Artificial intelligence (AI) technologies have the potential to enhance learning, teaching, and assessment by providing AI-generated feedback to students. This study used five different AI plug-ins and four different knowledge bases for the optimization of feedback in classroom formative assessments. The sample consisted of 26 students and 13 teachers at five secondary schools in Norway. Interviews and unstructured observations were used to collect data. The findings indicate that AI-generated feedback is appreciated by both students and teachers. It provides immediate subject-specific, concrete, and relevant feedback. High-performing students were motivated to further improve their writing. However, some students found the feedback to be too general or complicated, and the teachers who had not conducted whole-term periodic planning struggled with using AI for formative assessment purposes. Finally, both students and teachers contend that the teacher needs to have the last word when AI takes over formative feedback procedures.

Recent advancements in Generative AI (GenAI) demonstrate strong potential for enhancing student evaluation and assessment practices through complementary and symmetric human–AI collaboration. This study examines the dispositions, perceived benefits, and challenges that teachers experience when they and GenAI complement one another in providing formative assessment and feedback. Partnering with a leading GenAI-powered EdTech platform, we conducted a case study in a Norwegian secondary school using Learny, a GenAI-supported feedback tool. Qualitative data were collected from teachers who employed Learny in their feedback practices. The findings indicate that teachers were generally curious and open to using GenAI, while emphasizing the need to maintain professional responsibility. They valued GenAI’s capacity to save time, offer inspiration, and deliver timely feedback, yet raised concerns about feedback quality, lack of relational and contextual awareness, and the risk of student over-reliance on such feedback. The study contributes to the growing literature on hybrid intelligence in education by identifying conditions under which GenAI can effectively support, rather than replace, human educators. It underscores the centrality of teacher agency in AI-supported feedback and assessment practices and outlines key implications for pedagogy, future GenAI system design, and sustainable implementation.

Artificial Intelligence (AI) translation tools hold strong potential for supporting multilingual classrooms. This mixed-methods study investigates lower secondary school students’ use of and experiences with a real-time AI translation tool embedded in an interactive presentation platform developed by Learnlab in Norway. The tool combines a fine-tuned version of ChatGPT aligned with the Norwegian curriculum and Learnlab AI Translator. We analyze survey data and system logs (N = 148) and four focus-group interviews (N = 19) from five lower secondary schools. The results indicate that the AI translation tool is beneficial for students overall, but particularly for multilingual students. Students used the tool not only to understand difficult words and sentences, but also to explore languages and engage with their peers’ texts across languages. They reported feeling more included in classroom activities suggesting that the tool enabled collaborative meaning-making across languages and perspectives. However, some monolingual students mainly perceived the benefits for others and did not recognize the tool’s potential for their own language learning. There were no statistically significant differences in perceived inclusion or usability across genders or grade levels. The results are discussed in relation to language support, multilingual learning, and inclusion and participation, with the aim of contributing to more equitable learning environments.

Generative artificial intelligence (GenAI) can produce natural language and other content to support a wide range of tasks. While much research examines its performance in educational assessment, less is known about how teachers and GenAI can complement each other in providing formative feedback. To address this gap, we present Learny, a GenAI tool that generates draft feedback for teachers to adapt, and report findings from a case study with 15 teachers. We analyzed how teachers edited GenAI feedback to provide empirical evidence of its use in K-12 formative assessment. Teachers generally found the drafts high quality and a useful starting point, but refined and personalized them, underscoring the value of human-AI synergy rather than substitution. We discuss implications for learning analytics (LA) and hybrid intelligence (HI), highlighting how teacher expertise and GenAI capabilities can be combined to enhance feedback.