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.

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.

With growing expectations to use AI-based educational technology (AI-EdTech) to improve students’ learning outcomes and enrich teaching practice, teachers play a central role in the adoption of AI-EdTech in classrooms. Teachers’ willingness to accept vulnerability by integrating technology into their everyday teaching practice, that is, their trust in AI-EdTech, will depend on how much they expect it to benefit them versus how many concerns it raises for them. In this study, we surveyed 508 K-12 teachers across six countries on four continents to understand which teacher characteristics shape teachers’ trust in AI-EdTech, and its proposed antecedents, perceived benefits and concerns about AI-EdTech. We examined a comprehensive set of characteristics including demographic and professional characteristics (age, gender, subject, years of experience, etc.), cultural values (Hofstede’s cultural dimensions), geographic locations (Brazil, Israel, Japan, Norway, Sweden, USA), and psychological factors (self-efficacy and understanding). Using multiple regression analysis, we found that teachers with higher AI-EdTech self-efficacy and AI understanding perceive more benefits, fewer concerns, and report more trust in AI-EdTech. We also found geographic and cultural differences in teachers’ trust in AI-EdTech, but no demographic differences emerged based on their age, gender, or level of education. The findings provide a comprehensive, international account of factors associated with teachers’ trust in AI-EdTech. Efforts to raise teachers’ understanding of, and trust in AI-EdTech, while considering their cultural values are encouraged to support its adoption in K-12 education.

This paper discusses the ethical considerations surrounding generative artificial intelligence (GenAI) in chemistry education, aiming to guide teachers toward responsible AI integration. GenAI, driven by advanced AI models like Large Language Models, has shown substantial potential in generating educational content. However, this technology's rapid rise has brought forth ethical concerns regarding general and educational use that require careful attention from educators. The UNESCO framework on GenAI in education provides a comprehensive guide to controversies around generative AI and ethical educational considerations, emphasizing human agency, inclusion, equity, and cultural diversity. Ethical issues include digital poverty, lack of national regulatory adaptation, use of content without consent, unexplainable models used to generate outputs, AI-generated content polluting the internet, lack of understanding of the real world, reducing diversity of opinions, and further marginalizing already marginalized voices and generating deep fakes. The paper delves into these eight controversies, presenting relevant examples from chemistry education to stress the need to evaluate AI-generated content critically. The paper emphasizes the importance of relating these considerations to chemistry teachers' content and pedagogical knowledge and argues that responsible AI usage in education must integrate these insights to prevent the propagation of biases and inaccuracies. The conclusion stresses the necessity for comprehensive teacher training to effectively and ethically employ GenAI in educational practices.

Integrating generative artificial intelligence (GenAI) in pre-service teachers’ education programs offers a transformative opportunity to enhance the pedagogical development of future science educators. This conceptual paper suggests applying the GenAI tool to evaluate pedagogical content knowledge (PCK) among pre-service science teachers. By holding interactive dialogues with GenAI, pre-service teachers engage in lesson planning in a way that reveals their understanding of content, pedagogy, and PCK while facilitating the practical application of theoretical knowledge. Interpretation of these interactions provides insights into teachers-to-be knowledge and skills, enabling personalized learning experiences and targeted program adjustments. The paper underscores the need to equip pre-service teachers with the necessary competencies to utilize GenAI effectively in their future teaching practices. It contributes to the ongoing discourse on technology’s role in teacher preparation programs, highlighting the potential of addressing existing challenges in evaluating and developing teacher knowledge via GenAI. The suggested future research directions aim to further investigate the GenAI usage implications in educational contexts.

Artificial intelligence (AI) holds significant promise as a technology that may improve the quality of educational practices. This includes specialized AI-powered technologies tailored for education and general AI-based technologies, including recently popular generative AI tools that stakeholders are increasingly adapting for teaching and learning. Integrating AI tools into educational settings holds numerous potential pedagogical benefits, such as assisting teachers in planning lessons, promoting personalization, and enhancing student autonomy. However, concerns about bias and discrimination linked to the use of these technologies have rapidly emerged. Today, standardized evaluation criteria to assess the potential contribution of such tools to education and their reliability within the learning and teaching context are lacking. To address this gap, we build on an existing taxonomy for the evaluation of open educational resources (OER) to better suit the unique features of generative AI. The result is a six-dimensional evaluation approach that includes descriptive, pedagogical, representational, communication, scientific content, as well as the ethical and transparency dimension. We then apply this approach to examine the educational potential and ethical concerns around 30 AI tools. The analysis facilitates a critical mapping of the potential and risks of AI-powered technologies in education settings.