Background Large language models (LLMs) have shown potential not only as content generators but as evaluators capable of providing nuanced feedback. However, much of the current application of LLMs in education treats them as static graders rather than dynamic participants in formative assessment processes. Purpose This study explores how rubric-guided prompting and role-aware feedback simulations can enable LLMs to approximate human evaluative reasoning across dimensions critical to design-based learning. Scope/Method Using situated learning theory, iterative design pedagogy, and cognitive models of scientific and engineering thinking, the research developed a framework wherein LLMs were trained to align with expert judgment. A stratified sample of student design artifacts was evaluated across different roles (instructor, peer reviewer, grant reviewer) using targeted prompting. Feedback outputs were coded for tone and evaluation focus. Results Rubric engineering was found to substantially improve LLM-human agreement in cognitively complex categories. LLMs demonstrated role-sensitive feedback variation, and final rubric-tuned LLM ratings achieved high consistency with human ratings (Cronbach’s Alpha > 0.75). Figures and tables illustrate how role-specific emphasis and tone were reliably modulated. Conclusions When properly scaffolded, LLMs can serve as dynamic co-evaluators and rubric co-design partners. These findings advance the use of AI from automation to pedagogical emulation, offering scalable, reflective feedback ecosystems for design-rich learning environments.
While generative artificial intelligence (GenAI) is increasingly used in language education, empirical evidence about how educators perceive, adopt, and learn to integrate these tools remains fragmented. This systematic review examines language educators’ adaptation to GenAI technologies through an analysis of 23 empirical studies published in SSCI-indexed journals between December 2022 and September 2024. Using PRSIMA guidelines, we examined educators across pre-service, in-service, and higher education contexts to address four questions: their perceptions of GenAI, factors influencing adoption, competency gaps, and professional development (PD). Across contexts, educators expressed cautious, selective adoption: GenAI was valued for lesson planning, materials creation, and writing support, yet classroom-facing use lagged due to concerns about academic integrity, role redefinition, and technostress. Reported competency gaps spanned episteme (limited understanding of AI capabilities/limits), techne (prompting, AI-enhanced task/assessment design, content evaluation), and phronesis (ethical judgment, bias/privacy, adaptive expertise). Only three studies reported structured PD, but each signaled gains in knowledge, confidence, and identity reframing. Findings underscore the need to pair technical skill-building with practical wisdom to enable effective, ethical GenAI integration in language education.
Managing blended learning environments, which combine traditional face-to-face and online learning, can be challenging for teachers as it requires adapting and orchestrating both components effectively. Learning analytics dashboards (LAD) can provide teachers with insights into students’ self-studying habits in the online component. While recent advances in machine learning (ML) enable the identification of meaningful behavioral patterns, existing LADs mostly focus on providing aggregated information. Reasons for this are manifold, including the potential lack of trust in ML processes and the intricate nature of their visualizations. In this work, we investigate how to make ML findings accessible to teachers using a teacher-centered, mixed-method approach. We first design multiple visualizations and assess their perceived clarity, appeal, and actionability in a user study with 100 teachers. We then implement these visualizations in a dashboard to monitor students’ self-regulated learning behavior and adapt it to two different learning contexts: Reflective Writing and Flipped Classrooms. We evaluate the effectiveness and applicability of our dashboard through semi-structured interviews with 19 teachers. Our findings suggest that the visualization preferences, requirements, uses, and concerns of LADs differ considerably between the two contexts. Our study contributes to understanding teachers’ design preferences in LADs and the integration of ML-derived insights into classrooms.
Equitable access to quality education remains a pressing challenge, particularly for learners with disabilities who are often excluded from mainstream opportunities. This study explores the potential of Generative Artificial Intelligence (GenAI) to advance inclusive education for undergraduate students with visual impairments in Palestine. Adopting a qualitative case study approach, semi-structured interviews were conducted with 21 participants to capture their lived experiences of using GenAI in learning contexts. Thematic analysis revealed that GenAI facilitates personalized adaptation, inclusive access, teacher augmentation, educational parity, and lifelong learning. Key technological attributes—interactivity, user-friendliness, affordability, multimodality, integration, and scalability—were identified as central to accessibility in low-resource contexts. Participants also highlighted GenAI’s role in fostering equity, responsible use, digital fairness, and cross-border collaboration, broadening inclusion to encompass cultural and social participation. The findings extend adaptive learning theories by emphasizing modality flexibility and immediacy; enhance technology acceptance models by showing the interdependence of usability, affordability, and accessibility; and enrich digital equity discourse by framing inclusion as both infrastructural and cultural. The study contributes to Sustainable Development Goal 4 (SDG 4) by offering practical insights for ethical and equitable GenAI integration in education.
The rapid evolution of AI has reshaped many educational practices, raising new questions about its role in supporting students with learning disorders. This work presents a systematic and interdisciplinary review on the use of artificial intelligence (AI) and generative AI (GAI) to support students with dyslexia in educational settings. Dyslexia is the most common disorder among people and, despite the rapid development of AI technologies in recent years, the connections between AI, dyslexia, and education remain underexplored, and comprehensive, up-to-date analyses are lacking. To address this gap, an extensive literature, published between 2018 and 2024, was analyzed, ultimately identifying n = 72 relevant studies, guided by the PRISMA methodology. These studies were classified according to a set of semantic categories carefully defined by the authors, allowing to map current research trends and identify emerging interdisciplinary research directions. This analysis shows that AI has been used to enhance detection, assistive support, and personalized learning for students with dyslexia, yet the evidence remains fragmented and experimental validation is limited. While GAI offers promising opportunities for content generation and interactive support, its presence in the current literature is still minimal. The review highlights existing achievements, identifies key challenges, and outlines future research avenues to advance inclusive, AI-supported learning for individuals with dyslexia.
