a part of embryonic and emerging open, social learning, which focuses learner activity in a social setting. Finally we conclude by arguing that the sense making in MOOCs is likely to take place in a liminal space, between individuals’ sense giving and sense reading processes.