A Design Science Research approach is adopted and multiple GenAI-based interventions at a French business school are implemented, indicating increased student awareness and confidence in critically using GenAI and six actionable design principles are derived to support the meaningful integration of GenAI into higher education.
The rapid expansion of generative artificial intelligence (Gen AI) tools, such as ChatGPT, has significantly influenced practices in language teacher education, creating new opportunities for innovation and instructional support. This study explores how pre-service English teachers experience and perceive the use of Gen AI in language education contexts. 14 participants engaged in AI-supported activities, and data were collected through reflective journals, semi-structured focus group interviews, and analysis of instructional materials they developed. Thematic analysis revealed several affordances of Gen AI, including enhanced creativity, increased efficiency, adaptability, and support for professional development. However, participants also reported challenges, such as concerns about reliability, risks of over-reliance, cultural limitations, and ethical considerations. The findings emphasize the importance of maintaining a balance between AI use and pedagogical judgment, highlighting the need for teacher guidance and critical engagement. Overall, the study underscores the potential of Gen AI as a supportive tool in teacher education while reinforcing the necessity of human oversight and reflective practice.
Ramazan Yetkin, Zekiye Özer-Altınkaya· Kuramsal Eğitimbilim· 0 citations
The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including a working understanding of these systems' fundamental concepts, their limitations, and implications for responsible use. However, misconceptions about GenAI, such as perceiving these systems as mere search engines or database lookup systems, are commonly observed among students, while the availability of teaching resources remains fragmented, and learning objectives lack alignment. This Working Group aims to design pedagogical resources for computing science instructors, enabling them to develop students' GenAI literacy. To achieve this, the Working Group will first identify a concise set of GenAI literacy learning objectives informed by instructor experience, research literature, and community input, and subsequently design pedagogical resources aligned with these objectives.
Bruno Pereira Cipriano, O. Petrovska, Nuno Pombo et al.· Annual Conference on Innovat...· 0 citations
The Responsible AI Literacy in Education (RAIL-Ed) framework is introduced, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions.
S. Hossain, S. Ahmadi, Leqi Li et al.· 0 citations
Although generative artificial intelligence (GenAI) has gained popularity to assist in planning lessons in EFL teaching, its potential to enhance teachers’ creativity remains underexplored. Thus, the study aims to investigate the impact of GenAI on EFL teachers’ creativity in lesson design during a six-week professional development program at Nguyen Van Cu High School, Hai Phong, Vietnam. The study was carried out with ten in-service EFL teachers. A mixed-methods design was utilized, including pre- and post-intervention lesson plans, creativity evaluation rubrics, and reflective journals. Quantitative data were analyzed through creativity scores, whereas qualitative data were analyzed thematically. The results suggested that GenAI improved teachers’ originality, multimodal lesson design, and learner-centered instructional practices. However, participants were worried about authenticity and overreliance on AI as well. The findings highlight GenAI as a valuable pedagogical partner for enhancing teacher creativity while underscoring the necessity for reflective and balanced integration of AI into language education.
Thanh Loan Vu, Minh Tri Nguyen· International Journal of AI...· 0 citations
This study aims to analyze recurring design issues in reading education using Generative Artificial Intelligence (GAI) and to structure them into design principles applicable to reading instruction. To this end, 47 core studies were analyzed, and meaning units, initial codes, subcategories, and provisional upper categories were derived. Subsequently, a first-round independent rating and a second-round re-rating were conducted with six experts in reading education, Korean language education, AI literacy, AI ethics, educational technology, and educational evaluation. In the second-round re-rating, all 40 items showed an I-CVI of .83 or higher, and the S-CVI/Ave was .96. As a result, five design principles for GAI-based reading education were derived: learning support and performance efficiency (Efficiency type), creative expansion of reading experience (Augmentation type), critical verification of AI-generated output (Critique type), protection of learner-directed reading (Detachment type), and adaptive learning support (Personalization type). These principles were also explained as being combined through adaptive regulation, a complementary pathway, auxiliary interactions, and tension– balance relationships. Based on these findings, a one-period instructional design example was presented for argumentative text reading in the second year of middle school. This study is significant in that it specified the core of GAI-based reading education not as the introduction of AI functions but as the design of boundaries between AI support and learners’ direct performance.
Young-Jun Seo· The Journal of Future and Co...· 0 citations
Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels.
C. Papaneophytou, Stella A. Nicolaou· Trends in Higher Education· 0 citations