Reading proficiency is predictive of academic success, yet many students, especially those with diverse learning needs, struggle with complex academic texts. Existing support tools often fail to adequately address challenges related to the complexity of text vocabulary and grammar. However, large language models (LLMs) might be able to meet this need. We compared the effectiveness of two prompting strategies for simplifying academic texts (N = 2,000): one that used plain-text instructions and another that incorporated a readability metric. The Metric-Guided Prompt demonstrated a significant reduction in text complexity as measured by the Flesch-Kincaid Grade Level. Following this intrinsic evaluation, we conducted a between-subjects study with 37 students to determine whether there were differences in learner perceptions of the texts and their learning gains, based on the source of the information provided (i.e., the original and simplified texts). The results of both the intrinsic evaluation and the user study indicate that the Metric-Guided Prompt improves text readability without hindering learning. These findings underscore the potential for appropriately prompted LLMs to foster academic success for diverse learners by improving information access and supporting comprehension.

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.

With LLM-based applications now widely accessible, students increasingly leverage them to support their studies, especially in programming education. From completing specific tasks to managing their study routines, students can use LLMs to self-regulate their learning. However, while LLMs have the potential to support students and improve educational outcomes, they could hamper learning. This exploratory case study seeks to understand how students taking programming courses interact with LLM-based applications. We analyzed and clustered the content of student prompts (N = 364) and coded the prompts for self-regulated learning (SRL) strategies. We identified seven distinct clusters of prompts that were characterized by student task (e.g., debugging, seeking help) and prompt topic (e.g., mathematical models, security). Students primarily relied on LLMs for elaboration and help-seeking, while SRL strategies like effort regulation, critical thinking, and organization were used less frequently. Overreliance on LLMs for immediate support may hinder the development of deeper cognitive strategies and impede learning, suggesting a need for student support.

This study addresses the challenges of documenting and demonstrating practical knowledge (PK) in craft subjects. Acquiring PK is a dynamic process involving experience, reflection, practical work, and active engagement with the environment. The aim of this study is to explore the aspects of PK that can be seen in silent video documentation of the Swedish craft subject Sloyd. Twenty students consented to film and submit videos documenting their craft projects. Four categories of PK were identified: know-how, know-what, know-when, and know-why. The findings from the video data analysis provide valuable insights into PK, showcasing, for example, students’ dexterity, material knowledge, planning, creativity, and decision-making. However, observing reflections in the silent videos was challenging. This study increases our understanding of what PK is in the context of craft education and how it becomes visible in action, offering an alternative or complement to the assessment of students’ written reflections and oral presentations.

Dyslexia presents significant challenges in education for students worldwide. While assistive technologies have been used to enhance readability, no study has systematically evaluated the ability of Language Models (LMs) to generate dyslexia-friendly text aligned with established accessibility guidelines. This proof-of-concept study assesses three state-of-the-art LMs on their ability to identify and apply dyslexia-friendly text criteria. Our findings reveal that their knowledge is limited and poses potential risks. To address this, we introduce DysText, a novel metric that quantifies dyslexia-friendly text characteristics based on the British Dyslexia Association’s Dyslexia Style Guide. Results indicate that while LMs can enhance the dyslexia-friendliness of texts, their responses should not be blindly trusted, underscoring the need for further verification.

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.

While student privacy is frequently a topic of concern in studies about data-powered technologies in higher education, we still know little about handling student privacy in online proctoring systems (OPS). To better understand the challenges associated with student privacy in higher educational practice, we conducted an interview study examining various stakeholders’ understandings of privacy in OPS. We interviewed ten stakeholders–including teachers, students, and administration staff, such as the head of the department, the head of the IT department, and examination administrators–directly involved in the procurement and use of an online examination platform with proctoring features, in one of the largest universities in Scandinavia, to investigate stakeholders’ privacy perceptions, privacy concerns, privacy awareness, and perceived responsibility regarding the privacy practices of OPS. The findings show the participants perceive privacy in OPS as a seclusive and anonymous state of being, a way to control information, and an individual right. The results also point to stakeholders’ concerns regarding collecting sensitive information about the students, the possibility of information misuse, and improper access to students’ personal information. Furthermore, the study’s results identify trade-offs between the stakeholders’ concerns for privacy and the benefits of using OPS for examinations in higher education. This study underscores the need for higher education institutions to involve students and educators in procuring and deploying OPS and develop strategies for cultivating privacy awareness and responsible privacy practices. Finally, implications for developing responsible digital practices in higher education are discussed.

Technology-enhanced learning (TEL) is a research field occupied with how teaching and human learning can be supported through the help of digital tools for increased efficiency, effectiveness, learnability, and pedagogical values by applying verified learning theories supported by analyses of the data generated by the students’ activities. Research in TEL is closely related to a social mandate that is becoming eminent in education nowadays: digitalizing education in an accessible, ethical and sustainable way. Most literature on TEL has focused on technological aspects, pedagogical approaches, ethical considerations or accessibility concerns in isolation, often within different research communities. Also, with generative AI's broad and unpredictable impact, these gaps could widen further. This commentary paper aims to bridge these gaps by offering an integrated perspective addressing all three aspects—technology, pedagogy, and accessibility—while examining intersections and implications from multiple viewpoints. From a historical perspective, various educational technologies have facilitated the scaling of different pedagogies and contributed to students' understanding by enhancing personalized learning, expanding visualization possibilities, and improving access to learning materials. While television and radio enabled remote learning, technological advancements in recent decades have significantly increased accessibility, such as radio and TV learning programs, to the emergence of e-learning platforms, adaptive learning systems, and artificial intelligence-driven educational tools. However, it is essential to acknowledge that, despite these advancements, technology-supported educational tools often remain more accessible to learners from developed countries or those with a high socio-economic background who can afford the costs and possess the necessary skills for effective use of these tools.